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Bibliography on: Brain-Computer Interface

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ESP: PubMed Auto Bibliography 08 Dec 2022 at 12:46 Created: 

Brain-Computer Interface

Wikipedia: A brain–computer interface (BCI), sometimes called a neural control interface (NCI), mind–machine interface (MMI), direct neural interface (DNI), or brain–machine interface (BMI), is a direct communication pathway between an enhanced or wired brain and an external device. BCIs are often directed at researching, mapping, assisting, augmenting, or repairing human cognitive or sensory-motor functions. Research on BCIs began in the 1970s at the University of California, Los Angeles (UCLA) under a grant from the National Science Foundation, followed by a contract from DARPA. The papers published after this research also mark the first appearance of the expression brain–computer interface in scientific literature. BCI-effected sensory input: Due to the cortical plasticity of the brain, signals from implanted prostheses can, after adaptation, be handled by the brain like natural sensor or effector channels. Following years of animal experimentation, the first neuroprosthetic devices implanted in humans appeared in the mid-1990s. BCI-effected motor output: When artificial intelligence is used to decode neural activity, then send that decoded information to some kind of effector device, BCIs have the potential to restore communication to people who have lost the ability to move or speak. To date, the focus has largely been on motor skills such as reaching or grasping. However, in May of 2021 a study showed that an AI/BCI system could be use to translate thoughts about handwriting into the output of legible characters at a usable rate (90 characters per minute with 94% accuracy).

Created with PubMed® Query: (bci OR (brain-computer OR brain-machine OR mind-machine OR neural-control interface) NOT 26799652[PMID] ) NOT pmcbook NOT ispreviousversion

Citations The Papers (from PubMed®)

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RevDate: 2022-12-08

Cao W, Li JH, Lin S, et al (2022)

NMDA receptor hypofunction underlies deficits in parvalbumin interneurons and social behavior in neuroligin 3 R451C knockin mice.

Cell reports, 41(10):111771.

Neuroligins (NLs), a family of postsynaptic cell-adhesion molecules, have been associated with autism spectrum disorder. We have reported that dysfunction of the medial prefrontal cortex (mPFC) leads to social deficits in an NL3 R451C knockin (KI) mouse model of autism. However, the underlying molecular mechanism remains unclear. Here, we find that N-methyl-D-aspartate receptor (NMDAR) function and parvalbumin-positive (PV+) interneuron number and expression are reduced in the mPFC of the KI mice. Selective knockdown of NMDAR subunit GluN1 in the mPFC PV+ interneuron decreases its intrinsic excitability. Restoring NMDAR function by its partial agonist D-cycloserine rescues the PV+ interneuron dysfunction and social deficits in the KI mice. Interestingly, early D-cycloserine administration at adolescence prevents adult KI mice from social deficits. Together, our results suggest that NMDAR hypofunction and the resultant PV+ interneuron dysfunction in the mPFC may constitute a central node in the pathogenesis of social deficits in the KI mice.

RevDate: 2022-12-08

Asahina T, Shimba K, Kotani K, et al (2022)

Improving the accuracy of decoding monkey brain-machine interface data by estimating the state of unobserved cell assemblies.

Journal of neuroscience methods pii:S0165-0270(22)00290-4 [Epub ahead of print].

BACKGROUND: The brain-machine interface is a technology that has been used for improving the quality of life of individuals with physical disabilities and also healthy individuals. It is important to improve the methods used for decoding the brain-machine interface data as the accuracy and speed of movements achieved using the existing technology are not comparable to the normal body.

Decoding of brain-machine interface data using the proposed method resulted in improved decoding accuracy compared to the existing method.

CONCLUSIONS: The results demonstrated the usefulness of cell assembly state estimation method for decoding the brain-machine interface data.

NEW METHOD: We incorporated a novel method of estimating cell assembly states using spike trains with the existing decoding method that used only firing rate data. Synaptic connectivity pattern was used as feature values in addition to firing rate. Publicly available monkey brain-machine interface datasets were used in the study.

RESULTS: As long as the decoding was successful, the root mean square error of the proposed method was significantly smaller than the existing method. Artificial neural netowork-based decoding method resulted in more stable decoding, and also improved the decoding accuracy due to incorporation of synaptic connectivity pattern.

RevDate: 2022-12-07
CmpDate: 2022-12-07

Bex A, B Mathon (2022)

Advances, technological innovations, and future prospects in stereotactic brain biopsies.

Neurosurgical review, 46(1):5.

Stereotactic brain biopsy is one of the most frequently performed brain surgeries. This review aimed to expose the latest cutting-edge and updated technologies and innovations available to neurosurgeons to safely perform stereotactic brain biopsy by minimizing the risks of complications and ensuring that the procedure is successful, leading to a histological diagnosis. We also examined methods for improving preoperative, intraoperative, and postoperative workflows. We performed a comprehensive state-of-the-art literature review. Intraoperative histology, fluorescence, and imaging techniques appear as smart tools to improve the diagnostic yield of biopsy. Constant innovations such as optical methods and augmented reality are also being made to increase patient safety. Robotics and integrated imaging techniques provide an enhanced intraoperative workflow. Patients' management algorithms based on early discharge after biopsy optimize the patient's personal experience and make the most efficient possible use of the available hospital resources. Many new trends are emerging, constantly improving patient care and safety, as well as surgical workflow. A parameter that must be considered is the cost-effectiveness of these devices and the possibility of using them on a daily basis. The decision to implement a new instrument in the surgical workflow should also be dependent on the number of procedures per year, the existing stereotactic equipment, and the experience of each center. Research on patients' postbiopsy management is another mandatory approach to enhance the safety profile of stereotactic brain biopsy and patient satisfaction, as well as to reduce healthcare costs.

RevDate: 2022-12-08

Chen W, Wu J, Wei R, et al (2022)

Improving the diagnosis of acute ischemic stroke on non-contrast CT using deep learning: a multicenter study.

Insights into imaging, 13(1):184.

OBJECTIVE: This study aimed to develop a deep learning (DL) model to improve the diagnostic performance of EIC and ASPECTS in acute ischemic stroke (AIS).

METHODS: Acute ischemic stroke patients were retrospectively enrolled from 5 hospitals. We proposed a deep learning model to simultaneously segment the infarct and estimate ASPECTS automatically using baseline CT. The model performance of segmentation and ASPECTS scoring was evaluated using dice similarity coefficient (DSC) and ROC, respectively. Four raters participated in the multi-reader and multicenter (MRMC) experiment to fulfill the region-based ASPECTS reading under the assistance of the model or not. At last, sensitivity, specificity, interpretation time and interrater agreement were used to evaluate the raters' reading performance.

RESULTS: In total, 1391 patients were enrolled for model development and 85 patients for external validation with onset to CT scanning time of 176.4 ± 93.6 min and NIHSS of 5 (IQR 2-10). The model achieved a DSC of 0.600 and 0.762 and an AUC of 0.876 (CI 0.846-0.907) and 0.729 (CI 0.679-0.779), in the internal and external validation set, respectively. The assistance of the DL model improved the raters' average sensitivities and specificities from 0.254 (CI 0.22-0.26) and 0.896 (CI 0.884-0.907), to 0.333 (CI 0.301-0.345) and 0.915 (CI 0.904-0.926), respectively. The average interpretation time of the raters was reduced from 219.0 to 175.7 s (p = 0.035). Meanwhile, the interrater agreement increased from 0.741 to 0.980.

CONCLUSIONS: With the assistance of our proposed DL model, radiologists got better performance in the detection of AIS lesions on NCCT.

RevDate: 2022-12-05

Wang H, Xia H, Xu Z, et al (2022)

Effect of surface structure on the antithrombogenicity performance of poly(-caprolactone)-cellulose acetate small-diameter tubular scaffolds.

International journal of biological macromolecules pii:S0141-8130(22)02880-X [Epub ahead of print].

Small-diameter artificial blood vessels have always faced the problem of thrombosis. In this research, three types of poly(-caprolactone)-cellulose acetate (PCL-CA) composite nanofiber membranes were prepared by various collectors to make into a tubular scaffold with a 4.5-mm diameter. The collector consisted of two sizes of stainless steel wire mesh large-mesh (LM) and small-mesh (SM), respectively. There is also a random flat (RF) that acts as the third type collector. The nanofiber membrane's surface structure mimicked the collectors' surface morphology, they named LM, SM and RF scaffolds. The water contact angles of RF and LM scaffolds are 126.5° and 105.5°, and the distinct square-groove construction greatly improves the contact angle of LM. The tubular scaffolds' radial mechanical property test demonstrated that the large-mesh (LM) tubular scaffold enhanced the strain and tensile strength; the tensile strength and strain are 30 % and 148 % higher than that of the random-flat (RF) tubular scaffold, respectively. The suture retention strength value of the LM tubular scaffold was 103 % higher than that of the RF tubular scaffold. The cytotoxicity and antithrombogenicity performance were also evaluated, the LM tubular scaffold has 88 % cell viability, and the 5-min blood coagulation index (BCI) value was 89 %, which is much higher than other tubular scaffolds. The findings indicate that changing the tubular scaffold's surface morphology cannot only enhance the mechanical and hydrophilic properties but also increase cell survival and antithrombogenicity performance. Thus, the development of a small-diameter artificial blood vessel will be a big step toward solving the problem on thrombosis. Furthermore, artificial blood vessel is expected to be a candidate material for biomedical applications.

RevDate: 2022-12-06

Carino-Escobar RI, Rodríguez-García ME, Ramirez-Nava AG, et al (2022)

A case report: Upper limb recovery from stroke related to SARS-CoV-2 infection during an intervention with a brain-computer interface.

Frontiers in neurology, 13:1010328.

COVID-19 may increase the risk of acute ischemic stroke that can cause a loss of upper limb function, even in patients with low risk factors. However, only individual cases have been reported assessing different degrees of hospitalization outcomes. Therefore, outpatient recovery profiles during rehabilitation interventions are needed to better understand neuroplasticity mechanisms required for upper limb motor recovery. Here, we report the progression of physiological and clinical outcomes during upper limb rehabilitation of a 41-year-old patient, without any stroke risk factors, which presented a stroke on the same day as being diagnosed with COVID-19. The patient, who presented hemiparesis with incomplete motor recovery after conventional treatment, participated in a clinical trial consisting of an experimental brain-computer interface (BCI) therapy focused on upper limb rehabilitation during the chronic stage of stroke. Clinical and physiological features were measured throughout the intervention, including the Fugl-Meyer Assessment for the Upper Extremity (FMA-UE), Action Research Arm Test (ARAT), the Modified Ashworth Scale (MAS), corticospinal excitability using transcranial magnetic stimulation, cortical activity with electroencephalography, and upper limb strength. After the intervention, the patient gained 8 points and 24 points of FMA-UE and ARAT, respectively, along with a reduction of one point of MAS. In addition, grip and pinch strength doubled. Corticospinal excitability of the affected hemisphere increased while it decreased in the unaffected hemisphere. Moreover, cortical activity became more pronounced in the affected hemisphere during movement intention of the paralyzed hand. Recovery was higher compared to that reported in other BCI interventions in stroke and was due to a reengagement of the primary motor cortex of the affected hemisphere during hand motor control. This suggests that patients with stroke related to COVID-19 may benefit from a BCI intervention and highlights the possibility of a significant recovery in these patients, even in the chronic stage of stroke.

RevDate: 2022-12-06

Mussi MG, KD Adams (2022)

EEG hybrid brain-computer interfaces: A scoping review applying an existing hybrid-BCI taxonomy and considerations for pediatric applications.

Frontiers in human neuroscience, 16:1007136.

Most hybrid brain-computer interfaces (hBCI) aim at improving the performance of single-input BCI. Many combinations are possible to configure an hBCI, such as using multiple brain input signals, different stimuli or more than one input system. Multiple studies have been done since 2010 where such interfaces have been tested and analyzed. Results and conclusions are promising but little has been discussed as to what is the best approach for the pediatric population, should they use hBCI as an assistive technology. Children might face greater challenges when using BCI and might benefit from less complex interfaces. Hence, in this scoping review we included 42 papers that developed hBCI systems for the purpose of control of assistive devices or communication software, and we analyzed them through the lenses of potential use in clinical settings and for children. We extracted taxonomic categories proposed in previous studies to describe the types of interfaces that have been developed. We also proposed interface characteristics that could be observed in different hBCI, such as type of target, number of targets and number of steps before selection. Then, we discussed how each of the extracted characteristics could influence the overall complexity of the system and what might be the best options for applications for children. Effectiveness and efficiency were also collected and included in the analysis. We concluded that the least complex hBCI interfaces might involve having a brain inputs and an external input, with a sequential role of operation, and visual stimuli. Those interfaces might also use a minimal number of targets of the strobic type, with one or two steps before the final selection. We hope this review can be used as a guideline for future hBCI developments and as an incentive to the design of interfaces that can also serve children who have motor impairments.

RevDate: 2022-12-06
CmpDate: 2022-12-06

Senathirajah Y, AE Solomonides (2022)

Best Papers in Human Factors and Sociotechnical Development.

Yearbook of medical informatics, 31(1):221-225.

OBJECTIVES: To select the best papers that made original and high impact contributions in human factors and organizational issues in biomedical informatics in 2021.

METHODS: A rigorous extraction process based on queries from Web of Science® and PubMed/Medline was conducted to identify the scientific contributions published in 2021 that address human factors and organizational issues in biomedical informatics. The screening of papers on titles and abstracts independently by the two section editors led to a total of 3,206 papers. These papers were discussed for a selection of 12 finalist papers, which were then reviewed by the two section editors, two chief editors, and by three external reviewers from internationally renowned research teams.

RESULTS: The query process resulted in 12 papers that reveal interesting and rigorous methods and important studies in human factors that move the field forward, particularly in clinical informatics and emerging technologies such as brain-computer interfaces and mobile health. This year three papers were clearly outstanding and help advance in the field. They provide examples of examining novel and important topics such as the nature of human-machine interaction behavior and norms, use of social-media based design for an electronic health record, and emerging topics such as brain-computer interfaces. thematic development of electronic health records and usability techniques, and condition-focused patient facing tools. Those concerning the Corona Virus Disease 2019 (COVID-19) were included as part of that section.

CONCLUSION: The selected papers make important contributions to human factors and organizational issues, expanding and deepening our knowledge of how to apply theory and applications of new technologies in health.

RevDate: 2022-12-06
CmpDate: 2022-12-06

Yan JJ, Ding XJ, He T, et al (2022)

A circuit from the ventral subiculum to anterior hypothalamic nucleus GABAergic neurons essential for anxiety-like behavioral avoidance.

Nature communications, 13(1):7464.

Behavioral observations suggest a connection between anxiety and predator defense, but the underlying neural mechanisms remain unclear. Here we examine the role of the anterior hypothalamic nucleus (AHN), a node in the predator defense network, in anxiety-like behaviors. By in vivo recordings in male mice, we find that activity of AHN GABAergic (AHN[Vgat+]) neurons shows individually stable increases when animals approach unfamiliar objects in an open field (OF) or when they explore the open-arm of an elevated plus-maze (EPM). Moreover, object-evoked AHN activity overlap with predator cue responses and correlate with the object and open-arm avoidance. Crucially, exploration-triggered optogenetic inhibition of AHN[Vgat+] neurons reduces object and open-arm avoidance. Furthermore, retrograde viral tracing identifies the ventral subiculum (vSub) of the hippocampal formation as a significant input to AHN[Vgat+] neurons in driving avoidance behaviors in anxiogenic situations. Thus, convergent activation of AHN[Vgat+] neurons serves as a shared mechanism between anxiety and predator defense to promote behavioral avoidance.

RevDate: 2022-12-02

Pan H, Fu Y, Li Z, et al (2022)

Images Reconstruction from functional magnetic resonance imaging Patterns Based on the Improved Deep Generative Multiview Model.

Neuroscience pii:S0306-4522(22)00577-2 [Epub ahead of print].

Reconstructing visual stimulus images from the brain activity signals is an important research task in the field of brain decoding. Many methods of reconstructing visual stimulus images mainly focus on how to use deep learning to classify the brain activities measured by functional magnetic resonance imaging or identify visual stimulus images. Accurate reconstruction of visual stimulus images by using deep learning still remains challenging. This paper proposes an improved deep generative multiview model to further promote the accuracy of reconstructing visual stimulus images. Firstly, an encoder based on residual-in-residual dense blocks is designed to fit the deep and multiview visual features of human natural state, and extract the features of visual stimulus images. Secondly, the structure of original decoder is extended to a deeper network in the deep generative multiview model, which makes the features obtained by each deconvolution layer more distinguishable. Finally, we configure the parameters of the optimizer and compare the performance of various optimizers under different parameter values, and then the one with the best performance is chosen and adopted to the whole model. The performance evaluations conducted on two publicly available datasets demonstrate that the improved model has more accurate reconstruction effectiveness than the original deep generative multiview model.

RevDate: 2022-12-05
CmpDate: 2022-12-05

Dimova-Edeleva V, Ehrlich SK, G Cheng (2022)

Brain computer interface to distinguish between self and other related errors in human agent collaboration.

Scientific reports, 12(1):20764.

When a human and machine collaborate on a shared task, ambiguous events might occur that could be perceived as an error by the human partner. In such events, spontaneous error-related potentials (ErrPs) are evoked in the human brain. Knowing whom the human perceived as responsible for the error would help a machine in co-adaptation and shared control paradigms to better adapt to human preferences. Therefore, we ask whether self- and agent-related errors evoke different ErrPs. Eleven subjects participated in an electroencephalography human-agent collaboration experiment with a collaborative trajectory-following task on two collaboration levels, where movement errors occurred as trajectory deviations. Independently of the collaboration level, we observed a higher amplitude of the responses on the midline central Cz electrode for self-related errors compared to observed errors made by the agent. On average, Support Vector Machines classified self- and agent-related errors with 72.64% accuracy using subject-specific features. These results demonstrate that ErrPs can tell if a person relates an error to themselves or an external autonomous agent during collaboration. Thus, the collaborative machine will receive more informed feedback for the error attribution that allows appropriate error identification, a possibility for correction, and avoidance in future actions.

RevDate: 2022-12-05
CmpDate: 2022-12-05

Tian X, Chen Y, Majka P, et al (2022)

An integrated resource for functional and structural connectivity of the marmoset brain.

Nature communications, 13(1):7416.

Comprehensive integration of structural and functional connectivity data is required to model brain functions accurately. While resources for studying the structural connectivity of non-human primate brains already exist, their integration with functional connectivity data has remained unavailable. Here we present a comprehensive resource that integrates the most extensive awake marmoset resting-state fMRI data available to date (39 marmoset monkeys, 710 runs, 12117 mins) with previously published cellular-level neuronal tracing data (52 marmoset monkeys, 143 injections) and multi-resolution diffusion MRI datasets. The combination of these data allowed us to (1) map the fine-detailed functional brain networks and cortical parcellations, (2) develop a deep-learning-based parcellation generator that preserves the topographical organization of functional connectivity and reflects individual variabilities, and (3) investigate the structural basis underlying functional connectivity by computational modeling. This resource will enable modeling structure-function relationships and facilitate future comparative and translational studies of primate brains.

RevDate: 2022-12-01

Bian R, Wu H, Liu B, et al (2022)

Small Data Least-Squares Transformation (sd-LST) for Fast Calibration of SSVEP-based BCIs.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society, PP: [Epub ahead of print].

Steady-state visual evoked potential (SSVEP) is one of the most popular brain-computer interface (BCI) paradigms, with high information transmission rate and signal-to-noise ratio. Many calibration-free and calibration-based approaches have been proposed to improve the performance of SSVEP-based BCIs. This paper considers a quick calibration scenario, where there are plenty of data from multiple source subjects, but only a small number of calibration trials from a subset of stimulus frequencies for the new subject. We propose small data least-squares transformation (sd-LST) to solve this problem. Experiments on three publicly available SSVEP datasets demonstrated that sd-LST outperformed several classical or state-of-the-art approaches, with only about 10 calibration trials for 40-target SSVEP-based BCI spellers.

RevDate: 2022-12-02

Weisinger B, Pandey DP, Saver JL, et al (2022)

Frequency-tuned electromagnetic field therapy improves post-stroke motor function: A pilot randomized controlled trial.

Frontiers in neurology, 13:1004677.

BACKGROUND AND PURPOSE: Impaired upper extremity (UE) motor function is a common disability after ischemic stroke. Exposure to extremely low frequency and low intensity electromagnetic fields (ELF-EMF) in a frequency-specific manner (Electromagnetic Network Targeting Field therapy; ENTF therapy) is a non-invasive method available to a wide range of patients that may enhance neuroplasticity, potentially facilitating motor recovery. This study seeks to quantify the benefit of the ENTF therapy on UE motor function in a subacute ischemic stroke population.

METHODS: In a randomized, sham-controlled, double-blind trial, ischemic stroke patients in the subacute phase with moderately to severely impaired UE function were randomly allocated to active or sham treatment with a novel, non-invasive, brain computer interface-based, extremely low frequency and low intensity ENTF therapy (1-100 Hz, < 1 G). Participants received 40 min of active ENTF or sham treatment 5 days/week for 8 weeks; ~three out of the five treatments were accompanied by 10 min of concurrent physical/occupational therapy. Primary efficacy outcome was improvement on the Fugl-Meyer Assessment - Upper Extremity (FMA-UE) from baseline to end of treatment (8 weeks).

RESULTS: In the per protocol set (13 ENTF and 8 sham participants), mean age was 54.7 years (±15.0), 19% were female, baseline FMA-UE score was 23.7 (±11.0), and median time from stroke onset to first stimulation was 11 days (interquartile range (IQR) 8-15). Greater improvement on the FMA-UE from baseline to week 4 was seen with ENTF compared to sham stimulation, 23.2 ± 14.1 vs. 9.6 ± 9.0, p = 0.007; baseline to week 8 improvement was 31.5 ± 10.7 vs. 23.1 ± 14.1. Similar favorable effects at week 8 were observed for other UE and global disability assessments, including the Action Research Arm Test (Pinch, 13.4 ± 5.6 vs. 5.3 ± 6.5, p = 0.008), Box and Blocks Test (affected hand, 22.5 ± 12.4 vs. 8.5 ± 8.6, p < 0.0001), and modified Rankin Scale (-2.5 ± 0.7 vs. -1.3 ± 0.7, p = 0.0005). No treatment-related adverse events were reported.

CONCLUSIONS: ENTF stimulation in subacute ischemic stroke patients was associated with improved UE motor function and reduced overall disability, and results support its safe use in the indicated population. These results should be confirmed in larger multicenter studies.

CLINICAL TRIAL REGISTRATION: https://clinicaltrials.gov/ct2/show/NCT04039178, identifier: NCT04039178.

RevDate: 2022-11-30

Vansteensel MJ, Klein E, van Thiel G, et al (2022)

Towards clinical application of implantable brain-computer interfaces for people with late-stage ALS: medical and ethical considerations.

Journal of neurology [Epub ahead of print].

Individuals with amyotrophic lateral sclerosis (ALS) frequently develop speech and communication problems in the course of their disease. Currently available augmentative and alternative communication technologies do not present a solution for many people with advanced ALS, because these devices depend on residual and reliable motor activity. Brain-computer interfaces (BCIs) use neural signals for computer control and may allow people with late-stage ALS to communicate even when conventional technology falls short. Recent years have witnessed fast progression in the development and validation of implanted BCIs, which place neural signal recording electrodes in or on the cortex. Eventual widespread clinical application of implanted BCIs as an assistive communication technology for people with ALS will have significant consequences for their daily life, as well as for the clinical management of the disease, among others because of the potential interaction between the BCI and other procedures people with ALS undergo, such as tracheostomy. This article aims to facilitate responsible real-world implementation of implanted BCIs. We review the state of the art of research on implanted BCIs for communication, as well as the medical and ethical implications of the clinical application of this technology. We conclude that the contribution of all BCI stakeholders, including clinicians of the various ALS-related disciplines, will be needed to develop procedures for, and shape the process of, the responsible clinical application of implanted BCIs.

RevDate: 2022-12-03
CmpDate: 2022-12-02

Kaushik P, Moye A, Vugt MV, et al (2022)

Decoding the cognitive states of attention and distraction in a real-life setting using EEG.

Scientific reports, 12(1):20649.

Lapses in attention can have serious consequences in situations such as driving a car, hence there is considerable interest in tracking it using neural measures. However, as most of these studies have been done in highly controlled and artificial laboratory settings, we want to explore whether it is also possible to determine attention and distraction using electroencephalogram (EEG) data collected in a natural setting using machine/deep learning. 24 participants volunteered for the study. Data were collected from pairs of participants simultaneously while they engaged in Tibetan Monastic debate, a practice that is interesting because it is a real-life situation that generates substantial variability in attention states. We found that attention was on average associated with increased left frontal alpha, increased left parietal theta, and decreased central delta compared to distraction. In an attempt to predict attention and distraction, we found that a Long Short Term Memory model classified attention and distraction with maximum accuracy of 95.86% and 95.4% corresponding to delta and theta waves respectively. This study demonstrates that EEG data collected in a real-life setting can be used to predict attention states in participants with good accuracy, opening doors for developing Brain-Computer Interfaces that track attention in real-time using data extracted in daily life settings, rendering them much more usable.

RevDate: 2022-12-01
CmpDate: 2022-12-01

Li Y, Qu T, Li D, et al (2022)

Human herpesvirus 7 encephalitis in an immunocompetent adult and a literature review.

Virology journal, 19(1):200.

BACKGROUND: Human herpesvirus 7 (HHV-7) is a common virus that infects children early and is accompanied by lifelong latency in cells, which is easy to reactivate in immunodeficient adults, but the underlying pathological mechanism is uncertain in immunocompetent adults without peculiar past medical history. Even though the clinical manifestation of the encephalitis caused by HHV-7 is uncommon in immunocompetent adults, the HHV-7 infection should not be neglected for encephalitis for unknown reasons.

CASE PRESENTATION: We reported here a case of HHV-7 encephalitis with epileptic seizures. While the brain computer tomography was standard, electroencephalography displayed slow waves in the temporal and bilateral frontal areas, then HHV-7 DNA was detected in the metagenomic next-generation sequencing of cerebrospinal fluid. Fortunately, the patient recovered after treatment and was discharged 2 months later. We also collected the related cases and explored a better way to illuminate the underlying mechanism.

CONCLUSION: The case indicates clinicians should memorize HHV-7 as an unusual etiology of encephalitis to make an early diagnosis and therapy.

RevDate: 2022-12-01
CmpDate: 2022-12-01

Yu XD, Zhu Y, Sun QX, et al (2022)

Distinct serotonergic pathways to the amygdala underlie separate behavioral features of anxiety.

Nature neuroscience, 25(12):1651-1663.

Anxiety-like behaviors in mice include social avoidance and avoidance of bright spaces. Whether these features are distinctly regulated is unclear. We demonstrate that in mice, social and anxiogenic stimuli, respectively, increase and decrease serotonin (5-HT) levels in basal amygdala (BA). In dorsal raphe nucleus (DRN), 5-HT∩vGluT3 neurons projecting to BA parvalbumin (DRN[5-HT∩vGluT3]-BA[PV]) and pyramidal (DRN[5-HT∩vGluT3]-BA[Pyr]) neurons have distinct intrinsic properties and gene expression and respond to anxiogenic and social stimuli, respectively. Activation of DRN[5-HT∩vGluT3]→BA[PV] inhibits 5-HT release via GABAB receptors on serotonergic terminals in BA, inducing social avoidance and avoidance of bright spaces. Activation of DRN[5-HT∩vGluT3]→BA neurons inhibits two subsets of BA[Pyr] neurons via 5-HT1A receptors (HTR1A) and 5-HT1B receptors (HTR1B). Pharmacological inhibition of HTR1A and HTR1B in BA induces avoidance of bright spaces and social avoidance, respectively. These findings highlight the functional significance of heterogenic inputs from DRN to BA subpopulations in the regulation of separate anxiety-related behaviors.

RevDate: 2022-12-02
CmpDate: 2022-12-01

Nicolelis MAL, Alho EJL, Donati ARC, et al (2022)

Training with noninvasive brain-machine interface, tactile feedback, and locomotion to enhance neurological recovery in individuals with complete paraplegia: a randomized pilot study.

Scientific reports, 12(1):20545.

In recent years, our group and others have reported multiple cases of consistent neurological recovery in people with spinal cord injury (SCI) following a protocol that integrates locomotion training with brain machine interfaces (BMI). The primary objective of this pilot study was to compare the neurological outcomes (motor, tactile, nociception, proprioception, and vibration) in both an intensive assisted locomotion training (LOC) and a neurorehabilitation protocol integrating assisted locomotion with a noninvasive brain-machine interface (L + BMI), virtual reality, and tactile feedback. We also investigated whether individuals with chronic-complete SCI could learn to perform leg motor imagery. We ran a parallel two-arm randomized pilot study; the experiments took place in São Paulo, Brazil. Eight adults sensorimotor-complete (AIS A) (all male) with chronic (> 6 months) traumatic spinal SCI participated in the protocol that was organized in two blocks of 14 weeks of training and an 8-week follow-up. The participants were allocated to either the LOC group (n = 4) or L + BMI group (n = 4) using block randomization (blinded outcome assessment). We show three important results: (i) locomotion training alone can induce some level of neurological recovery in sensorimotor-complete SCI, and (ii) the recovery rate is enhanced when such locomotion training is associated with BMI and tactile feedback (∆Mean Lower Extremity Motor score improvement for LOC = + 2.5, L + B = + 3.5; ∆Pinprick score: LOC = + 3.75, L + B = + 4.75 and ∆Tactile score LOC = + 4.75, L + B = + 9.5). (iii) Furthermore, we report that the BMI classifier accuracy was significantly above the chance level for all participants in L + B group. Our study shows potential for sensory and motor improvement in individuals with chronic complete SCI following a protocol with BMIs and locomotion therapy. We report no dropouts nor adverse events in both subgroups participating in the study, opening the possibility for a more definitive clinical trial with a larger cohort of people with SCI.Trial registration: http://www.ensaiosclinicos.gov.br/ identifier RBR-2pb8gq.

RevDate: 2022-11-29

Heubel-Moenen FCJI, Ansems LEM, Verhezen PWM, et al (2022)

Effectiveness and costs of a stepwise versus an all-in-one approach to diagnose mild bleeding disorders.

British journal of haematology [Epub ahead of print].

The diagnostic work-up of patients referred to the haematologist for bleeding evaluation is performed in a stepwise way: bleeding history and results of screening laboratory tests guide further diagnostic evaluation. This can be ineffective, time-consuming and burdensome for patients. To improve this strategy, the initial laboratory investigation can be extended. In a model-based approach, effectiveness and costs of a conventional stepwise versus a newly proposed all-in-one diagnostic approach for bleeding evaluation were evaluated and compared, using data from an observational patient cohort study, including adult patients referred for bleeding evaluation. In the all-in-one approach, specialized platelet function tests, coagulation factors, and fibrinolysis tests were included in the initial investigation. Final diagnosis, hospital resource use and costs and patient burden were compared. A total of 150 patients were included. Compared to the stepwise approach, in the all-in-one approach, 19 additional patients reached a diagnosis and patient burden was lower, but total costs per patient were higher [€359, 95% bootstrapped confidence interval (BCI) 283-518, p = 0.001]. For bleeding evaluation of patients referred to the haematologist, an all-in-one diagnostic approach has a higher diagnostic yield and reduces patient burden, at a higher cost. This raises the question what costs justify the diagnosis of a bleeding disorder and a less burdensome diagnostic strategy.

RevDate: 2022-11-28

Strypsteen T, A BertrandSenior Member (2022)

Bandwidth-efficient distributed neural network architectures with application to neuro-sensor networks.

IEEE journal of biomedical and health informatics, PP: [Epub ahead of print].

In this paper, we describe a conceptual design methodology to design distributed neural network architectures that can perform efficient inference within sensor networks with communication bandwidth constraints. The different sensor channels are distributed across multiple sensor devices, which have to exchange data over bandwidth-limited communication channels to solve a classification task. Our design methodology starts from a user-defined centralized neural network and transforms it into a distributed architecture in which the channels are distributed over different nodes. The distributed network consists of two parallel branches, whose outputs are fused at the fusion center. The first branch collects classification results from local, node-specific classifiers while the second branch compresses each node's signal and then reconstructs the multi-channel time series for classification at the fusion center. We further improve bandwidth gains by dynamically activating the compression path when the local classifications do not suffice. We validate this method on a motor execution task in an emulated EEG sensor network and analyze the resulting bandwidth-accuracy trade-offs. Our experiments show that the proposed framework enables up to a factor 20 in bandwidth reduction and factor 9 in power reduction with minimal loss (up to 2%) in classification accuracy compared to the centralized baseline on the demonstrated task. The proposed method offers a way to smoothly transform a centralized architecture to a distributed, bandwidth-efficient network amenable for low-power sensor networks. While the application focus of this paper is on wearable brain-computer interfaces, the proposed methodology can be applied in other sensor network-like applications as well.

RevDate: 2022-11-28

Wood CR, Xi Y, Yang WJ, et al (2022)

Insight into Neuroethical Considerations of the Newly Emerging Technologies and Techniques of the Global Brain Initiatives.

Neuroscience bulletin [Epub ahead of print].

RevDate: 2022-11-28

Kong X, Shu X, Wang J, et al (2022)

Fine-tuning of mTOR signaling by the UBE4B-KLHL22 E3 ubiquitin ligase cascade in brain development.

Development (Cambridge, England) pii:285123 [Epub ahead of print].

Spatiotemporal regulation of the mechanistic target of rapamycin (mTOR) pathway is pivotal for establishment of brain architecture. Dysregulation of mTOR signaling is associated with a variety of neurodevelopmental disorders (NDDs). Here, we discover that the UBE4B-KLHL22 E3 ubiquitin ligase cascade regulates mTOR activity in neurodevelopment. In a mouse model with UBE4B conditionally deleted in the nervous system, animals display severe growth defects, spontaneous seizures, and premature death. Loss of UBE4B in the brains of mutant mice results in depletion of neural precursor cells (NPCs) and impairment of neurogenesis. Mechanistically, UBE4B polyubiquitinates and degrades KLHL22, an E3 ligase previously shown to degrade the GATOR1 component DEPDC5. Deletion of UBE4B causes upregulation of KLHL22 and hyperactivation of mTOR, leading to defective proliferation and differentiation of NPCs. Suppression of KLHL22 expression reverses the elevated activity of mTOR caused by acute local deletion of UBE4B. Prenatal treatment with the mTOR inhibitor rapamycin rescues neurogenesis defects in Ube4b mutant mice. Taken together, these findings demonstrate that UBE4B and KLHL22 are essential for maintenance and differentiation of the precursor pool through fine-tuning of mTOR activity.

RevDate: 2022-11-29

Sisti HM, Beebe A, Bishop M, et al (2022)

A brief review of motor imagery and bimanual coordination.

Frontiers in human neuroscience, 16:1037410.

Motor imagery is increasingly being used in clinical settings, such as in neurorehabilitation and brain computer interface (BCI). In stroke, patients lose upper limb function and must re-learn bimanual coordination skills necessary for the activities of daily living. Physiotherapists integrate motor imagery with physical rehabilitation to accelerate recovery. In BCIs, users are often asked to imagine a movement, often with sparse instructions. The EEG pattern that coincides with this cognitive task is captured, then used to execute an external command, such as operating a neuroprosthetic device. As such, BCIs are dependent on the efficient and reliable interpretation of motor imagery. While motor imagery improves patient outcome and informs BCI research, the cognitive and neurophysiological mechanisms which underlie it are not clear. Certain types of motor imagery techniques are more effective than others. For instance, focusing on kinesthetic cues and adopting a first-person perspective are more effective than focusing on visual cues and adopting a third-person perspective. As motor imagery becomes more dominant in neurorehabilitation and BCIs, it is important to elucidate what makes these techniques effective. The purpose of this review is to examine the research to date that focuses on both motor imagery and bimanual coordination. An assessment of current research on these two themes may serve as a useful platform for scientists and clinicians seeking to use motor imagery to help improve bimanual coordination, either through augmenting physical therapy or developing more effective BCIs.

RevDate: 2022-11-27

Robinson DA, Foster ME, Bennett CH, et al (2022)

Tunable Intervalence Charge Transfer in Ruthenium Prussian Blue Analogue Enables Stable and Efficient Biocompatible Artificial Synapses.

Advanced materials (Deerfield Beach, Fla.) [Epub ahead of print].

Emerging concepts for neuromorphic computing, bioelectronics, and brain-computer interfacing inspire new research avenues aimed at understanding the relationship between oxidation state and conductivity in unexplored materials. This report expands the materials playground for neuromorphic devices to include a mixed valence inorganic 3D coordination framework, a ruthenium Prussian blue analogue (RuPBA), for flexible and biocompatible artificial synapses that reversibly switch conductance by more than four orders of magnitude based on electrochemically tunable oxidation state. The electrochemically tunable degree of mixed valency and electronic coupling between N-coordinated Ru sites controls the carrier concentration and mobility, as supported by density functional theory (DFT) computations and application of electron transfer theory to in-situ spectroscopy of intervalence charge transfer. Retention of programmed states is improved by nearly two orders of magnitude compared to extensively studied organic polymers, thus reducing the frequency, complexity and energy costs associated with error correction schemes. This report demonstrates dopamine-mediated plasticity of RuPBA synapses and biocompatibility of RuPBA with neuronal cells, evoking prospective application for brain-computer interfacing. This article is protected by copyright. All rights reserved.

RevDate: 2022-11-29
CmpDate: 2022-11-29

Wang H, Zhu C, Jin W, et al (2022)

A Linear-Power-Regulated Wireless Power Transfer Method for Decreasing the Heat Dissipation of Fully Implantable Microsystems.

Sensors (Basel, Switzerland), 22(22): pii:s22228765.

Magnetic coupling resonance wireless power transfer can efficiently provide energy to intracranial implants under safety constraints, and is the main way to power fully implantable brain-computer interface systems. However, the existing maximum efficiency tracking wireless power transfer system is aimed at optimizing the overall system efficiency, but the efficiency of the secondary side is not optimized. Moreover, the parameters of the transmitter and the receiver change nonlinearly in the power control process, and the efficiency tracking mainly depends on wireless communication. The heat dissipation caused by the unoptimized receiver efficiency and the wireless communication delay in power control will inevitably affect neural activity and even cause damage, thus affecting the results of neuroscience research. Here, a linear-power-regulated wireless power transfer method is proposed to realize the linear change of the received power regulation and optimize the receiver efficiency, and a miniaturized linear-power-regulated wireless power transfer system is developed. With the received power control, the efficiency of the receiver is increased to more than 80%, which can significantly reduce the heating of fully implantable microsystems. The linear change of the received power regulation makes the reflected impedance in the transmitter change linearly, which will help to reduce the dependence on wireless communication and improve biological safety in received power control applications.

RevDate: 2022-11-29
CmpDate: 2022-11-29

Chang D, Xiang Y, Zhao J, et al (2022)

Exploration of Brain-Computer Interaction for Supporting Children's Attention Training: A Multimodal Design Based on Attention Network and Gamification Design.

International journal of environmental research and public health, 19(22): pii:ijerph192215046.

Recent developments in brain-computer interface (BCI) technology have shown great potential in terms of estimating users' mental state and supporting children's attention training. However, existing training tasks are relatively simple and lack a reliable task-generation process. Moreover, the training experience has not been deeply studied, and the empirical validation of the training effect is still insufficient. This study thusly proposed a BCI training system for children's attention improvement. In particular, to achieve a systematic training process, the attention network was referred to generate the training games for alerting, orienting and executive attentions, and to improve the training experience and adherence, the gamification design theory was introduced to derive attractive training tasks. A preliminary experiment was conducted to set and modify the training parameters. Subsequently, a series of contrasting user experiments were organized to examine the impact of BCI training. To test the training effect of the proposed system, a hypothesis-testing approach was adopted. The results revealed that the proposed BCI gamification attention training system can significantly improve the participants' attention behaviors and concentration ability. Moreover, an immersive, inspiring and smooth training process can be created, and a pleasant user experience can be achieved. Generally, this work is promising in terms of providing a valuable reference for related practices, especially for how to generate BCI attention training tasks using attention networks and how to improve training adherence by integrating multimodal gamification elements.

RevDate: 2022-11-28

Shovon MSH, Islam MJ, Nabil MNAK, et al (2022)

Strategies for Enhancing the Multi-Stage Classification Performances of HER2 Breast Cancer from Hematoxylin and Eosin Images.

Diagnostics (Basel, Switzerland), 12(11): pii:diagnostics12112825.

Breast cancer is a significant health concern among women. Prompt diagnosis can diminish the mortality rate and direct patients to take steps for cancer treatment. Recently, deep learning has been employed to diagnose breast cancer in the context of digital pathology. To help in this area, a transfer learning-based model called 'HE-HER2Net' has been proposed to diagnose multiple stages of HER2 breast cancer (HER2-0, HER2-1+, HER2-2+, HER2-3+) on H&E (hematoxylin & eosin) images from the BCI dataset. HE-HER2Net is the modified version of the Xception model, which is additionally comprised of global average pooling, several batch normalization layers, dropout layers, and dense layers with a swish activation function. This proposed model exceeds all existing models in terms of accuracy (0.87), precision (0.88), recall (0.86), and AUC score (0.98) immensely. In addition, our proposed model has been explained through a class-discriminative localization technique using Grad-CAM to build trust and to make the model more transparent. Finally, nuclei segmentation has been performed through the StarDist method.

RevDate: 2022-11-26

Si C, Qin H, Chuanzhuang Y, et al (2022)

Study of event-related potentials by withdrawal friction on the fingertip.

Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging (ISSI) [Epub ahead of print].

OBJECTIVES: The lack of understanding about the brain's reaction processes in perceiving touch and separation between skin and object surfaces is a barrier to the development of existing brain-computer interface technologies and virtual haptics. These technologies are limited in their ability to advance. It leaves prosthesis users with a limited amount of tactile information that they can feel. This study aims to determine whether distinct surface aspects of various items trigger different reactions from the brain when friction is removed from the surface.

METHODS: When friction is suddenly removed from the surface of an item, a technique called event-related potential,or ERP, is used to study the features of people's EEGs. It is done after the subject has actively explored the object's surface. A Neuroscan 64-conductor event-related potential device was utilized to acquire EEG data from the individuals. The event-related potentials for friction removal were generated using the Oddball paradigm, and the samples consisted of sandpaper with three distinct degrees of roughness. We utilized a total of 20 participants, 10 of whom were male, and 10 of whom were female, with a mean age of 21 years.

RESULTS: It was discovered that the P3 component of event-related potentials, which is essential for cognition, was noticeably absent in the friction withdrawal response for various roughnesses. It was the case regardless of whether the surface was smooth or rough. Moreover, there was no statistically significant difference between the P1 andP2 components, which suggests that the brain could not recognize the surface properties of objects with varying roughness as the friction withdrawal was being performed.

CONCLUSIONS: It has been demonstrated that tactile recognition does not occur after friction withdrawal. The findings of this paper could have significant repercussions for future research involving the study of haptic perception and brain-computer interaction in prosthetic hands. It is a step toward future research on the mechanisms underlying human tactile perception, so think of it as preparation.

RevDate: 2022-11-25

Deng X, Wang Z, Liu K, et al (2022)

A GAN model encoded by CapsEEGNet for visual EEG encoding and image reproduction.

Journal of neuroscience methods pii:S0165-0270(22)00273-4 [Epub ahead of print].

In last few decades, reading the human mind is an innovative topic in scientific research. Recent studies in neuroscience indicate that it is possible to decode the signals of the human brain based on the neuroimaging data. The work in this paper explores the possibility of building an end-to-end BCI system to learn and visualize the brain thoughts evoked by the stimulating images. To achieve this goal, it designs an experiment to collect the EEG signals evoked by randomly presented images. Based on these data, this work analyzes and compares the classification abilities by several improved methods, including the Transformer, CapsNet and the ensemble strategies. After obtaining the optimal method to be the encoder, this paper proposes a distribution-to-distribution mapping network to transform an encoded latent feature vector into a prior image feature vector. To visualize the brain thoughts, a pretrained IC-GAN model is used to receive these image feature vectors and generate images. Extensive experiments are carried out and the results show that the proposed method can effectively deal with the small sample data original from the less electrode channels. By examining the generated images coming from the EEG signals, it verifies that the proposed model is capable of reproducing the images seen by human eyes to some extent.

RevDate: 2022-12-07
CmpDate: 2022-12-05

Colucci A, Vermehren M, Cavallo A, et al (2022)

Brain-Computer Interface-Controlled Exoskeletons in Clinical Neurorehabilitation: Ready or Not?.

Neurorehabilitation and neural repair, 36(12):747-756.

The development of brain-computer interface-controlled exoskeletons promises new treatment strategies for neurorehabilitation after stroke or spinal cord injury. By converting brain/neural activity into control signals of wearable actuators, brain/neural exoskeletons (B/NEs) enable the execution of movements despite impaired motor function. Beyond the use as assistive devices, it was shown that-upon repeated use over several weeks-B/NEs can trigger motor recovery, even in chronic paralysis. Recent development of lightweight robotic actuators, comfortable and portable real-world brain recordings, as well as reliable brain/neural control strategies have paved the way for B/NEs to enter clinical care. Although B/NEs are now technically ready for broader clinical use, their promotion will critically depend on early adopters, for example, research-oriented physiotherapists or clinicians who are open for innovation. Data collected by early adopters will further elucidate the underlying mechanisms of B/NE-triggered motor recovery and play a key role in increasing efficacy of personalized treatment strategies. Moreover, early adopters will provide indispensable feedback to the manufacturers necessary to further improve robustness, applicability, and adoption of B/NEs into existing therapy plans.

RevDate: 2022-11-24

Chen YF, Fu R, Wu J, et al (2022)

Continuous Bimanual Trajectory Decoding of Coordinated Movement from EEG Signals.

IEEE journal of biomedical and health informatics, PP: [Epub ahead of print].

While many voluntary movements involve bimanual coordination, few attempts have been made to simultaneously decode the trajectory of bimanual movements from electroencephalogram (EEG) signals. In this study, we proposed a novel bimanual brain-computer interface (BCI) paradigm to reconstruct the continuous trajectory of both hands during coordinated movements from EEG. The protocol required human subjects to complete a bimanual reaching task to the left, middle, or right target while EEG data were collected. A multi-task deep learning model combining the EEGNet and long short-term memory network (LSTM) was proposed to decode bimanual trajectories, including position and velocity. Decoding performance was evaluated in terms of the correlation coefficient (CC) and normalized root mean square error (NRMSE) between decoded and real trajectories. Experimental results from 13 human subjects showed that the grand-averaged combined CC values achieved 0.54 and 0.42 for position and velocity decoding, respectively. The corresponding combined NRMSE values were 0.22 and 0.23. Both CC and NRMSE were significantly superior to the chance level (p<0.05). Comparative experiments also indicated that the proposed model significantly outperformed some other commonly-used methods in terms of CC and NRMSE for continuous trajectory decoding. These findings demonstrated the feasibility of simultaneously decoding bimanual trajectory from EEG, indicating the potential of bimanual control for coordinated tasks.

RevDate: 2022-11-29

Diao X, Luo D, Wang D, et al (2022)

Lurasidone versus Quetiapine for Cognitive Impairments in Young Patients with Bipolar Depression: A Randomized, Controlled Study.

Pharmaceuticals (Basel, Switzerland), 15(11):.

The clinical efficacy of lurasidone and quetiapine, two commonly prescribed atypical antipsychotics for bipolar depression, has been inadequately studied in young patients. In this randomized and controlled study, we aimed to compare the effects of these two drugs on cognitive function, emotional status, and metabolic profiles in children and adolescents with bipolar depression. We recruited young participants (aged 10-17 years old) with a DSM-5 diagnosis of bipolar disorder during a depressive episode, who were then randomly assigned to two groups and treated with flexible doses of lurasidone (60 to 120 mg/day) or quetiapine (300 to 600 mg/day) for consecutive 8 weeks, respectively. All the participants were clinically evaluated on cognitive function using the THINC-it instrument at baseline and week 8, and emotional status was assessed at baseline and the end of week 2, 4, and 8. Additionally, the changes in weight and serum metabolic profiles (triglyceride, cholesterol, and fasting blood glucose) during the trial were also analyzed. In results, a total of 71 patients were randomly assigned to the lurasidone group (n = 35) or the quetiapine group (n = 36), of which 31 patients completed the whole treatment course. After an 8-week follow-up, participants in the lurasidone group showed better performance in the Symbol Check Reaction and Accuracy Tests, when compared to those in the quetiapine group. No inter-group difference was observed in the depression scores, response rate, or remission rate throughout the trial. In addition, there was no significant difference in serum metabolic profiles between the lurasidone group and the quetiapine group, including triglyceride level, cholesterol level, and fasting blood glucose level. However, the quetiapine group presented a more apparent change in body weight than the lurasidone group. In conclusion, the present study provided preliminary evidence that quetiapine and lurasidone had an equivalent anti-depressive effect, and lurasidone appeared to be superior to quetiapine in improving the cognitive function of young patients with bipolar depression.

RevDate: 2022-11-26

Li J, Huang B, Wang F, et al (2022)

A Potential Prognosis Indicator Based on P300 Brain-Computer Interface for Patients with Disorder of Consciousness.

Brain sciences, 12(11):.

For patients with disorders of consciousness, such as unresponsive wakefulness syndrome (UWS) patients and minimally conscious state (MCS) patients, their long treatment cycle and high cost commonly put a heavy burden on the patient's family and society. Therefore, it is vital to accurately diagnose and predict consciousness recovery for such patients. In this paper, we explored the role of the P300 signal based on an audiovisual BCI in the classification and prognosis prediction of patients with disorders of consciousness. This experiment included 18 patients: 10 UWS patients and 8 MCS- patients. At the three-month follow-up, we defined patients with an improved prognosis (from UWS to MCS-, from UWS to MCS+, or from MCS- to MCS+) as "improved patients" and those who stayed in UWS/MCS as "not improved patients". First, we compared and analyzed different types of patients, and the results showed that the P300 detection accuracy rate of "improved" patients was significantly higher than that of "not improved" patients. Furthermore, the P300 detection accuracy of traumatic brain injury (TBI) patients was significantly higher than that of non-traumatic brain injury (NTBI, including acquired brain injury and cerebrovascular disease) patients. We also found that there was a positive linear correlation between P300 detection accuracy and CRS-R score, and patients with higher P300 detection accuracy were likely to achieve higher CRS-R scores. In addition, we found that the patients with higher P300 detection accuracies tend to have better prognosis in this audiovisual BCI. These findings indicate that the detection accuracy of P300 is significantly correlated with the level of consciousness, etiology, and prognosis of patients. P300 can be used to represent the preservation level of consciousness in clinical neurophysiology and predict the possibility of recovery in patients with disorders of consciousness.

RevDate: 2022-11-26

Zavala Hernández JG, LI Barbosa-Santillán (2022)

Virtual Intelligence: A Systematic Review of the Development of Neural Networks in Brain Simulation Units.

Brain sciences, 12(11):.

The functioning of the brain has been a complex and enigmatic phenomenon. From the first approaches made by Descartes about this organism as the vehicle of the mind to contemporary studies that consider the brain as an organism with emergent activities of primary and higher order, this organism has been the object of continuous exploration. It has been possible to develop a more profound study of brain functions through imaging techniques, the implementation of digital platforms or simulators through different programming languages and the use of multiple processors to emulate the speed at which synaptic processes are executed in the brain. The use of various computational architectures raises innumerable questions about the possible scope of disciplines such as computational neurosciences in the study of the brain and the possibility of deep knowledge into different devices with the support that information technology (IT) brings. One of the main interests of cognitive science is the opportunity to develop human intelligence in a system or mechanism. This paper takes the principal articles of three databases oriented to computational sciences (EbscoHost Web, IEEE Xplore and Compendex Engineering Village) to understand the current objectives of neural networks in studying the brain. The possible use of this kind of technology is to develop artificial intelligence (AI) systems that can replicate more complex human brain tasks (such as those involving consciousness). The results show the principal findings in research and topics in developing studies about neural networks in computational neurosciences. One of the principal developments is the use of neural networks as the basis of much computational architecture using multiple techniques such as computational neuromorphic chips, MRI images and brain-computer interfaces (BCI) to enhance the capacity to simulate brain activities. This article aims to review and analyze those studies carried out on the development of different computational architectures that focus on affecting various brain activities through neural networks. The aim is to determine the orientation and the main lines of research on this topic and work in routes that allow interdisciplinary collaboration.

RevDate: 2022-12-07
CmpDate: 2022-12-07

Jervis-Rademeyer H, Ong K, Djuric A, et al (2022)

Therapists' perspectives on using brain-computer interface-triggered functional electrical stimulation therapy for individuals living with upper extremity paralysis: a qualitative case series study.

Journal of neuroengineering and rehabilitation, 19(1):127.

BACKGROUND: Brain computer interface-triggered functional electrical stimulation therapy (BCI-FEST) has shown promise as a therapy to improve upper extremity function for individuals who have had a stroke or spinal cord injury. The next step is to determine whether BCI-FEST could be used clinically as part of broader therapy practice. To do this, we need to understand therapists' opinions on using the BCI-FEST and what limitations potentially exist. Therefore, we conducted a qualitative exploratory study to understand the perspectives of therapists on their experiences delivering BCI-FEST and the feasibility of large-scale clinical implementation.

METHODS: Semi-structured interviews were conducted with physical therapists (PTs) and occupational therapists (OTs) who have delivered BCI-FEST. Interview questions were developed using the COM-B (Capability, Opportunity, Motivation-Behaviour) model of behaviour change. COM-B components were used to inform deductive content analysis while other subthemes were detected using an inductive approach.

RESULTS: We interviewed PTs (n = 3) and OTs (n = 3), with 360 combined hours of experience delivering BCI-FEST. Components and subcomponents of the COM-B determined deductively included: (1) Capability (physical, psychological), (2) Opportunity (physical, social), and (3) Motivation (automatic, reflective). Under each deductive subcomponent, one to two inductive subthemes were identified (n = 8). Capability and Motivation were perceived as strengths, and therefore supported therapists' decisions to use BCI-FEST. Under Opportunity, for both subcomponents (physical, social), therapists recognized the need for more support to clinically implement BCI-FEST.

CONCLUSIONS: We identified facilitating and limiting factors to BCI-FEST delivery in a clinical setting according to clinicians. These factors implied that education, training, a support network or mentors, and restructuring the physical environment (e.g., scheduling) should be targeted as interventions. The results of this study may help to inform future development of new technologies and interventions.

RevDate: 2022-11-23

Baudry AS, Vanlemmens L, Congard A, et al (2022)

Emotional processes in partners' quality of life at various stages of breast cancer pathway: a longitudinal study.

Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation [Epub ahead of print].

INTRODUCTION: Several studies have shown that emotional competence (EC) impacts cancer adjustment via anxiety and depression symptoms. The objective was to test this model for the quality of life (QoL) of partners: first, the direct effect of partners' EC on their QoL, anxiety and depression symptoms after cancer diagnosis (T1), after chemotherapy (T2) and after radiotherapy (T3); Second, the indirect effects of partners' EC at T1 on their QoL at T2 and T3 through anxiety and depression symptoms.

METHODS: 192 partners of women with breast cancer completed a questionnaire at T1, T2 and T3 to assess their EC (PEC), anxiety and depression symptoms (HADS) and QoL (Partner-YW-BCI). Partial correlations and regression analyses were performed to test direct and indirect effects of EC on issues.

RESULTS: EC at T1 predicted fewer anxiety and depression symptoms at each time and all dimensions of QoL, except for career management and financial difficulties. EC showed different significant indirect effects (i.e. via anxiety or depression symptoms) on all sub-dimensions of QoL, except for financial difficulties, according to the step of care pathway (T2 and T3). Anxiety and depression played a different role in the psychological processes that influence QoL.

CONCLUSION: Findings confirm the importance of taking emotional processes into account in the adjustment of partners, especially regarding their QoL and the support they may provide to patients. It, thus, seems important to integrate EC in future health models and psychosocial interventions focused on partners or caregivers.

RevDate: 2022-11-21

Kucewicz MT, Worrell GA, N Axmacher (2022)

Direct electrical brain stimulation of human memory: lessons learnt and future perspectives.

Brain : a journal of neurology pii:6835141 [Epub ahead of print].

Modulation of cognitive functions supporting human declarative memory is one of the grand challenges of neuroscience, and of vast importance for a variety of neuropsychiatric, neurodegenerative and neurodevelopmental diseases. Despite a recent surge of successful attempts at improving performance in a range of memory tasks, the optimal approaches and parameters for memory enhancement have yet to be determined. On a more fundamental level, it remains elusive how delivering electrical current in a given brain area leads to enhanced memory processing. Starting from the local and distal physiological effects on neural populations, the mechanisms of enhanced memory encoding, maintenance, consolidation, or recall in response to direct electrical stimulation are only now being unraveled. With the advent of innovative neurotechnologies for concurrent recording and stimulation intracranially in the human brain, it becomes possible to study both acute and chronic effects of stimulation on memory performance and the underlying neural activities. In this review, we summarize the effects of various invasive stimulation approaches for modulating memory functions. We first outline the challenges that were faced in the initial studies of memory enhancement and the lessons learned. Electrophysiological biomarkers are then reviewed as more objective measures of the stimulation effects than behavioral outcomes. Finally, we classify the various stimulation approaches into continuous and phasic modulation with open or closed loop for responsive stimulation based on analysis of the recorded neural activities. Although the potential advantage of closed-loop responsive stimulation over the classic open-loop approaches is inconclusive, we foresee the emerging results from ongoing longitudinal studies and clinical trials to shed light on both the mechanisms and optimal strategies for improving declarative memory. Adaptive stimulation based on the biomarker analysis over extended periods of time is proposed as a future direction for obtaining lasting effects on memory functions. Chronic tracking and modulation of neural activities intracranially through adaptive stimulation opens tantalizing new avenues to continually monitor and treat memory and cognitive deficits in a range of brain disorders. Brain co-processors created with machine-learning tools and wireless bi-directional connectivity to seamlessly integrate implanted devices with smartphones and cloud computing are poised to enable real-time automated analysis of large data volumes and adaptively tune electrical stimulation based on electrophysiological biomarkers of behavioral states. Next generation implantable devices for high-density recording and stimulation of electrophysiological activities, and technologies for distributed brain-computer interfaces are presented as selected future perspectives for modulating human memory and associated mental processes.

RevDate: 2022-11-22

Hu J, Zou J, Wan Y, et al (2022)

Rehabilitation of motor function after stroke: A bibliometric analysis of global research from 2004 to 2022.

Frontiers in aging neuroscience, 14:1024163.

BACKGROUND AND AIMS: The mortality rate of stroke has been increasing worldwide. Poststroke somatic dysfunctions are common. Motor function rehabilitation of patients with such somatic dysfunctions enhances the quality of life and has long been the primary practice to achieve functional recovery. In this regard, we aimed to delineate the new trends and frontiers in stroke motor function rehabilitation literature published from 2004 to 2022 using a bibliometric software.

METHODS: All documents related to stroke rehabilitation and published from 2004 to 2022 were retrieved from the Web of Science Core Collection. Publication output, research categories, countries/institutions, authors/cocited authors, journals/cocited journals, cocited references, and keywords were assessed using VOSviewer v.1.6.15.0 and CiteSpace version 5.8. The cocitation map was plotted according to the analysis results to intuitively observe the research hotspots.

RESULTS: Overall, 3,302 articles were retrieved from 78 countries or regions and 564 institutions. Over time, the publication outputs increased annually. In terms of national contribution, the United States published the most papers, followed by China, Japan, South Korea, and Canada. Yeungnam University had the most articles among all institutions, followed by Emory University, Fudan University, and National Taiwan University. Jang Sung Ho and Wolf S.L. were the most productive (56 published articles) and influential (cited 1,121 times) authors, respectively. "Effect of constraint-induced movement therapy on upper extremity function 3-9 months after stroke: the Extremity Constraint Induced Therapy Evaluation randomized clinical trial" was the most frequently cited reference. Analysis of keywords showed that upper limbs, Fugl-Meyer assessment, electromyography, virtual reality, telerehabilitation, exoskeleton, and brain-computer interface were the research development trends and focus areas for this topic.

CONCLUSION: Publications regarding motor function rehabilitation following stroke are likely to continuously increase. Research on virtual reality, telemedicine, electroacupuncture, the brain-computer interface, and rehabilitation robots has attracted increasing attention, with these topics becoming the hotspots of present research and the trends of future research.

RevDate: 2022-11-22

Cui Z, Li Y, Huang S, et al (2022)

BCI system with lower-limb robot improves rehabilitation in spinal cord injury patients through short-term training: a pilot study.

Cognitive neurodynamics, 16(6):1283-1301.

UNLABELLED: In the recent years, the increasing applications of brain-computer interface (BCI) in rehabilitation programs have enhanced the chances of functional recovery for patients with neurological disorders. We presented and validated a BCI system with a lower-limb robot for short-term training of patients with spinal cord injury (SCI). The cores of this system included: (1) electroencephalogram (EEG) features related to motor intention reported through experiments and used to drive the robot; (2) a decision tree to determine the training mode provided for patients with different degrees of injuries. Seven SCI patients (one American Spinal Injury Association Impairment Scale (AIS) A, three AIS B, and three AIS C) participated in the short-term training with this system. All patients could learn to use the system rapidly and maintained a high intensity during the training program. The strength of the lower limb key muscles of the patients was improved. Four AIS A/B patients were elevated to AIS C. The cumulative results indicate that clinical application of the BCI system with lower-limb robot is feasible and safe, and has potentially positive effects on SCI patients.

SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11571-022-09801-6.

RevDate: 2022-11-22

Jaipriya D, KC Sriharipriya (2022)

A comparative analysis of masking empirical mode decomposition and a neural network with feed-forward and back propagation along with masking empirical mode decomposition to improve the classification performance for a reliable brain-computer interface.

Frontiers in computational neuroscience, 16:1010770.

In general, extraction and classification are used in various fields like image processing, pattern recognition, signal processing, and so on. Extracting effective characteristics from raw electroencephalogram (EEG) signals is a crucial role of the brain-computer interface for motor imagery. Recently, there has been a great deal of focus on motor imagery in the EEG signals since they encode a person's intent to do an action. Researchers have been using MI signals to assist paralyzed people and even move them on their own with certain equipment, like wheelchairs. As a result, proper decoding is an important step required for the interconnection of the brain and the computer. EEG decoding is a challenging process because of poor SNR, complexity, and other reasons. However, choosing an appropriate method to extract the features to improve the performance of motor imagery recognition is still a research hotspot. To extract the features of the EEG signal in the classification task, this paper proposes a Masking Empirical Mode Decomposition (MEMD) based Feed Forward Back Propagation Neural Network (MEMD-FFBPNN). The dataset consists of EEG signals which are first normalized using the minimax method and given as input to the MEMD to extract the features and then given to the FFBPNN to classify the tasks. The accuracy of the proposed method MEMD-FFBPNN has been measured using the confusion matrix, mean square error and which has been recorded up to 99.9%. Thus, the proposed method gives better accuracy than the other conventional methods.

RevDate: 2022-11-20

Zhang F, Zhang L, Xia J, et al (2022)

Multimodal Electrocorticogram Active Electrode Array Based on Zinc Oxide-Thin Film Transistors.

Advanced science (Weinheim, Baden-Wurttemberg, Germany) [Epub ahead of print].

Active electrocorticogram (ECoG) electrodes can amplify weak electrophysiological signals and improve anti-interference ability; however, traditional active electrodes are opaque and cannot realize photoelectric collaborative observation. In this study, an active and fully transparent ECoG array based on zinc oxide thin-film transistors (ZnO TFTs) is developed as a local neural signal amplifier for electrophysiological monitoring. The transparency of the proposed ECoG array is up to 85%, which is superior to that of the previously reported active electrode arrays. Various electrical characterizations have demonstrated its ability to record electrophysiological signals with a higher signal-to-noise ratio of 19.9 dB compared to the Au grid (13.2 dB). The high transparency of the ZnO-TFT electrode array allows the concurrent collection of high-quality electrophysiological signals (32.2 dB) under direct optical stimulation of the optogenetic mice brain. The ECoG array can also work under 7-Tesla magnetic resonance imaging to record local brain signals without affecting brain tissue imaging. As the most transparent active ECoG array to date, it provides a powerful multimodal tool for brain observation, including recording brain activity under synchronized optical modulation and 7-Tesla magnetic resonance imaging.

RevDate: 2022-11-20

Klein E, Kinsella M, Stevens I, et al (2022)

Ethical issues raised by incorporating personalized language models into brain-computer interface communication technologies: a qualitative study of individuals with neurological disease.

Disability and rehabilitation. Assistive technology [Epub ahead of print].

PURPOSE: To examine the views of individuals with neurodegenerative diseases about ethical issues related to incorporating personalized language models into brain-computer interface (BCI) communication technologies.

METHODS: Fifteen semi-structured interviews and 51 online free response surveys were completed with individuals diagnosed with neurodegenerative disease that could lead to loss of speech and motor skills. Each participant responded to questions after six hypothetical ethics vignettes were presented that address the possibility of building language models with personal words and phrases in BCI communication technologies. Data were analyzed with consensus coding, using modified grounded theory.

RESULTS: Four themes were identified. (1) The experience of a neurodegenerative disease shapes preferences for personalized language models. (2) An individual's identity will be affected by the ability to personalize the language model. (3) The motivation for personalization is tied to how relationships can be helped or harmed. (4) Privacy is important to people who may need BCI communication technologies. Responses suggest that the inclusion of personal lexica raises ethical issues. Stakeholders want their values to be considered during development of BCI communication technologies.

CONCLUSIONS: With the rapid development of BCI communication technologies, it is critical to incorporate feedback from individuals regarding their ethical concerns about the storage and use of personalized language models. Stakeholder values and preferences about disability, privacy, identity and relationships should drive design, innovation and implementation.IMPLICATIONS FOR REHABILITATIONIndividuals with neurodegenerative diseases are important stakeholders to consider in development of natural language processing within brain-computer interface (BCI) communication technologies.The incorporation of personalized language models raises issues related to disability, identity, relationships, and privacy.People who may one day rely on BCI communication technologies care not just about usability of communication technology but about technology that supports their values and priorities.Qualitative ethics-focused research is a valuable tool for exploring stakeholder perspectives on new capabilities of BCI communication technologies, such as the storage and use of personalized language models.

RevDate: 2022-12-06

Xi C, Li A, Lai J, et al (2022)

Brain-gut microbiota multimodal predictive model in patients with bipolar depression.

Journal of affective disorders, 323:140-152 pii:S0165-0327(22)01273-3 [Epub ahead of print].

BACKGROUND: The "microbiota-gut-brain axis" which bridges the brain and gut microbiota is involved in the pathological mechanisms of bipolar disorder (BD), but rare is known about the exact association patterns and the potential for clinical diagnosis and treatment outcome prediction.

METHODS: At baseline, fecal samples and resting-state MRI data were collected from 103 BD depression patients and 39 healthy controls (HCs) for metagenomic sequencing and network-based functional connectivity (FC), grey matter volume (GMV) analyses. All patients then received 4-weeks quetiapine treatment and were further classified as responders and non-responders. Based on pre-treatment datasets, the correlation networks were established between gut microbiota and neuroimaging measures and the multimodal kernal combination support vector machine (SVM) classifiers were constructed to distinguish BD patients from HCs, and quetiapine responders from non-responders.

RESULTS: The multi-modal pre-treatment characteristics of quetiapine responders, were closer to the HCs compared to non-responders. And the correlation network analyses found the substantial correlations existed in HC between the Anaerotruncus_ unclassified,Porphyromonas_asaccharolytica,Actinomyces_graevenitzii et al. and the functional connectomes involved default mode network (DMN),somatomotor (SM), visual, limbic and basal ganglia networks were disrupted in BD. Moreover, in terms of the multimodal classifier, it reached optimized area under curve (AUC-ROC) at 0.9517 when classified BD from HC, and also acquired 0.8292 discriminating quetiapine responders from non-responders, which consistently better than even using the best unique modality.

LIMITATIONS: Lack post-treatment and external validation datasets; size of HCs is modest.

CONCLUSIONS: Multi-modalities of combining pre-treatment gut microbiota with neuroimaging endophenotypes might be a superior approach for accurate diagnosis and quetiapine efficacy prediction in BD.

RevDate: 2022-11-18

Bhuvaneshwari M, Grace Mary Kanaga E, ST George (2022)

Classification of SSVEP-EEG signals using CNN and Red Fox Optimization for BCI applications.

Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine [Epub ahead of print].

Classification of electroencephalography (EEG) signals associated with Steady-state visually evoked potential (SSVEP) is prominent because of its potential in restoring the communication and controlling capability of paralytic people. However, SSVEP signals classification is a challenging task for researchers because of its low signal-to-noise ratio, non-stationary and high dimensional properties. A proficient technique has to be evolved to classify the SSVEP-based EEG data. In recent times, convolutional neural network (CNN) has reached a quantum leap in EEG signal classification. Therefore, the proposed system employs CNN to classify the SSVEP-based EEG signals. Though CNN has proved its proficiency in handling EEG signal classification problems, the calibration of hyperparameters is required to enhance the performance of the model. The calibration of a hyperparameter is a time-consuming task, hence proposed an automated hyperparameter optimization technique using the Red Fox Optimization Algorithm (RFO). The effectiveness of the algorithm is evaluated by comparing it with the performance of Harris Hawk Optimization (HHO), Flower Pollination Algorithm (FPA), Grey Wolf Optimization Algorithm (GWO) and Whale Optimization Algorithm (WOA) based hyperparameter optimized CNN applied to the SSVEP based EEG signals multiclass dataset. The experimental results infer that the proposed algorithm can achieve a testing accuracy of 88.91% which is higher than other comparative algorithms like HHO, FPA, GWO and WOA. The above-mentioned values clearly show that the proposed algorithm achieved competitive performance when compared to the other reported algorithm.

RevDate: 2022-11-22
CmpDate: 2022-11-21

Rainey S, Dague KO, R Crisp (2022)

Brain-State Transitions, Responsibility, and Personal Identity.

Cambridge quarterly of healthcare ethics : CQ : the international journal of healthcare ethics committees, 31(4):453-463.

This article examines the emerging possibility of "brain-state transitioning," in which one brain state is prompted through manipulating the dynamics of the active brain. The technique, still in its infancy, is intended to provide the basis for novel treatments for brain-based disorders. Although a detailed literature exists covering topics around brain-machine interfaces, where targets of brain-based activity include artificial limbs, hardware, and software, there is less concentration on the brain itself as a target for instrumental intervention. This article examines some of the science behind brain-state transitioning, before extending beyond current possibilities in order to explore philosophical and ethical questions about how transitions could be seen to impact on assessment of responsibility and personal identity. It concludes with some thoughts on how best to pursue this nascent approach while accounting for the philosophical and ethical issues.

RevDate: 2022-11-18

Zhang J, Wang L, Xue Y, et al (2022)

Engineering Electrodes with Robust Conducting Hydrogel Coating for Neural Recording and Modulation.

Advanced materials (Deerfield Beach, Fla.) [Epub ahead of print].

Coating conventional metallic electrodes with conducting polymers has enabled the essential characteristics required for bioelectronics, such as biocompatibility, electrical conductivity, mechanical compliance and the capacity for structural and chemical functionalization of the bioelectrodes. However, the fragile interface between the conducting polymer and the electrode in wet physiological environment has greatly limited their utility and reliability. Here, we established a general yet reliable strategy to seamlessly interface conventional electrodes with conducting hydrogel coatings, featuring tissue-like modulus, highly-desirable electrochemical properties, robust interface and long-term reliability. Numerical modelling reveals the role of toughening mechanism, synergy of covalent anchorage of long-chain polymers and chemical crosslinking, in improving the long-term robustness of the interface. Through in vivo implantation in freely-moving mouse models, we show that stable electrophysiological recording could be achieved, while the conducting hydrogel/electrode interface remained robust during the long-term low-voltage electrical stimulation. This simple yet versatile design strategy addresses the long-standing technical challenges in functional bioelectrode engineering, and opens up new avenues for the next-generation diagnostic brain-machine interfaces. This article is protected by copyright. All rights reserved.

RevDate: 2022-11-17

Faes A, Camarrone F, MMV Hulle (2022)

Single Finger Trajectory Prediction From Intracranial Brain Activity Using Block-Term Tensor Regression With Fast and Automatic Component Extraction.

IEEE transactions on neural networks and learning systems, PP: [Epub ahead of print].

Multiway-or tensor-based decoding techniques for brain-computer interfaces (BCIs) are believed to better account for the multilinear structure of brain signals than conventional vector-or matrix-based ones. However, despite their outlook on significant performance gains, the used parameter optimization approach is often too computationally demanding so that conventional techniques are still preferred. We propose two novel tensor factorizations which we integrate into our block-term tensor regression (BTTR) algorithm and further introduce a marginalization procedure that guarantees robust predictions while reducing the risk of overfitting (generalized regression). BTTR accounts for the underlying (hidden) data structure in a fully automatic and computationally efficient manner, leading to a significant performance gain over conventional vector-or matrix-based techniques in a challenging real-world application. As a challenging real-world application, we apply BTTR to accurately predict single finger movement trajectories from intracranial recordings in human subjects. We compare the obtained performance with that of the state-of-the-art.

RevDate: 2022-11-19

Insausti-Delgado A, López-Larraz E, Nishimura Y, et al (2022)

Non-invasive brain-spine interface: Continuous control of trans-spinal magnetic stimulation using EEG.

Frontiers in bioengineering and biotechnology, 10:975037.

Brain-controlled neuromodulation has emerged as a promising tool to promote functional recovery in patients with motor disorders. Brain-machine interfaces exploit this neuromodulatory strategy and could be used for restoring voluntary control of lower limbs. In this work, we propose a non-invasive brain-spine interface (BSI) that processes electroencephalographic (EEG) activity to volitionally control trans-spinal magnetic stimulation (ts-MS), as an approach for lower-limb neurorehabilitation. This novel platform allows to contingently connect motor cortical activation during leg motor imagery with the activation of leg muscles via ts-MS. We tested this closed-loop system in 10 healthy participants using different stimulation conditions. This BSI efficiently removed stimulation artifacts from EEG regardless of ts-MS intensity used, allowing continuous monitoring of cortical activity and real-time closed-loop control of ts-MS. Our BSI induced afferent and efferent evoked responses, being this activation ts-MS intensity-dependent. We demonstrated the feasibility, safety and usability of this non-invasive BSI. The presented system represents a novel non-invasive means of brain-controlled neuromodulation and opens the door towards its integration as a therapeutic tool for lower-limb rehabilitation.

RevDate: 2022-11-18

Li P, Su J, Belkacem AN, et al (2022)

Corrigendum: Multi-person feature fusion transfer learning-based convolutional neural network for SSVEP-based collaborative BCI.

Frontiers in neuroscience, 16:1024150.

[This corrects the article DOI: 10.3389/fnins.2022.971039.].

RevDate: 2022-11-19

Hou X, Zhao J, H Zhang (2022)

Reconstruction of perceived face images from brain activities based on multi-attribute constraints.

Frontiers in neuroscience, 16:1015752.

Reconstruction of perceived faces from brain signals is a hot topic in brain decoding and an important application in the field of brain-computer interfaces. Existing methods do not fully consider the multiple facial attributes represented in face images, and their different activity patterns at multiple brain regions are often ignored, which causes the reconstruction performance very poor. In the current study, we propose an algorithmic framework that efficiently combines multiple face-selective brain regions for precise multi-attribute perceived face reconstruction. Our framework consists of three modules: a multi-task deep learning network (MTDLN), which is developed to simultaneously extract the multi-dimensional face features attributed to facial expression, identity and gender from one single face image, a set of linear regressions (LR), which is built to map the relationship between the multi-dimensional face features and the brain signals from multiple brain regions, and a multi-conditional generative adversarial network (mcGAN), which is used to generate the perceived face images constrained by the predicted multi-dimensional face features. We conduct extensive fMRI experiments to evaluate the reconstruction performance of our framework both subjectively and objectively. The results show that, compared with the traditional methods, our proposed framework better characterizes the multi-attribute face features in a face image, better predicts the face features from brain signals, and achieves better reconstruction performance of both seen and unseen face images in both visual effects and quantitative assessment. Moreover, besides the state-of-the-art intra-subject reconstruction performance, our proposed framework can also realize inter-subject face reconstruction to a certain extent.

RevDate: 2022-11-19

Keogh C, JJ FitzGerald (2022)

Decomposition into dynamic features reveals a conserved temporal structure in hand kinematics.

iScience, 25(11):105428.

The human hand is a unique and highly complex effector. The ability to describe hand kinematics with a small number of features suggests that complex hand movements are composed of combinations of simpler movements. This would greatly simplify the neural control of hand movements. If such movement primitives exist, a dimensionality reduction approach designed to exploit these features should outperform existing methods. We developed a deep neural network to capture the temporal dynamics of movements and demonstrate that the features learned allow accurate representation of functional hand movements using lower-dimensional representations than previously reported. We show that these temporal features are highly conserved across individuals and can interpolate previously unseen movements, indicating that they capture the intrinsic structure of hand movements. These results indicate that functional hand movements are defined by a low-dimensional basis set of movement primitives with important temporal dynamics and that these features are common across individuals.

RevDate: 2022-11-21
CmpDate: 2022-11-21

Bak S, Yeu M, J Jeong (2022)

Forecasting Unplanned Purchase Behavior under Buy-One Get-One-Free Promotions Using Functional Near-Infrared Spectroscopy.

Computational intelligence and neuroscience, 2022:1034983.

It is very important for consumers to recognize their wrong shopping habits such as unplanned purchase behavior (UPB). The traditional methods used for measuring the UPB in qualitative and quantitative studies have some drawbacks because of human perception and memory. We proposed a UPB identification methodology applied with the brain-computer interface technique using a support vector machine (SVM) along with a functional near-infrared spectroscopy (fNIRS). Hemodynamic signals and behavioral data were collected from 33 subjects by performing Task 1 which included the Buy-One-Get-One-Free (BOGOF) and Task 2 which excluded the BOGOF condition. The acquired data were calculated with 6 time-domain features and then classified them using SVM with 10-cross validations. Thereafter, we evaluated whether the results were reliable using the area under the receiver operating characteristic curve (AUC). As a result, we achieved average accuracy greater than 94%, which is reliable because of the AUC values above 0.97. We found that the UPB brain activity was more relevant to Task 1 with the BOGOF condition than with Task 2 in the prefrontal cortex. UPBs were sufficiently derived from self-reported measurement, indicating that the subjects perceived increased impulsivity in the BOGOF condition. Therefore, this study improves the detection and understanding of UPB as a path for a computer-aided detection perspective for rating the severity of UPBs.

RevDate: 2022-11-18

Cui X, Wu Y, Wu J, et al (2022)

A review: Music-emotion recognition and analysis based on EEG signals.

Frontiers in neuroinformatics, 16:997282.

Music plays an essential role in human life and can act as an expression to evoke human emotions. The diversity of music makes the listener's experience of music appear diverse. Different music can induce various emotions, and the same theme can also generate other feelings related to the listener's current psychological state. Music emotion recognition (MER) has recently attracted widespread attention in academics and industry. With the development of brain science, MER has been widely used in different fields, e.g., recommendation systems, automatic music composing, psychotherapy, and music visualization. Especially with the rapid development of artificial intelligence, deep learning-based music emotion recognition is gradually becoming mainstream. Besides, electroencephalography (EEG) enables external devices to sense neurophysiological signals in the brain without surgery. This non-invasive brain-computer signal has been used to explore emotions. This paper surveys EEG music emotional analysis, involving the analysis process focused on the music emotion analysis method, e.g., data processing, emotion model, and feature extraction. Then, challenging problems and development trends of EEG-based music emotion recognition is proposed. Finally, the whole paper is summarized.

RevDate: 2022-11-29

Lee SH, Thunemann M, Lee K, et al (2022)

Scalable Thousand Channel Penetrating Microneedle Arrays on Flex for Multimodal and Large Area Coverage BrainMachine Interfaces.

Advanced functional materials, 32(25):.

The Utah array powers cutting-edge projects for restoration of neurological function, such as BrainGate, but the underlying electrode technology has itself advanced little in the last three decades. Here, advanced dual-side lithographic microfabrication processes is exploited to demonstrate a 1024-channel penetrating silicon microneedle array (SiMNA) that is scalable in its recording capabilities and cortical coverage and is suitable for clinical translation. The SiMNA is the first penetrating microneedle array with a flexible backing that affords compliancy to brain movements. In addition, the SiMNA is optically transparent permitting simultaneous optical and electrophysiological interrogation of neuronal activity. The SiMNA is used to demonstrate reliable recordings of spontaneous and evoked field potentials and of single unit activity in chronically implanted mice for up to 196 days in response to optogenetic and to whisker air-puff stimuli. Significantly, the 1024-channel SiMNA establishes detailed spatiotemporal mapping of broadband brain activity in rats. This novel scalable and biocompatible SiMNA with its multimodal capability and sensitivity to broadband brain activity will accelerate the progress in fundamental neurophysiological investigations and establishes a new milestone for penetrating and large area coverage microelectrode arrays for brain-machine interfaces.

RevDate: 2022-12-06
CmpDate: 2022-12-05

Heusser MR, Bourrelly C, NJ Gandhi (2022)

Decoding the Time Course of Spatial Information from Spiking and Local Field Potential Activities in the Superior Colliculus.

eNeuro, 9(6):.

Place code representation is ubiquitous in circuits that encode spatial parameters. For visually guided eye movements, neurons in many brain regions emit spikes when a stimulus is presented in their receptive fields and/or when a movement is directed into their movement fields. Crucially, individual neurons respond for a broad range of directions or eccentricities away from the optimal vector, making it difficult to decode the stimulus location or the saccade vector from each cell's activity. We investigated whether it is possible to decode the spatial parameter with a population-level analysis, even when the optimal vectors are similar across neurons. Spiking activity and local field potentials (LFPs) in the superior colliculus (SC) were recorded with a laminar probe as monkeys performed a delayed saccade task to one of eight targets radially equidistant in direction. A classifier was applied offline to decode the spatial configuration as the trial progresses from sensation to action. For spiking activity, decoding performance across all eight directions was highest during the visual and motor epochs and lower but well above chance during the delay period. Classification performance followed a similar pattern for LFP activity too, except the performance during the delay period was limited mostly to the preferred direction. Increasing the number of neurons in the population consistently increased classifier performance for both modalities. Overall, this study demonstrates the power of population activity for decoding spatial information not possible from individual neurons.

RevDate: 2022-11-23

Jia Y, Xu S, Han G, et al (2022)

Transmembrane water-efflux rate measured by magnetic resonance imaging as a biomarker of the expression of aquaporin-4 in gliomas.

Nature biomedical engineering [Epub ahead of print].

The water-selective channel protein aquaporin-4 (AQP4) contributes to the migration and proliferation of gliomas, and to their resistance to therapy. Here we show, in glioma cell cultures, in subcutaneous and orthotopic gliomas in rats, and in glioma tumours in patients, that transmembrane water-efflux rate is a sensitive biomarker of AQP4 expression and can be measured via conventional dynamic-contrast-enhanced magnetic resonance imaging. Water-efflux rates correlated with stages of glioma proliferation as well as with changes in the heterogeneity of intra-tumoural and inter-tumoural AQP4 in rodent and human gliomas following treatment with temozolomide and with the AQP4 inhibitor TGN020. Regions with low water-efflux rates contained higher fractions of stem-like slow-cycling cells and therapy-resistant cells, suggesting that maps of water-efflux rates could be used to identify gliomas that are resistant to therapies.

RevDate: 2022-12-07
CmpDate: 2022-11-30

Iwama S, Zhang Y, J Ushiba (2022)

De Novo Brain-Computer Interfacing Deforms Manifold of Populational Neural Activity Patterns in Human Cerebral Cortex.

eNeuro, 9(6):.

Human brains are capable of modulating innate activities to adapt to novel environments and tasks; for sensorimotor neural system this means acquisition of a rich repertoire of activity patterns that improve behavioral performance. To directly map the process of acquiring the neural repertoire during tasks onto performance improvement, we analyzed net neural populational activity during the learning of its voluntary modulation by brain-computer interface (BCI) operation in female and male humans. The recorded whole-head high-density scalp electroencephalograms (EEGs) were subjected to dimensionality reduction algorithm to capture changes in cortical activity patterns represented by the synchronization of neuronal oscillations during adaptation. Although the preserved variance of targeted features in the reduced dimensions was 20%, we found systematic interactions between the activity patterns and BCI classifiers that detected motor attempt; the neural manifold derived in the embedded space was stretched along with motor-related features of EEG by model-based fixed classifiers but not with adaptive classifiers that were constantly recalibrated to user activity. Moreover, the manifold was deformed to be orthogonal to the boundary by de novo classifiers with a fixed decision boundary based on biologically unnatural features. Collectively, the flexibility of human cortical signaling patterns (i.e., neural plasticity) is only induced by operation of a BCI whose classifier required fixed activities, and the adaptation could be induced even the requirement is not consistent with biologically natural responses. These principles of neural adaptation at a macroscopic level may underlie the ability of humans to learn wide-ranging behavioral repertoires and adapt to novel environments.

RevDate: 2022-11-14

Lyu C, Yu C, Sun G, et al (2022)

Deconstruction of Vermal Cerebellum in Ramp Locomotion in Mice.

Advanced science (Weinheim, Baden-Wurttemberg, Germany) [Epub ahead of print].

The cerebellum is involved in encoding balance, posture, speed, and gravity during locomotion. However, most studies are carried out on flat surfaces, and little is known about cerebellar activity during free ambulation on slopes. Here, it has been imaged the neuronal activity of cerebellar molecular interneurons (MLIs) and Purkinje cells (PCs) using a miniaturized microscope while a mouse is walking on a slope. It has been found that the neuronal activity of vermal MLIs specifically enhanced during uphill and downhill locomotion. In addition, a subset of MLIs is activated during entire uphill or downhill positions on the slope and is modulated by the slope inclines. In contrast, PCs showed counter-balanced neuronal activity to MLIs, which reduced activity at the ramp peak. So, PCs may represent the ramp environment at the population level. In addition, chemogenetic inactivation of lobule V of the vermis impaired uphill locomotion. These results revealed a novel micro-circuit in the vermal cerebellum that regulates ambulatory behavior in 3D terrains.

RevDate: 2022-11-17
CmpDate: 2022-11-15

Willsey MS, Nason-Tomaszewski SR, Ensel SR, et al (2022)

Real-time brain-machine interface in non-human primates achieves high-velocity prosthetic finger movements using a shallow feedforward neural network decoder.

Nature communications, 13(1):6899.

Despite the rapid progress and interest in brain-machine interfaces that restore motor function, the performance of prosthetic fingers and limbs has yet to mimic native function. The algorithm that converts brain signals to a control signal for the prosthetic device is one of the limitations in achieving rapid and realistic finger movements. To achieve more realistic finger movements, we developed a shallow feed-forward neural network to decode real-time two-degree-of-freedom finger movements in two adult male rhesus macaques. Using a two-step training method, a recalibrated feedback intention-trained (ReFIT) neural network is introduced to further improve performance. In 7 days of testing across two animals, neural network decoders, with higher-velocity and more natural appearing finger movements, achieved a 36% increase in throughput over the ReFIT Kalman filter, which represents the current standard. The neural network decoders introduced herein demonstrate real-time decoding of continuous movements at a level superior to the current state-of-the-art and could provide a starting point to using neural networks for the development of more naturalistic brain-controlled prostheses.

RevDate: 2022-11-29
CmpDate: 2022-11-29

Rommel C, Paillard J, Moreau T, et al (2022)

Data augmentation for learning predictive models on EEG: a systematic comparison.

Journal of neural engineering, 19(6):.

Objective.The use of deep learning for electroencephalography (EEG) classification tasks has been rapidly growing in the last years, yet its application has been limited by the relatively small size of EEG datasets. Data augmentation, which consists in artificially increasing the size of the dataset during training, can be employed to alleviate this problem. While a few augmentation transformations for EEG data have been proposed in the literature, their positive impact on performance is often evaluated on a single dataset and compared to one or two competing augmentation methods. This work proposes to better validate the existing data augmentation approaches through a unified and exhaustive analysis.Approach.We compare quantitatively 13 different augmentations with two different predictive tasks, datasets and models, using three different types of experiments.Main results.We demonstrate that employing the adequate data augmentations can bring up to 45% accuracy improvements in low data regimes compared to the same model trained without any augmentation. Our experiments also show that there is no single best augmentation strategy, as the good augmentations differ on each task.Significance.Our results highlight the best data augmentations to consider for sleep stage classification and motor imagery brain-computer interfaces. More broadly, it demonstrates that EEG classification tasks benefit from adequate data augmentation.

RevDate: 2022-11-11

Wu L, Ren K, Chen G, et al (2022)

Hemostatic effect and safety evaluation of the absorbable macroporous polysaccharides composite hemostatic material prepared by a green fabrication approach.

Journal of biomaterials applications [Epub ahead of print].

Carboxymethyl chitosan is widely used in the medical field such as wound healing and other medical fields. We previously fabricated the absorbable macroporous polysaccharides composite hemostatics (AMPCs) mainly composed of carboxymethyl chitosan which possess excellent hemostatic effect. To further elucidate the impact of CMCTs on the hemostatic effect and biosafety of AMPCs, carboxymethyl chitosan with different properties were used to prepare AMPCs. By comparing the physical and chemical properties, AMPCs performed high water absorption ability, especially Group 1 (swelling ratio reached 5792%), which facilitated the rapid formation of blood clots. It was confirmed by blood clotting index (BCI) and blood coagulation tests in vitro that Group 1 showed a slightly higher coagulation capacity than groups 2 and 3, which may be due to the positive charge on the surface of the cations in the salts attaches to the negative charge on the surface of the red blood cells, an electrostatic neutralization reaction occurs. The biosafety was a preliminary evaluation by implanted AMPCs into the back of Sprague-Dawley rats and the tissue was harvested after feeding for 28 days. The AMPCs exhibited good biosafety for whole blood and major organs during the degradation in vivo: during the degradation of AMPCs, excluding changes in some serum indicators, no tissue necrosis or inflammatory cell infiltration was observed in these organs, either by gross observation or histological analysis. These findings demonstrate that expecting to develop a highly functional and safe hemostatic agent based on Group 1 for rapid hemostasis applications in emergencies.

RevDate: 2022-11-17
CmpDate: 2022-11-14

Hu H, Pu Z, Li H, et al (2022)

Learning Optimal Time-Frequency-Spatial Features by the CiSSA-CSP Method for Motor Imagery EEG Classification.

Sensors (Basel, Switzerland), 22(21):.

The common spatial pattern (CSP) is a popular method in feature extraction for motor imagery (MI) electroencephalogram (EEG) classification in brain-computer interface (BCI) systems. However, combining temporal and spectral information in the CSP-based spatial features is still a challenging issue, which greatly affects the performance of MI-based BCI systems. Here, we propose a novel circulant singular spectrum analysis embedded CSP (CiSSA-CSP) method for learning the optimal time-frequency-spatial features to improve the MI classification accuracy. Specifically, raw EEG data are first segmented into multiple time segments and spectrum-specific sub-bands are further derived by CiSSA from each time segment in a set of non-overlapping filter bands. CSP features extracted from all time-frequency segments contain more sufficient time-frequency-spatial information. An experimental study was implemented on the publicly available EEG dataset (BCI Competition III dataset IVa) and a self-collected experimental EEG dataset to validate the effectiveness of the CiSSA-CSP method. Experimental results demonstrate that discriminative and robust features are extracted effectively. Compared with several state-of-the-art methods, the proposed method exhibited optimal accuracies of 96.6% and 95.2% on the public and experimental datasets, respectively, which confirms that it is a promising method for improving the performance of MI-based BCIs.

RevDate: 2022-11-17
CmpDate: 2022-11-14

Chuang CC, Lee CC, So EC, et al (2022)

Multi-Task Learning-Based Deep Neural Network for Steady-State Visual Evoked Potential-Based Brain-Computer Interfaces.

Sensors (Basel, Switzerland), 22(21):.

Amyotrophic lateral sclerosis (ALS) causes people to have difficulty communicating with others or devices. In this paper, multi-task learning with denoising and classification tasks is used to develop a robust steady-state visual evoked potential-based brain-computer interface (SSVEP-based BCI), which can help people communicate with others. To ease the operation of the input interface, a single channel-based SSVEP-based BCI is selected. To increase the practicality of SSVEP-based BCI, multi-task learning is adopted to develop the neural network-based intelligent system, which can suppress the noise components and obtain a high level of accuracy of classification. Thus, denoising and classification tasks are selected in multi-task learning. The experimental results show that the proposed multi-task learning can effectively integrate the advantages of denoising and discriminative characteristics and outperform other approaches. Therefore, multi-task learning with denoising and classification tasks is very suitable for developing an SSVEP-based BCI for practical applications. In the future, an augmentative and alternative communication interface can be implemented and examined for helping people with ALS communicate with others in their daily lives.

RevDate: 2022-11-17
CmpDate: 2022-11-14

Emsawas T, Morita T, Kimura T, et al (2022)

Multi-Kernel Temporal and Spatial Convolution for EEG-Based Emotion Classification.

Sensors (Basel, Switzerland), 22(21):.

Deep learning using an end-to-end convolutional neural network (ConvNet) has been applied to several electroencephalography (EEG)-based brain-computer interface tasks to extract feature maps and classify the target output. However, the EEG analysis remains challenging since it requires consideration of various architectural design components that influence the representational ability of extracted features. This study proposes an EEG-based emotion classification model called the multi-kernel temporal and spatial convolution network (MultiT-S ConvNet). The multi-scale kernel is used in the model to learn various time resolutions, and separable convolutions are applied to find related spatial patterns. In addition, we enhanced both the temporal and spatial filters with a lightweight gating mechanism. To validate the performance and classification accuracy of MultiT-S ConvNet, we conduct subject-dependent and subject-independent experiments on EEG-based emotion datasets: DEAP and SEED. Compared with existing methods, MultiT-S ConvNet outperforms with higher accuracy results and a few trainable parameters. Moreover, the proposed multi-scale module in temporal filtering enables extracting a wide range of EEG representations, covering short- to long-wavelength components. This module could be further implemented in any model of EEG-based convolution networks, and its ability potentially improves the model's learning capacity.

RevDate: 2022-11-17
CmpDate: 2022-11-14

Khare SK, Gaikwad N, ND Bokde (2022)

An Intelligent Motor Imagery Detection System Using Electroencephalography with Adaptive Wavelets.

Sensors (Basel, Switzerland), 22(21):.

Classification of motor imagery (MI) tasks provides a robust solution for specially-abled people to connect with the milieu for brain-computer interface. Precise selection of uniform tuning parameters of tunable Q wavelet transform (TQWT) for electroencephalography (EEG) signals is arduous. Therefore, this paper proposes robust TQWT for automatically selecting optimum tuning parameters to decompose non-stationary EEG signals accurately. Three evolutionary optimization algorithms are explored for automating the tuning parameters of robust TQWT. The fitness function of the mean square error of decomposition is used. This paper also exploits channel selection using a Laplacian score for dominant channel selection. Important features elicited from sub-bands of robust TQWT are classified using different kernels of the least square support vector machine classifier. The radial basis function kernel has provided the highest accuracy of 99.78%, proving that the proposed method is superior to other state-of-the-art using the same database.

RevDate: 2022-11-17

Frenzel J, Kupferer A, Zink M, et al (2022)

Laminin Adsorption and Adhesion of Neurons and Glial Cells on Carbon Implanted Titania Nanotube Scaffolds for Neural Implant Applications.

Nanomaterials (Basel, Switzerland), 12(21):.

Interfacing neurons persistently to conductive matter constitutes one of the key challenges when designing brain-machine interfaces such as neuroelectrodes or retinal implants. Novel materials approaches that prevent occurrence of loss of long-term adhesion, rejection reactions, and glial scarring are highly desirable. Ion doped titania nanotube scaffolds are a promising material to fulfill all these requirements while revealing sufficient electrical conductivity, and are scrutinized in the present study regarding their neuron-material interface. Adsorption of laminin, an essential extracellular matrix protein of the brain, is comprehensively analyzed. The implantation-dependent decline in laminin adsorption is revealed by employing surface characteristics such as nanotube diameter, ζ-potential, and surface free energy. Moreover, the viability of U87-MG glial cells and SH-SY5Y neurons after one and four days are investigated, as well as the material's cytotoxicity. The higher conductivity related to carbon implantation does not affect the viability of neurons, although it impedes glial cell proliferation. This gives rise to novel titania nanotube based implant materials with long-term stability, and could reduce undesirable glial scarring.

RevDate: 2022-11-26

Wang K, Tian F, Xu M, et al (2022)

Resting-State EEG in Alpha Rhythm May Be Indicative of the Performance of Motor Imagery-Based Brain-Computer Interface.

Entropy (Basel, Switzerland), 24(11):.

Motor imagery-based brain-computer interfaces (MI-BCIs) have great application prospects in motor enhancement and rehabilitation. However, the capacity to control a MI-BCI varies among persons. Predicting the MI ability of a user remains challenging in BCI studies. We first calculated the relative power level (RPL), power spectral entropy (PSE) and Lempel-Ziv complexity (LZC) of the resting-state open and closed-eye EEG of different frequency bands and investigated their correlations with the upper and lower limbs MI performance (left hand, right hand, both hands and feet MI tasks) on as many as 105 subjects. Then, the most significant related features were used to construct a classifier to separate the high MI performance group from the low MI performance group. The results showed that the features of open-eye resting alpha-band EEG had the strongest significant correlations with MI performance. The PSE performed the best among all features for the screening of the MI performance, with the classification accuracy of 85.24%. These findings demonstrated that the alpha bands might offer information related to the user's MI ability, which could be used to explore more effective and general neural markers to screen subjects and design individual MI training strategies.

RevDate: 2022-11-26

Syrov N, Yakovlev L, Nikolaeva V, et al (2022)

Mental Strategies in a P300-BCI: Visuomotor Transformation Is an Option.

Diagnostics (Basel, Switzerland), 12(11):.

Currently, P300-BCIs are mostly used for spelling tasks, where the number of commands is equal to the number of stimuli that evoke event-related potentials (ERPs). Increasing this number slows down the BCI operation because each stimulus has to be presented several times for better classification. Furthermore, P300 spellers typically do not utilize potentially useful imagery-based approaches, such as the motor imagery successfully practiced in motor rehabilitation. Here, we tested a P300-BCI with a motor-imagery component. In this BCI, the number of commands was increased by adding mental strategies instead of increasing the number of targets. Our BCI had only two stimuli and four commands. The subjects either counted target appearances mentally or imagined hand movements toward the targets. In this design, the motor-imagery paradigm enacted a visuomotor transformation known to engage cortical and subcortical networks participating in motor control. The operation of these networks suffers in neurological conditions such as stroke, so we view this BCI as a potential tool for the rehabilitation of patients. As an initial step toward the development of this clinical method, sixteen healthy participants were tested. Consistent with our expectation that mental strategies would result in distinct EEG activities, ERPs were different depending on whether subjects counted stimuli or imagined movements. These differences were especially clear in the late ERP components localized in the frontal and centro-parietal regions. We conclude that (1) the P300 paradigm is suitable for enacting visuomotor transformations and (2) P300-based BCIs with multiple mental strategies could be used in applications where the number of possible outputs needs to be increased while keeping the number of targets constant. As such, our approach adds to both the development of versatile BCIs and clinical approaches to rehabilitation.

RevDate: 2022-11-26

Adhia DB, Mani R, Turner PR, et al (2022)

Infraslow Neurofeedback Training Alters Effective Connectivity in Individuals with Chronic Low Back Pain: A Secondary Analysis of a Pilot Randomized Placebo-Controlled Study.

Brain sciences, 12(11):.

This study explored the effect of electroencephalographic infraslow neurofeedback (EEG ISF-NF) training on effective connectivity and tested whether such effective connectivity changes are correlated with changes in pain and disability in people with chronic low back pain. This involved secondary analysis of a pilot double-blinded randomised placebo-controlled study. Participants (n = 60) were randomised to receive ISF-NF targeting either the pregenual anterior cingulate cortex (pgACC), dorsal anterior cingulate and somatosensory cortex (dACC + S1), ratio of pgACC*2/dACC + S1, or Sham-NF. Resting-state EEG and clinical outcomes were assessed at baseline, immediately after intervention, and at one-week and one-month follow-up. Kruskal-Wallis tests demonstrated significant between-group differences in effective connectivity from pgACC to S1L at one-month follow up and marginal significant changes from S1L to pgACC at one-week and one-month follow up. Mann-Whitney U tests demonstrated significant increases in effective connectivity in the ISF-NF up-training pgACC group when compared to the Sham-NF group (pgACC to S1L at one-month (p = 0.013), and S1L to pgACC at one-week (p = 0.008) and one-month follow up (p = 0.016)). Correlational analyses demonstrated a significant negative correlation (ρ = -0.630, p = 0.038) between effective connectivity changes from pgACC to S1L and changes in pain severity at one-month follow-up. The ISF-NF training pgACC can reduce pain via influencing effective connectivity between pgACC and S1L.

RevDate: 2022-11-26

Cao L, Wu H, Chen S, et al (2022)

A Novel Deep Learning Method Based on an Overlapping Time Window Strategy for Brain-Computer Interface-Based Stroke Rehabilitation.

Brain sciences, 12(11):.

Globally, stroke is a leading cause of death and disability. The classification of motor intentions using brain activity is an important task in the rehabilitation of stroke patients using brain-computer interfaces (BCIs). This paper presents a new method for model training in EEG-based BCI rehabilitation by using overlapping time windows. For this aim, three different models, a convolutional neural network (CNN), graph isomorphism network (GIN), and long short-term memory (LSTM), are used for performing the classification task of motor attempt (MA). We conducted several experiments with different time window lengths, and the results showed that the deep learning approach based on overlapping time windows achieved improvements in classification accuracy, with the LSTM combined vote-counting strategy (VS) having achieved the highest average classification accuracy of 90.3% when the window size was 70. The results verified that the overlapping time window strategy is useful for increasing the efficiency of BCI rehabilitation.

RevDate: 2022-12-06
CmpDate: 2022-12-06

Wan Z, Yang R, Huang M, et al (2022)

Segment alignment based cross-subject motor imagery classification under fading data.

Computers in biology and medicine, 151(Pt A):106267.

Motor imagery (MI) aims to use brain imagination without actual body activities to support motor learning, and machine learning algorithms such as common spatial patterns (CSP) are proven effective in the analysis of MI signals. In the conventional machine learning-based approaches, there are two main difficulties in feature extraction and recognition of MI signals: high personalization and data fading. The high personalization problem is due to the multi-subject nature when collecting MI signals, and the data fading problem as a recurring issue in MI signal quality is first raised by us but is not widely discussed at present. Aiming to solve the above two mentioned problems, a cross-subject fading data classification approach with segment alignment is proposed to classify the fading data of one single target with the model trained with the normal data of multiple sources in this paper. he effectiveness of proposed method is verified via two experiments: a dataset-based experiment with the dataset from BCI Competition and a lab-based experiment designed and conducted by us. The experimental results obtained from both experiments show that the proposed method can obtain optimal classification performance effectively under different fading levels with data from different subjects.

RevDate: 2022-11-25
CmpDate: 2022-11-25

Petrosyan A, Voskoboinikov A, Sukhinin D, et al (2022)

Speech decoding from a small set of spatially segregated minimally invasive intracranial EEG electrodes with a compact and interpretable neural network.

Journal of neural engineering, 19(6):.

Objective. Speech decoding, one of the most intriguing brain-computer interface applications, opens up plentiful opportunities from rehabilitation of patients to direct and seamless communication between human species. Typical solutions rely on invasive recordings with a large number of distributed electrodes implanted through craniotomy. Here we explored the possibility of creating speech prosthesis in a minimally invasive setting with a small number of spatially segregated intracranial electrodes.Approach. We collected one hour of data (from two sessions) in two patients implanted with invasive electrodes. We then used only the contacts that pertained to a single stereotactic electroencephalographic (sEEG) shaft or an electrocorticographic (ECoG) stripe to decode neural activity into 26 words and one silence class. We employed a compact convolutional network-based architecture whose spatial and temporal filter weights allow for a physiologically plausible interpretation.Mainresults. We achieved on average 55% accuracy using only six channels of data recorded with a single minimally invasive sEEG electrode in the first patient and 70% accuracy using only eight channels of data recorded for a single ECoG strip in the second patient in classifying 26+1 overtly pronounced words. Our compact architecture did not require the use of pre-engineered features, learned fast and resulted in a stable, interpretable and physiologically meaningful decision rule successfully operating over a contiguous dataset collected during a different time interval than that used for training. Spatial characteristics of the pivotal neuronal populations corroborate with active and passive speech mapping results and exhibit the inverse space-frequency relationship characteristic of neural activity. Compared to other architectures our compact solution performed on par or better than those recently featured in neural speech decoding literature.Significance. We showcase the possibility of building a speech prosthesis with a small number of electrodes and based on a compact feature engineering free decoder derived from a small amount of training data.

RevDate: 2022-11-11

Chen R, Xu G, Jia Y, et al (2022)

Enhancement of time-frequency energy for the classification of motor imagery electroencephalogram based on an improved FitzHugh-Nagumo neuron system.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society, PP: [Epub ahead of print].

Brain-computer interface (BCI) based on motor imagery (MI) electroencephalogram (EEG) has become an essential way for rehabilitation, because of the activation and interaction of motor neurons between the brain and rehabilitation devices in recent years. However, due to the discrepancies between individuals, the frequency ranges can be different even for the same rhythm component of EEG recordings, which brings difficulties to the extraction of features for MI classification. Typical algorithms for MI classification such as common spatial patterns (CSP) require multi-channel analysis and lack frequency information. With the development of BCI, the single-channel BCI system has become indispensable for simplicity of use. However, the currently available single-channel detection methods have low classification accuracy. To address this issue, two novel frameworks based on an improved two-dimensional nonlinear FitzHugh-Nagumo (FHN) neuron system are proposed to extract features of the single-channel MI. To evaluate the effectiveness of the proposed methods, this research utilized an open-access database (BCI competition IV dataset 2a), an offline database, and a 10-fold cross-validation procedure. Experimental results showed that the improved nonlinear FHN system can transfer the energy of noise into MI, thereby effectively enhancing the time-frequency energy. Compared with the traditional methods, the proposed methods can achieve higher classification accuracy and robustness.

RevDate: 2022-11-10

Johnson KA, Worbe Y, Foote KD, et al (2022)

Tourette syndrome: clinical features, pathophysiology, and treatment.

The Lancet. Neurology pii:S1474-4422(22)00303-9 [Epub ahead of print].

Tourette syndrome is a chronic neurodevelopmental disorder characterised by motor and phonic tics that can substantially diminish the quality of life of affected individuals. Evaluating and treating Tourette syndrome is complex, in part due to the heterogeneity of symptoms and comorbidities between individuals. The underlying pathophysiology of Tourette syndrome is not fully understood, but recent research in the past 5 years has brought new insights into the genetic variations and the alterations in neurophysiology and brain networks contributing to its pathogenesis. Treatment options for Tourette syndrome are expanding with novel pharmacological therapies and increased use of deep brain stimulation for patients with symptoms that are refractory to pharmacological or behavioural treatments. Potential predictors of patient responses to therapies for Tourette syndrome, such as specific networks modulated during deep brain stimulation, can guide clinical decisions. Multicentre data sharing initiatives have enabled several advances in our understanding of the genetics and pathophysiology of Tourette syndrome and will be crucial for future large-scale research and in refining effective treatments.

RevDate: 2022-12-04
CmpDate: 2022-11-11

Fischer L, Mojica Soto-Albors R, Tang VD, et al (2022)

Dendritic Mechanisms for In Vivo Neural Computations and Behavior.

The Journal of neuroscience : the official journal of the Society for Neuroscience, 42(45):8460-8467.

Dendrites receive the vast majority of a single neuron's inputs, and coordinate the transformation of these signals into neuronal output. Ex vivo and theoretical evidence has shown that dendrites possess powerful processing capabilities, yet little is known about how these mechanisms are engaged in the intact brain or how they influence circuit dynamics. New experimental and computational technologies have led to a surge in interest to unravel and harness their computational potential. This review highlights recent and emerging work that combines established and cutting-edge technologies to identify the role of dendrites in brain function. We discuss active dendritic mediation of sensory perception and learning in neocortical and hippocampal pyramidal neurons. Complementing these physiological findings, we present theoretical work that provides new insights into the underlying computations of single neurons and networks by using biologically plausible implementations of dendritic processes. Finally, we present a novel brain-computer interface task, which assays somatodendritic coupling to study the mechanisms of biological credit assignment. Together, these findings present exciting progress in understanding how dendrites are critical for in vivo learning and behavior, and highlight how subcellular processes can contribute to our understanding of both biological and artificial neural computation.

RevDate: 2022-12-02
CmpDate: 2022-11-11

McNamara CG, Rothwell M, A Sharott (2022)

Stable, interactive modulation of neuronal oscillations produced through brain-machine equilibrium.

Cell reports, 41(6):111616.

Closed-loop interaction has the potential to regulate ongoing brain activity by continuously binding an external stimulation to specific dynamics of a neural circuit. Achieving interactive modulation requires a stable brain-machine feedback loop. Here, we demonstrate that it is possible to maintain oscillatory brain activity in a desired state by delivering stimulation accurately aligned with the timing of each cycle. We develop a fast algorithm that responds on a cycle-by-cycle basis to stimulate basal ganglia nuclei at predetermined phases of successive cortical beta cycles in parkinsonian rats. Using this approach, an equilibrium emerges between the modified brain signal and feedback-dependent stimulation pattern, leading to sustained amplification or suppression of the oscillation depending on the phase targeted. Beta amplification slows movement speed by biasing the animal's mode of locomotion. Together, these findings show that highly responsive, phase-dependent stimulation can achieve a stable brain-machine interaction that leads to robust modulation of ongoing behavior.

RevDate: 2022-11-09

Zhang M, Wu J, Song J, et al (2022)

Decoding Coordinated Directions of Bimanual Movements from EEG Signals.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society, PP: [Epub ahead of print].

Bimanual coordination is common in human daily life, whereas current research focused mainly on decoding unimanual movement from electroencephalogram (EEG) signals. Here we developed a brain-computer interface (BCI) paradigm of task-oriented bimanual movements to decode coordinated directions from movement-related cortical potentials (MRCPs) of EEG. Eight healthy subjects participated in the target-reaching task, including (1) performing leftward, midward, and rightward bimanual movements, and (2) performing leftward and rightward unimanual movements. A combined deep learning model of convolution neural network and bidirectional long short-term memory network was proposed to classify movement directions from EEG. Results showed that the average peak classification accuracy for three coordinated directions of bimanual movements reached 73.39 ± 6.35%. The binary classification accuracies achieved 80.24 ± 6.25, 82.62 ± 7.82, and 86.28 ± 5.50% for leftward versus midward, rightward versus midward and leftward versus rightward, respectively. We also compared the binary classification (leftward versus rightward) of bimanual, left-hand, and right-hand movements, and accuracies achieved 86.28 ± 5.50%, 75.67 ± 7.18%, and 77.79 ± 5.65%, respectively. The results indicated the feasibility of decoding human coordinated directions of task-oriented bimanual movements from EEG.

RevDate: 2022-11-16
CmpDate: 2022-11-11

Sattler S, D Pietralla (2022)

Public attitudes towards neurotechnology: Findings from two experiments concerning Brain Stimulation Devices (BSDs) and Brain-Computer Interfaces (BCIs).

PloS one, 17(11):e0275454.

This study contributes to the emerging literature on public perceptions of neurotechnological devices (NTDs) in their medical and non-medical applications, depending on their invasiveness, framing effects, and interindividual differences related to personal needs and values. We conducted two web-based between-subject experiments (2×2×2) using a representative, nation-wide sample of the adult population in Germany. Using vignettes describing how two NTDs, brain stimulation devices (BSDs; NExperiment 1 = 1,090) and brain-computer interfaces (BCIs; NExperiment 2 = 1,089), function, we randomly varied the purpose (treatment vs. enhancement) and invasiveness (noninvasive vs. invasive) of the NTD, and assessed framing effects (variable order of assessing moral acceptability first vs. willingness to use first). We found a moderate moral acceptance and willingness to use BSDs and BCIs. Respondents preferred treatment over enhancement purposes and noninvasive over invasive devices. We also found a framing effect and explored the role of personal characteristics as indicators of personal needs and values (e.g., stress, religiosity, and gender). Our results suggest that the future demand for BSDs or BCIs may depend on the purpose, invasiveness, and personal needs and values. These insights can inform technology developers about the public's needs and concerns, and enrich legal and ethical debates.

RevDate: 2022-12-06

Zwerus EL, van Deurzen DFP, van den Bekerom MPJ, et al (2022)

Distal Biceps Tendon Ruptures: Diagnostic Strategy Through Physical Examination.

The American journal of sports medicine, 50(14):3956-3962.

BACKGROUND: Distinguishing a complete from a partial distal biceps tendon rupture is essential, as a complete rupture may require repair on short notice to restore function, whereas partial ruptures can be treated nonsurgically in most cases. Reliability of physical examination is crucial to determine the right workup and treatment in patients with a distal biceps tendon rupture.

PURPOSES: The primary aim of this study was to find a (combination of) test(s) that serves best to diagnose a complete rupture with certainty in the acute phase (≤1 month) without missing any complete ruptures. The secondary aims were to determine the best (combination of) test(s) to identify a chronic (>1 month) rupture of the distal biceps tendon and indicate additional imaging in case partial ruptures or tendinitis are suspected.

STUDY DESIGN: Cohort study (Diagnosis); Level of evidence, 2.

METHODS: A total of 86 patients with anterior elbow complaints or suspected distal biceps injury underwent standardized physical examination, including the Hook test, passive forearm pronation test, biceps crease interval (BCI), and biceps crease ratio. Diagnosis was confirmed intraoperatively (68 cases), by magnetic resonance imaging (13 cases), or by ultrasound (5 cases).

RESULTS: A combination of the Hook test and BCI (ie, both tests are positive) was most accurate for both acute and chronic ruptures but with a different purpose. For acute complete ruptures, sensitivity was 94% and specificity was 100%. In chronic cases, specificity was also 100%. Weakness on active supination and palpation of the tendon footprint provided excellent sensitivity of 100% for chronic complete ruptures and partial ruptures, respectively.

CONCLUSION: The combination of a positive Hook test and BCI serves best to accurately diagnose acute complete ruptures of the distal biceps tendon. Weakness on active supination and pain on palpation of the tendon footprint provide excellent sensitivity for chronic complete ruptures and partial ruptures. Using these tests in all suspected distal biceps ruptures allows a physician to refrain from imaging for a diagnostic purpose in certain cases, to limit treatment delay and thereby provide better treatment outcome, and to avoid hospital and social costs.

RevDate: 2022-12-05
CmpDate: 2022-12-05

Peterson V, Merk T, Bush A, et al (2023)

Movement decoding using spatio-spectral features of cortical and subcortical local field potentials.

Experimental neurology, 359:114261.

The first commercially sensing enabled deep brain stimulation (DBS) devices for the treatment of movement disorders have recently become available. In the future, such devices could leverage machine learning based brain signal decoding strategies to individualize and adapt therapy in real-time. As multi-channel recordings become available, spatial information may provide an additional advantage for informing machine learning models. To investigate this concept, we compared decoding performances from single channels vs. spatial filtering techniques using intracerebral multitarget electrophysiology in Parkinson's disease patients undergoing DBS implantation. We investigated the feasibility of spatial filtering in invasive neurophysiology and the putative utility of combined cortical ECoG and subthalamic local field potential signals for decoding grip-force, a well-defined and continuous motor readout. We found that adding spatial information to the model can improve decoding (6% gain in decoding), but the spatial patterns and additional benefit was highly individual. Beyond decoding performance results, spatial filters and patterns can be used to obtain meaningful neurophysiological information about the brain networks involved in target behavior. Our results highlight the importance of individualized approaches for brain signal decoding, for which multielectrode recordings and spatial filtering can improve precision medicine approaches for clinical brain computer interfaces.

RevDate: 2022-12-05
CmpDate: 2022-12-05

Khademi Z, Ebrahimi F, HM Kordy (2023)

A review of critical challenges in MI-BCI: From conventional to deep learning methods.

Journal of neuroscience methods, 383:109736.

Brain-computer interfaces (BCIs) have achieved significant success in controlling external devices through the Electroencephalogram (EEG) signal processing. BCI-based Motor Imagery (MI) system bridges brain and external devices as communication tools to control, for example, wheelchair for people with disabilities, robotic control, and exoskeleton control. This success largely depends on the machine learning (ML) approaches like deep learning (DL) models. DL algorithms provide effective and powerful models to analyze compact and complex EEG data optimally for MI-BCI applications. DL models with CNN network have revolutionized computer vision through end-to-end learning from raw data. Meanwhile, RNN networks have been able to decode EEG signals by processing sequences of time series data. However, many challenges in the MI-BCI field have affected the performance of DL models. A major challenge is the individual differences in the EEG signal of different subjects. Therefore, the model must be retrained from the scratch for each new subject, which leads to computational costs. Analyzing the EEG signals is challenging due to its low signal to noise ratio and non-stationary nature. Additionally, limited size of existence datasets can lead to overfitting which can be prevented by using transfer learning (TF) approaches. The main contributions of this study are discovering major challenges in the MI-BCI field by reviewing the state of art machine learning models and then suggesting solutions to address these challenges by focusing on feature selection, feature extraction and classification methods.

RevDate: 2022-11-09

Fu K, Du C, Wang S, et al (2022)

Multi-View Multi-Label Fine-Grained Emotion Decoding From Human Brain Activity.

IEEE transactions on neural networks and learning systems, PP: [Epub ahead of print].

Decoding emotional states from human brain activity play an important role in the brain-computer interfaces. Existing emotion decoding methods still have two main limitations: one is only decoding a single emotion category from a brain activity pattern and the decoded emotion categories are coarse-grained, which is inconsistent with the complex emotional expression of humans; the other is ignoring the discrepancy of emotion expression between the left and right hemispheres of the human brain. In this article, we propose a novel multi-view multi-label hybrid model for fine-grained emotion decoding (up to 80 emotion categories) which can learn the expressive neural representations and predict multiple emotional states simultaneously. Specifically, the generative component of our hybrid model is parameterized by a multi-view variational autoencoder, in which we regard the brain activity of left and right hemispheres and their difference as three distinct views and use the product of expert mechanism in its inference network. The discriminative component of our hybrid model is implemented by a multi-label classification network with an asymmetric focal loss. For more accurate emotion decoding, we first adopt a label-aware module for emotion-specific neural representation learning and then model the dependency of emotional states by a masked self-attention mechanism. Extensive experiments on two visually evoked emotional datasets show the superiority of our method.

RevDate: 2022-11-08

Sake SM, Kosch C, Blockus S, et al (2022)

Respiratory Syncytial Virus Two-Step Infection Screen Reveals Inhibitors of Early and Late Life Cycle Stages.

Antimicrobial agents and chemotherapy [Epub ahead of print].

Human respiratory syncytial virus (hRSV) infection is a leading cause of severe respiratory tract infections. Effective, directly acting antivirals against hRSV are not available. We aimed to discover new and chemically diverse candidates to enrich the hRSV drug development pipeline. We used a two-step screen that interrogates compound efficacy after primary infection and a consecutive virus passaging. We resynthesized selected hit molecules and profiled their activities with hRSV lentiviral pseudotype cell entry, replicon, and time-of-addition assays. The breadth of antiviral activity was tested against recent RSV clinical strains and human coronavirus (hCoV-229E), and in pseudotype-based entry assays with non-RSV viruses. Screening 6,048 molecules, we identified 23 primary candidates, of which 13 preferentially scored in the first and 10 in the second rounds of infection, respectively. Two of these molecules inhibited hRSV cell entry and selected for F protein resistance within the fusion peptide. One molecule inhibited transcription/replication in hRSV replicon assays, did not select for phenotypic hRSV resistance and was active against non-hRSV viruses, including hCoV-229E. One compound, identified in the second round of infection, did not measurably inhibit hRSV cell entry or replication/transcription. It selected for two coding mutations in the G protein and was highly active in differentiated BCi-NS1.1 lung cells. In conclusion, we identified four new hRSV inhibitor candidates with different modes of action. Our findings build an interesting platform for medicinal chemistry-guided derivatization approaches followed by deeper phenotypical characterization in vitro and in vivo with the aim of developing highly potent hRSV drugs.

RevDate: 2022-12-06
CmpDate: 2022-12-06

Pu X, Yi P, Chen K, et al (2022)

EEGDnet: Fusing non-local and local self-similarity for EEG signal denoising with transformer.

Computers in biology and medicine, 151(Pt A):106248.

Electroencephalogram (EEG) has shown a useful approach to produce a brain-computer interface (BCI). One-dimensional (1-D) EEG signal is yet easily disturbed by certain artifacts (a.k.a. noise) due to the high temporal resolution. Thus, it is crucial to remove the noise in received EEG signal. Recently, deep learning-based EEG signal denoising approaches have achieved impressive performance compared with traditional ones. It is well known that the characteristics of self-similarity (including non-local and local ones) of data (e.g., natural images and time-domain signals) are widely leveraged for denoising. However, existing deep learning-based EEG signal denoising methods ignore either the non-local self-similarity (e.g., 1-D convolutional neural network) or local one (e.g., fully connected network and recurrent neural network). To address this issue, we propose a novel 1-D EEG signal denoising network with 2-D transformer, namely EEGDnet. Specifically, we comprehensively take into account the non-local and local self-similarity of EEG signal through the transformer module. By fusing non-local self-similarity in self-attention blocks and local self-similarity in feed forward blocks, the negative impact caused by noises and outliers can be reduced significantly. Extensive experiments show that, compared with other state-of-the-art models, EEGDnet achieves much better performance in terms of both quantitative and qualitative metrics. Specifically, EEGDnet can achieve 18% and 11% improvements in correlation coefficients when removing ocular artifacts and muscle artifacts, respectively.

RevDate: 2022-11-14

Zhang D, Liu S, Zhang J, et al (2022)

Brain-Controlled 2D Navigation Robot Based on a Spatial Gradient Controller and Predictive Environmental Coordinator.

IEEE journal of biomedical and health informatics, PP: [Epub ahead of print].

OBJECTIVE: Brain-computer interfaces (BCIs) have been used in two-dimensional (2D) navigation robotic devices, such as brain-controlled wheelchairs and brain-controlled vehicles. However, contemporary BCI systems are driven by binary selective control. On the one hand, only directional information can be transferred from humans to machines, such as "turn left" or "turn right", which means that the quantified value, such as the radius of gyration, cannot be controlled. In this study, we proposed a spatial gradient BCI controller and corresponding environment coordinator, by which the quantified value of brain commands can be transferred in the form of a 2D vector, improving the flexibility, stability and efficiency of BCIs.

METHODS: A horizontal array of steady-state visual stimulation was arranged to excite subject (EEG) signals. Covariance arrays between subjects' electroencephalogram (EEG) and stimulation features were mapped into quantified 2-dimensional vectors. The generated vectors were then inputted into the predictive controller and fused with virtual forces generated by the robot's predictive environment coordinator in the form of vector calculation. The resultant vector was then interpreted into the driving force for the robot, and real-time speed feedback was generated.

RESULTS: The proposed SGC controller generated a faster (27.4 s vs. 34.9 s) response for the single-obstacle avoidance task than the selective control approach. In practical multiobstacle tasks, the proposed robot executed 39% faster in the target-reaching tasks than the selective controller and had better robustness in multiobstacle avoidance tasks (average failures significantly dropped from 27% to 4%).

SIGNIFICANCE: This research proposes a new form of brain-machine shared control strategy that quantifies brain commands in the form of a 2-D control vector stream rather than selective constant values. Combined with a predictive environment coordinator, the brain-controlled strategy of the robot is optimized and provided with higher flexibility. The proposed controller can be used in brain-controlled 2D navigation devices, such as brain-controlled wheelchairs and vehicles.

RevDate: 2022-11-08

Lee HS, Schreiner L, Jo SH, et al (2022)

Individual finger movement decoding using a novel ultra-high-density electroencephalography-based brain-computer interface system.

Frontiers in neuroscience, 16:1009878.

Brain-Computer Interface (BCI) technology enables users to operate external devices without physical movement. Electroencephalography (EEG) based BCI systems are being actively studied due to their high temporal resolution, convenient usage, and portability. However, fewer studies have been conducted to investigate the impact of high spatial resolution of EEG on decoding precise body motions, such as finger movements, which are essential in activities of daily living. Low spatial sensor resolution, as found in common EEG systems, can be improved by omitting the conventional standard of EEG electrode distribution (the international 10-20 system) and ordinary mounting structures (e.g., flexible caps). In this study, we used newly proposed flexible electrode grids attached directly to the scalp, which provided ultra-high-density EEG (uHD EEG). We explored the performance of the novel system by decoding individual finger movements using a total of 256 channels distributed over the contralateral sensorimotor cortex. Dense distribution and small-sized electrodes result in an inter-electrode distance of 8.6 mm (uHD EEG), while that of conventional EEG is 60 to 65 mm on average. Five healthy subjects participated in the experiment, performed single finger extensions according to a visual cue, and received avatar feedback. This study exploits mu (8-12 Hz) and beta (13-25 Hz) band power features for classification and topography plots. 3D ERD/S activation plots for each frequency band were generated using the MNI-152 template head. A linear support vector machine (SVM) was used for pairwise finger classification. The topography plots showed regular and focal post-cue activation, especially in subjects with optimal signal quality. The average classification accuracy over subjects was 64.8 (6.3)%, with the middle versus ring finger resulting in the highest average accuracy of 70.6 (9.4)%. Further studies are required using the uHD EEG system with real-time feedback and motor imagery tasks to enhance classification performance and establish the basis for BCI finger movement control of external devices.

RevDate: 2022-11-08

Tang C, Gao T, Li Y, et al (2022)

EEG channel selection based on sequential backward floating search for motor imagery classification.

Frontiers in neuroscience, 16:1045851.

Brain-computer interfaces (BCIs) based on motor imagery (MI) utilizing multi-channel electroencephalogram (EEG) data are commonly used to improve motor function of people with motor disabilities. EEG channel selection can enhance MI classification accuracy by selecting informative channels, accordingly reducing redundant information. The sequential backward floating search (SBFS) approach has been considered as one of the best feature selection methods. In this paper, SBFS is first implemented to select the optimal EEG channels in MI-BCI. Further, to reduce the time complexity of SBFS, the modified SBFS is proposed and applied to left and right hand MI tasks. In the modified SBFS, based on the map of EEG channels at the scalp, the symmetrical channels are selected as channel pairs and acceleration is thus realized by removing or adding multiple channels in each iteration. Extensive experiments were conducted on four public BCI datasets. Experimental results show that the SBFS achieves significantly higher classification accuracy (p < 0.001) than using all channels and conventional MI channels (i.e., C3, C4, and Cz). Moreover, the proposed method outperforms the state-of-the-art selection methods.

RevDate: 2022-11-08

He C, Du Y, X Zhao (2022)

A separable convolutional neural network-based fast recognition method for AR-P300.

Frontiers in human neuroscience, 16:986928.

Augmented reality-based brain-computer interface (AR-BCI) has a low signal-to-noise ratio (SNR) and high real-time requirements. Classical machine learning algorithms that improve the recognition accuracy through multiple averaging significantly affect the information transfer rate (ITR) of the AR-SSVEP system. In this study, a fast recognition method based on a separable convolutional neural network (SepCNN) was developed for an AR-based P300 component (AR-P300). SepCNN achieved single extraction of AR-P300 features and improved the recognition speed. A nine-target AR-P300 single-stimulus paradigm was designed to be administered with AR holographic glasses to verify the effectiveness of SepCNN. Compared with four classical algorithms, SepCNN significantly improved the average target recognition accuracy (81.1%) and information transmission rate (57.90 bits/min) of AR-P300 single extraction. SepCNN with single extraction also attained better results than classical algorithms with multiple averaging.

RevDate: 2022-11-08

Floreani ED, Rowley D, Kelly D, et al (2022)

On the feasibility of simple brain-computer interface systems for enabling children with severe physical disabilities to explore independent movement.

Frontiers in human neuroscience, 16:1007199.

INTRODUCTION: Children with severe physical disabilities are denied their fundamental right to move, restricting their development, independence, and participation in life. Brain-computer interfaces (BCIs) could enable children with complex physical needs to access power mobility (PM) devices, which could help them move safely and independently. BCIs have been studied for PM control for adults but remain unexamined in children. In this study, we explored the feasibility of BCI-enabled PM control for children with severe physical disabilities, assessing BCI performance, standard PM skills and tolerability of BCI.

MATERIALS AND METHODS: Patient-oriented pilot trial. Eight children with quadriplegic cerebral palsy attended two sessions where they used a simple, commercial-grade BCI system to activate a PM trainer device. Performance was assessed through controlled activation trials (holding the PM device still or activating it upon verbal and visual cueing), and basic PM skills (driving time, number of activations, stopping) were assessed through distance trials. Setup and calibration times, headset tolerability, workload, and patient/caregiver experience were also evaluated.

RESULTS: All participants completed the study with favorable tolerability and no serious adverse events or technological challenges. Average control accuracy was 78.3 ± 12.1%, participants were more reliably able to activate (95.7 ± 11.3%) the device than hold still (62.1 ± 23.7%). Positive trends were observed between performance and prior BCI experience and age. Participants were able to drive the PM device continuously an average of 1.5 meters for 3.0 s. They were able to stop at a target 53.1 ± 23.3% of the time, with significant variability. Participants tolerated the headset well, experienced mild-to-moderate workload and setup/calibration times were found to be practical. Participants were proud of their performance and both participants and families were eager to participate in future power mobility sessions.

DISCUSSION: BCI-enabled PM access appears feasible in disabled children based on evaluations of performance, tolerability, workload, and setup/calibration. Performance was comparable to existing pediatric BCI literature and surpasses established cut-off thresholds (70%) of "effective" BCI use. Participants exhibited PM skills that would categorize them as "emerging operational learners." Continued exploration of BCI-enabled PM for children with severe physical disabilities is justified.

RevDate: 2022-11-07

Li L (2022)

Preface to special topic on brain-machine interface.

National science review, 9(10):nwac211 pii:nwac211.

RevDate: 2022-11-08

Song X, Zeng Y, Tong L, et al (2022)

Corrigendum to "A Collaborative Brain-Computer Interface Framework for Enhancing Group Detection Performance of Dynamic Visual Targets".

Computational intelligence and neuroscience, 2022:9819804.

[This corrects the article DOI: 10.1155/2022/4752450.].

RevDate: 2022-11-10
CmpDate: 2022-11-08

Smrdel A (2022)

Use of common spatial patterns for early detection of Parkinson's disease.

Scientific reports, 12(1):18793.

One of the most common diseases that affects human brain is Parkinson's disease. Detection of Parkinson's disease (PD) poses a serious challenge. Robust methods for feature extraction allowing separation between the electroencephalograms (EEG) of healthy subjects and PD patients are required. We used the EEG records of healthy subjects and PD patients which were subject to auditory tasks. We used the common spatial patterns (CSP) and Laplacian mask as methods to allow robust selection and extraction of features. We used the derived CSP whitening matrix to determine those channels that are the most promising in the terms of differentiating between EEGs of healthy controls and of PD patients. Using the selection of features calculated using the CSP we managed to obtain the classification accuracy of 85% when classifying EEG records belonging to groups of controls or PD patients. Using the features calculated using the Laplacian operator we obtained the classification accuracy of 90%. Diagnosing the PD in early stages using EEG is possible. The CSP proved to be a promising technique to detect informative channels and to separate between the groups. Use of the combination of features calculated using the Laplacian offers good separability between the two groups.

RevDate: 2022-11-08
CmpDate: 2022-11-08

Zhao LH, Lin J, Ji SY, et al (2022)

Structure insights into selective coupling of G protein subtypes by a class B G protein-coupled receptor.

Nature communications, 13(1):6670.

The ability to couple with multiple G protein subtypes, such as Gs, Gi/o, or Gq/11, by a given G protein-coupled receptor (GPCR) is critical for many physiological processes. Over the past few years, the cryo-EM structures for all 15 members of the medically important class B GPCRs, all in complex with Gs protein, have been determined. However, no structure of class B GPCRs with Gq/11 has been solved to date, limiting our understanding of the precise mechanisms of G protein coupling selectivity. Here we report the structures of corticotropin releasing factor receptor 2 (CRF2R) bound to Urocortin 1 (UCN1), coupled with different classes of heterotrimeric G proteins, G11 and Go. We compare these structures with the structure of CRF2R in complex with Gs to uncover the structural differences that determine the selective coupling of G protein subtypes by CRF2R. These results provide important insights into the structural basis for the ability of CRF2R to couple with multiple G protein subtypes.

RevDate: 2022-12-08
CmpDate: 2022-12-07

Gao Z, Pang Z, Chen Y, et al (2022)

Restoring After Central Nervous System Injuries: Neural Mechanisms and Translational Applications of Motor Recovery.

Neuroscience bulletin, 38(12):1569-1587.

Central nervous system (CNS) injuries, including stroke, traumatic brain injury, and spinal cord injury, are leading causes of long-term disability. It is estimated that more than half of the survivors of severe unilateral injury are unable to use the denervated limb. Previous studies have focused on neuroprotective interventions in the affected hemisphere to limit brain lesions and neurorepair measures to promote recovery. However, the ability to increase plasticity in the injured brain is restricted and difficult to improve. Therefore, over several decades, researchers have been prompted to enhance the compensation by the unaffected hemisphere. Animal experiments have revealed that regrowth of ipsilateral descending fibers from the unaffected hemisphere to denervated motor neurons plays a significant role in the restoration of motor function. In addition, several clinical treatments have been designed to restore ipsilateral motor control, including brain stimulation, nerve transfer surgery, and brain-computer interface systems. Here, we comprehensively review the neural mechanisms as well as translational applications of ipsilateral motor control upon rehabilitation after CNS injuries.

RevDate: 2022-12-06
CmpDate: 2022-12-06

Zhan Q, Wang L, Ren L, et al (2022)

A novel heterogeneous transfer learning method based on data stitching for the sequential coding brain computer interface.

Computers in biology and medicine, 151(Pt A):106220.

OBJECTIVE: For the brain computer interface (BCI), it is necessary to collect enough electroencephalography (EEG) signals to train the classification model. When the operation dimension of BCI is large, it will bring great burden to data acquisition. Fortunately, this problem can be solved by our proposed transfer learning method.

METHOD: For the sequential coding experimental paradigm, the multi-band data stitching with label alignment and tangent space mapping (MDSLATSM) algorithm is proposed as a novel heterogeneous transfer learning method. After filtering by multi-band filtering, the artificial signals can be obtained by data stitching from the source domain, which build a bridge between the source domain and target domain. To make the distribution of two domains closer, their covariance matrices are aligned by label alignment. After mapping to the tangent space, the features are extracted from the Riemannian manifold. Finally, the classification results are obtained with feature selection and classification.

RESULTS: Our data set includes the EEG signals from 16 subjects. For the heterogeneous transfer learning of cross-label, the average classification accuracy is 78.28%. MDSLATSM is also tested for cross-subject, and the average classification accuracy is 64.01%, which is better than existing methods.

SIGNIFICANCE: Combining multi-band filtering, data stitching, label alignment and tangent space mapping, a novel heterogeneous transfer learning method can be achieved with superior performance, which promotes the practical application of the BCI systems.

RevDate: 2022-11-08

Zhang S, Wu L, Yu S, et al (2022)

An Explainable and Generalizable Recurrent Neural Network Approach for Differentiating Human Brain States on EEG Dataset.

IEEE transactions on neural networks and learning systems, PP: [Epub ahead of print].

Electroencephalogram (EEG) is one of the most widely used brain computer interface (BCI) approaches. Despite the success of existing EEG approaches in brain state recognition studies, it is still challenging to differentiate brain states via explainable and generalizable deep learning approaches. In other words, how to explore meaningful and distinguishing features and how to overcome the huge variability and overfitting problem still need to be further studied. To alleviate these challenges, in this work, a multiple random fragment search-based multilayer recurrent neural network (MRFS-MRNN) is proposed to improve the differentiating performance and explore meaningful patterns. Specifically, an explainable MRNN module is proposed to capture the temporal dependences preserved in EEG time series. Besides, a MRFS module is designed to cut multiple random fragments from the entire EEG signal time course to improve the effectiveness of brain state differentiating ability. MRFS-MRNN is concatenatedto effectively overcome the huge variabilities and overfitting problems. Experiment results demonstrate that the proposed MRFS-MRNN model not only has excellent differentiating performance, but also has good explanation and generalization ability. The classification accuracies reach as high as 95.18% for binary classification and 89.19% for four-category classification on the individual level. Similarly, 95.53% and 85.84% classification accuracies are obtained for the binary and four-category classification on the group level. What's more, 94.28% and 85.43% classification accuracies of binary and four-category classifications are achieved for predicting brand new subjects. The experiment results showed that the proposed method outperformed other state-of-the-art (SOTA) models on the same underlying data and improved the explanation and generalization ability.

RevDate: 2022-11-04

Wang R, Liu Y, Shi J, et al (2022)

Sound Target Detection under Noisy Environment Using Brain-Computer Interface.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society, PP: [Epub ahead of print].

As an important means of environmental reconnaissance and regional security protection, sound target detection (STD) has been widely studied in the field of machine learning for a long time. Considering the shortcomings of the robustness and generalization performance of existing methods based on machine learning, we proposed a target detection method by an auditory brain-computer interface (BCI). We designed the experimental paradigm according to the actual application scenarios of STD, recorded the changes in Electroencephalogram (EEG) signals during the process of detecting target sound, and further extracted the features used to decode EEG signals through the analysis of neural representations, including Event-Related Potential (ERP) and Event-Related Spectral Perturbation (ERSP). Experimental results showed that the proposed method achieved good detection performance under noisy environment. As the first study of BCI applied to STD, this study shows the feasibility of this scheme in BCI and can serve as the foundation for future related applications.

RevDate: 2022-11-09
CmpDate: 2022-11-09

Mencel J, Marusiak J, Jaskólska A, et al (2022)

Motor imagery training of goal-directed reaching in relation to imagery of reaching and grasping in healthy people.

Scientific reports, 12(1):18610.

The study aimed to determine whether four weeks of motor imagery training (MIT) of goal-directed reaching (reaching to grasp task) would affect the cortical activity during motor imagery of reaching (MIR) and grasping (MIG) in the same way. We examined cortical activity regarding event-related potentials (ERPs) in healthy young participants. Our study also evaluated the subjective vividness of the imagery. Furthermore, we aimed to determine the relationship between the subjective assessment of motor imagery (MI) ability to reach and grasp and the cortical activity during those tasks before and after training to understand the underlying neuroplasticity mechanisms. Twenty-seven volunteers participated in MIT of goal-directed reaching and two measurement sessions before and after MIT. During the sessions 128-channel electroencephalography (EEG) was recorded during MIR and MIG. Also, participants assessed the vividness of the MI tasks using a visual analog scale (VAS). The vividness of imagination improved significantly (P < .05) after MIT. A repeated measures ANOVA showed that the task (MIR/MIG) and the location of electrodes had a significant effect on the ERP's amplitude (P < .05). The interaction between the task, location, and session (before/after MIT) also had a significant effect on the ERP's amplitude (P < .05). Finally, the location of electrodes and the interaction between location and session had a significant effect on the ERP's latency (P < .05). We found that MIT influenced the EEG signal associated with reaching differently than grasping. The effect was more pronounced for MIR than for MIG. Correlation analysis showed that changes in the assessed parameters due to MIT reduced the relationship between the subjective evaluation of imagining and the EEG signal. This finding means that the subjective evaluation of imagining cannot be a simple, functional insight into the bioelectrical activity of the cerebral cortex expressed by the ERPs in mental training. The changes we noted in ERPs after MIT may benefit the use of non-invasive EEG in the brain-computer interface (BCI) context.Trial registration: NCT04048083.

RevDate: 2022-11-23
CmpDate: 2022-11-23

Fu Y, Zhu Y, Zhang Y, et al (2022)

Is AlphaFold a perfect experimental assistant of psychiatric drug discovery in precision psychiatry era?.

Asian journal of psychiatry, 78:103305.

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Rajesh Rao has written the perfect introduction to the exciting world of brain-computer interfaces. The book is remarkably comprehensive — not only including full descriptions of classic and current experiments but also covering essential background concepts, from the brain to Bayes and back. Brain-Computer Interfacing will be welcomed by a wide range of intelligent readers interested in understanding the first steps toward the symbiotic merger of brains and computers. Eberhard E. Fetz, UW

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