{"topic":"brain-computer-interfaces","sort":"top","limit":25,"offset":0,"count":25,"papers":[{"id":"doi:10.1038/s41586-023-06377-x","kind":"article","title":"A high-performance speech neuroprosthesis","authors":["Francis R. Willett","Erin M. Kunz","Chaofei Fan","Donald T. Avansino","Guy H Wilson","Eun Young Choi","Foram B. Kamdar","Matthew F. Glasser","Leigh R. Hochberg","Shaul Druckmann","K.V. Shenoy","Jaimie M. Henderson"],"author_count":12,"year":2023,"published":"2023-08-23","venue":"Nature","cited_by":578,"doi":"10.1038/s41586-023-06377-x","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1038/s41586-023-06377-x","archive_url":"https://space-kitty.com/papers/doi:10.1038/s41586-023-06377-x","updated":null,"pmid":"36711591","pmcid":"PMC10468393","openalex_id":"W4386091044","pdf_url":"https://www.nature.com/articles/s41586-023-06377-x.pdf","abstract":"Speech brain-computer interfaces (BCIs) have the potential to restore rapid communication to people with paralysis by decoding neural activity evoked by attempted speaking movements into text or sound. Early demonstrations, while promising, have not yet achieved accuracies high enough for communication of unconstrainted sentences from a large vocabulary. Here, we demonstrate the first speech-to-text BCI that records spiking activity from intracortical microelectrode arrays. Enabled by these high-resolution recordings, our study participant, who can no longer speak intelligibly due amyotrophic lateral sclerosis (ALS), achieved a 9.1% word error rate on a 50 word vocabulary (2.7 times fewer errors than the prior state of the art speech BCI2) and a 23.8% word error rate on a 125,000 word vocabulary (the first successful demonstration of large-vocabulary decoding). Our BCI decoded speech at 62 words per minute, which is 3.4 times faster than the prior record for any kind of BCI and begins to approach the speed of natural conversation (160 words per minute). Finally, we highlight two aspects of the neural code for speech that are encouraging for speech BCIs: spatially intermixed tuning to speech articulators that makes accurate decoding possible from only a small region of cortex, and a detailed articulatory representation of phonemes that persists years after paralysis. These results show a feasible path forward for using intracortical speech BCIs to restore rapid communication to people with paralysis who can no longer speak.","abstract_license":"CC-BY (publisher license, via OpenAlex)","license":"cc-by","is_open_access":true,"peer_reviewed":null,"categories":["EEG and Brain-Computer Interfaces"],"details":null,"provenance":"openalex+europepmc","attribution":"Abstract of \"A high-performance speech neuroprosthesis\" by Francis R. Willett, Erin M. Kunz, Chaofei Fan et al., https://doi.org/10.1038/s41586-023-06377-x, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified."},{"id":"doi:10.3390/s120201211","kind":"review","title":"Brain computer interfaces, a review","authors":["Nicolas-Alonso LF","Gomez-Gil J"],"author_count":2,"year":2012,"published":"2012-01-31","venue":"Sensors (Basel, Switzerland)","cited_by":715,"doi":"10.3390/s120201211","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.3390/s120201211","archive_url":"https://space-kitty.com/papers/doi:10.3390/s120201211","updated":null,"pmid":"22438708","pmcid":"PMC3304110","openalex_id":null,"pdf_url":"https://europepmc.org/articles/PMC3304110/pdf","abstract":"A brain-computer interface (BCI) is a hardware and software communications system that permits cerebral activity alone to control computers or external devices. The immediate goal of BCI research is to provide communications capabilities to severely disabled people who are totally paralyzed or 'locked in' by neurological neuromuscular disorders, such as amyotrophic lateral sclerosis, brain stem stroke, or spinal cord injury. Here, we review the state-of-the-art of BCIs, looking at the different steps that form a standard BCI: signal acquisition, preprocessing or signal enhancement, feature extraction, classification and the control interface. We discuss their advantages, drawbacks, and latest advances, and we survey the numerous technologies reported in the scientific literature to design each step of a BCI. First, the review examines the neuroimaging modalities used in the signal acquisition step, each of which monitors a different functional brain activity such as electrical, magnetic or metabolic activity. Second, the review discusses different electrophysiological control signals that determine user intentions, which can be detected in brain activity. Third, the review includes some techniques used in the signal enhancement step to deal with the artifacts in the control signals and improve the performance. Fourth, the review studies some mathematic algorithms used in the feature extraction and classification steps which translate the information in the control signals into commands that operate a computer or other device. Finally, the review provides an overview of various BCI applications that control a range of devices.","abstract_license":"CC-BY (article license as recorded by Europe PMC; abstract reproduced unmodified apart from markup removal)","license":"CC-BY","is_open_access":true,"peer_reviewed":null,"categories":[],"details":null,"provenance":"europepmc","attribution":"Abstract of \"Brain computer interfaces, a review\" by Nicolas-Alonso LF, Gomez-Gil J, https://doi.org/10.3390/s120201211, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified."},{"id":"doi:10.1056/nejmoa2314132","kind":"article","title":"An Accurate and Rapidly Calibrating Speech Neuroprosthesis","authors":["Nicholas S. Card","Maitreyee Wairagkar","Carrina Iacobacci","Xianda Hou","Tyler S. Singer-Clark","Francis R. Willett","Erin M. Kunz","Chaofei Fan","Maryam Vahdati Nia","Darrel R. Deo","Aparna Srinivasan","Eun Young Choi","Matthew F. Glasser","Leigh R. Hochberg","Jaimie M. Henderson","Kiarash Shahlaie","Sergey D. Stavisky","David M. Brandman"],"author_count":18,"year":2024,"published":"2024-08-14","venue":"New England Journal of Medicine","cited_by":243,"doi":"10.1056/nejmoa2314132","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1056/nejmoa2314132","archive_url":"https://space-kitty.com/papers/doi:10.1056/nejmoa2314132","updated":null,"pmid":"38645254","pmcid":null,"openalex_id":"W4401560881","pdf_url":"https://www.medrxiv.org/content/medrxiv/early/2023/12/26/2023.12.26.23300110.full.pdf","abstract":null,"abstract_license":null,"license":"cc-by-nc-nd","is_open_access":true,"peer_reviewed":null,"categories":["EEG and Brain-Computer Interfaces"],"details":null,"provenance":"openalex","attribution":"Metadata and citation counts: OpenAlex (CC0)."},{"id":"doi:10.3389/fnhum.2015.00003","kind":"review","title":"fNIRS-based brain-computer interfaces: a review","authors":["Naseer N","Hong KS"],"author_count":2,"year":2015,"published":"2015-01-28","venue":"Frontiers in human neuroscience","cited_by":404,"doi":"10.3389/fnhum.2015.00003","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.3389/fnhum.2015.00003","archive_url":"https://space-kitty.com/papers/doi:10.3389/fnhum.2015.00003","updated":null,"pmid":"25674060","pmcid":"PMC4309034","openalex_id":null,"pdf_url":"https://europepmc.org/articles/PMC4309034/pdf","abstract":"A brain-computer interface (BCI) is a communication system that allows the use of brain activity to control computers or other external devices. It can, by bypassing the peripheral nervous system, provide a means of communication for people suffering from severe motor disabilities or in a persistent vegetative state. In this paper, brain-signal generation tasks, noise removal methods, feature extraction/selection schemes, and classification techniques for fNIRS-based BCI are reviewed. The most common brain areas for fNIRS BCI are the primary motor cortex and the prefrontal cortex. In relation to the motor cortex, motor imagery tasks were preferred to motor execution tasks since possible proprioceptive feedback could be avoided. In relation to the prefrontal cortex, fNIRS showed a significant advantage due to no hair in detecting the cognitive tasks like mental arithmetic, music imagery, emotion induction, etc. In removing physiological noise in fNIRS data, band-pass filtering was mostly used. However, more advanced techniques like adaptive filtering, independent component analysis (ICA), multi optodes arrangement, etc. are being pursued to overcome the problem that a band-pass filter cannot be used when both brain and physiological signals occur within a close band. In extracting features related to the desired brain signal, the mean, variance, peak value, slope, skewness, and kurtosis of the noised-removed hemodynamic response were used. For classification, the linear discriminant analysis method provided simple but good performance among others: support vector machine (SVM), hidden Markov model (HMM), artificial neural network, etc. fNIRS will be more widely used to monitor the occurrence of neuro-plasticity after neuro-rehabilitation and neuro-stimulation. Technical breakthroughs in the future are expected via bundled-type probes, hybrid EEG-fNIRS BCI, and through the detection of initial dips.","abstract_license":"CC-BY (article license as recorded by Europe PMC; abstract reproduced unmodified apart from markup removal)","license":"CC-BY","is_open_access":true,"peer_reviewed":null,"categories":[],"details":null,"provenance":"europepmc","attribution":"Abstract of \"fNIRS-based brain-computer interfaces: a review\" by Naseer N, Hong KS, https://doi.org/10.3389/fnhum.2015.00003, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified."},{"id":"doi:10.7554/elife.18554","kind":"article","title":"High performance communication by people with paralysis using an intracortical brain-computer interface","authors":["Pandarinath C","Nuyujukian P","Blabe CH","Sorice BL","Saab J","Willett FR","Hochberg LR","Shenoy KV","Henderson JM"],"author_count":9,"year":2017,"published":"2017-02-21","venue":"eLife","cited_by":388,"doi":"10.7554/elife.18554","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.7554/elife.18554","archive_url":"https://space-kitty.com/papers/doi:10.7554/elife.18554","updated":null,"pmid":"28220753","pmcid":"PMC5319839","openalex_id":null,"pdf_url":"https://europepmc.org/articles/PMC5319839/pdf","abstract":"Brain-computer interfaces (BCIs) have the potential to restore communication for people with tetraplegia and anarthria by translating neural activity into control signals for assistive communication devices. While previous pre-clinical and clinical studies have demonstrated promising proofs-of-concept (Serruya et al., 2002; Simeral et al., 2011; Bacher et al., 2015; Nuyujukian et al., 2015; Aflalo et al., 2015; Gilja et al., 2015; Jarosiewicz et al., 2015; Wolpaw et al., 1998; Hwang et al., 2012; Spüler et al., 2012; Leuthardt et al., 2004; Taylor et al., 2002; Schalk et al., 2008; Moran, 2010; Brunner et al., 2011; Wang et al., 2013; Townsend and Platsko, 2016; Vansteensel et al., 2016; Nuyujukian et al., 2016; Carmena et al., 2003; Musallam et al., 2004; Santhanam et al., 2006; Hochberg et al., 2006; Ganguly et al., 2011; O'Doherty et al., 2011; Gilja et al., 2012), the performance of human clinical BCI systems is not yet high enough to support widespread adoption by people with physical limitations of speech. Here we report a high-performance intracortical BCI (iBCI) for communication, which was tested by three clinical trial participants with paralysis. The system leveraged advances in decoder design developed in prior pre-clinical and clinical studies (Gilja et al., 2015; Kao et al., 2016; Gilja et al., 2012). For all three participants, performance exceeded previous iBCIs (Bacher et al., 2015; Jarosiewicz et al., 2015) as measured by typing rate (by a factor of 1.4-4.2) and information throughput (by a factor of 2.2-4.0). This high level of performance demonstrates the potential utility of iBCIs as powerful assistive communication devices for people with limited motor function.Clinical Trial No: NCT00912041.","abstract_license":"CC0-1.0 (article license as recorded by Europe PMC; abstract reproduced unmodified apart from markup removal)","license":"CC0-1.0","is_open_access":true,"peer_reviewed":null,"categories":[],"details":null,"provenance":"europepmc","attribution":"Metadata: Europe PMC (EMBL-EBI)."},{"id":"doi:10.1088/1741-2552/aba162","kind":"review","title":"BCI for stroke rehabilitation: motor and beyond","authors":["Mane R","Chouhan T","Guan C"],"author_count":3,"year":2020,"published":"2020-08-17","venue":"Journal of neural engineering","cited_by":237,"doi":"10.1088/1741-2552/aba162","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1088/1741-2552/aba162","archive_url":"https://space-kitty.com/papers/doi:10.1088/1741-2552/aba162","updated":null,"pmid":"32613947","pmcid":null,"openalex_id":null,"pdf_url":null,"abstract":"Stroke is one of the leading causes of long-term disability among adults and contributes to major socio-economic burden globally. Stroke frequently results in multifaceted impairments including motor, cognitive and emotion deficits. In recent years, brain-computer interface (BCI)-based therapy has shown promising results for post-stroke motor rehabilitation. In spite of the success received by BCI-based interventions in the motor domain, non-motor impairments are yet to receive similar attention in research and clinical settings. Some preliminary encouraging results in post-stroke cognitive rehabilitation using BCI seem to suggest that it may also hold potential for treating non-motor deficits such as cognitive and emotion impairments. Moreover, past studies have shown an intricate relationship between motor, cognitive and emotion functions which might influence the overall post-stroke rehabilitation outcome. A number of studies highlight the inability of current treatment protocols to account for the implicit interplay between motor, cognitive and emotion functions. This indicates the necessity to explore an all-inclusive treatment plan targeting the synergistic influence of these standalone interventions. This approach may lead to better overall recovery than treating the individual deficits in isolation. In this paper, we review the recent advances in BCI-based post-stroke motor rehabilitation and highlight the potential for the use of BCI systems beyond the motor domain, in particular, in improving cognition and emotion of stroke patients. Building on the current results and findings of studies in individual domains, we next discuss the possibility of a holistic BCI system for motor, cognitive and affect rehabilitation which may synergistically promote restorative neuroplasticity. Such a system would provide an all-encompassing rehabilitation platform, leading to overarching clinical outcomes and transfer of these outcomes to a better quality of living. This is one of the first works to analyse the possibility of targeting cross-domain influence of post-stroke functional recovery enabled by BCI-based rehabilitation.","abstract_license":"CC-BY (article license as recorded by Europe PMC; abstract reproduced unmodified apart from markup removal)","license":"CC-BY","is_open_access":false,"peer_reviewed":null,"categories":[],"details":null,"provenance":"europepmc","attribution":"Abstract of \"BCI for stroke rehabilitation: motor and beyond\" by Mane R, Chouhan T, Guan C, https://doi.org/10.1088/1741-2552/aba162, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified."},{"id":"doi:10.1093/gigascience/giz002","kind":"article","title":"EEG dataset and OpenBMI toolbox for three BCI paradigms: an investigation into BCI illiteracy","authors":["Lee MH","Kwon OY","Kim YJ","Kim HK","Lee YE","Williamson J","Fazli S","Lee SW"],"author_count":8,"year":2019,"published":"2019-05-01","venue":"GigaScience","cited_by":235,"doi":"10.1093/gigascience/giz002","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1093/gigascience/giz002","archive_url":"https://space-kitty.com/papers/doi:10.1093/gigascience/giz002","updated":null,"pmid":"30698704","pmcid":"PMC6501944","openalex_id":null,"pdf_url":"https://europepmc.org/articles/PMC6501944/pdf","abstract":"Background Electroencephalography (EEG)-based brain-computer interface (BCI) systems are mainly divided into three major paradigms: motor imagery (MI), event-related potential (ERP), and steady-state visually evoked potential (SSVEP). Here, we present a BCI dataset that includes the three major BCI paradigms with a large number of subjects over multiple sessions. In addition, information about the psychological and physiological conditions of BCI users was obtained using a questionnaire, and task-unrelated parameters such as resting state, artifacts, and electromyography of both arms were also recorded. We evaluated the decoding accuracies for the individual paradigms and determined performance variations across both subjects and sessions. Furthermore, we looked for more general, severe cases of BCI illiteracy than have been previously reported in the literature. Results Average decoding accuracies across all subjects and sessions were 71.1% (± 0.15), 96.7% (± 0.05), and 95.1% (± 0.09), and rates of BCI illiteracy were 53.7%, 11.1%, and 10.2% for MI, ERP, and SSVEP, respectively. Compared to the ERP and SSVEP paradigms, the MI paradigm exhibited large performance variations between both subjects and sessions. Furthermore, we found that 27.8% (15 out of 54) of users were universally BCI literate, i.e., they were able to proficiently perform all three paradigms. Interestingly, we found no universally illiterate BCI user, i.e., all participants were able to control at least one type of BCI system. Conclusions Our EEG dataset can be utilized for a wide range of BCI-related research questions. All methods for the data analysis in this study are supported with fully open-source scripts that can aid in every step of BCI technology. Furthermore, our results support previous but disjointed findings on the phenomenon of BCI illiteracy.","abstract_license":"CC-BY (article license as recorded by Europe PMC; abstract reproduced unmodified apart from markup removal)","license":"CC-BY","is_open_access":true,"peer_reviewed":null,"categories":[],"details":null,"provenance":"europepmc","attribution":"Abstract of \"EEG dataset and OpenBMI toolbox for three BCI paradigms: an investigation into BCI illiteracy\" by Lee MH, Kwon OY, Kim YJ et al., https://doi.org/10.1093/gigascience/giz002, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified."},{"id":"arxiv:1901.03442","kind":"review","title":"Progress in Brain Computer Interfaces: Challenges and Trends","authors":["Simanto Saha","Khondaker A. Mamun","Khawza Ahmed","Raqibul Mostafa","Ganesh R. Naik","Ahsan Khandoker","Sam Darvishi","Mathias Baumert"],"author_count":8,"year":2019,"published":"2019-01-11","venue":"Frontiers in systems neuroscience","cited_by":396,"doi":"10.3389/fnsys.2021.578875","arxiv_id":"1901.03442","nct_id":null,"source_url":"https://arxiv.org/abs/1901.03442","archive_url":"https://space-kitty.com/papers/arxiv:1901.03442","updated":"2019-01-11","pmid":"33716680","pmcid":"PMC7947348","openalex_id":"W2910098374","pdf_url":"https://arxiv.org/pdf/1901.03442","abstract":"Brain computer interfaces (BCI) provide a direct communication link between the brain and a computer or other external devices. They offer an extended degree of freedom either by strengthening or by substituting human peripheral working capacity and have potential applications in various fields such as rehabilitation, affective computing, robotics, gaming and artificial intelligence. Significant research efforts on a global scale have delivered common platforms for technology standardization and help tackle highly complex and nonlinear brain dynamics and related feature extraction and classification challenges. Psycho-neurophysiological phenomena and their impact on brain signals impose another challenge for BCI researchers to transform the technology from laboratory experiments to plug-and-play daily life. This review summarizes progress in BCI field and highlights critical challenges.","abstract_license":"CC0-1.0 (arXiv descriptive metadata)","license":"CC-BY","is_open_access":true,"peer_reviewed":null,"categories":["cs.HC","q-bio.NC"],"details":null,"provenance":"arxiv+europepmc","attribution":"Abstract: arXiv descriptive metadata (CC0). Thank you to arXiv for use of its open access interoperability. This service was not reviewed or approved by, nor does it necessarily express or reflect the policies or opinions of, arXiv."},{"id":"doi:10.1371/journal.pone.0055344","kind":"article","title":"An electrocorticographic brain interface in an individual with tetraplegia","authors":["Wang W","Collinger JL","Degenhart AD","Tyler-Kabara EC","Schwartz AB","Moran DW","Weber DJ","Wodlinger B","Vinjamuri RK","Ashmore RC","Kelly JW","Boninger ML"],"author_count":12,"year":2013,"published":"2013-02-06","venue":"PloS one","cited_by":233,"doi":"10.1371/journal.pone.0055344","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1371/journal.pone.0055344","archive_url":"https://space-kitty.com/papers/doi:10.1371/journal.pone.0055344","updated":null,"pmid":"23405137","pmcid":"PMC3566209","openalex_id":null,"pdf_url":"https://europepmc.org/articles/PMC3566209/pdf","abstract":"Brain-computer interface (BCI) technology aims to help individuals with disability to control assistive devices and reanimate paralyzed limbs. Our study investigated the feasibility of an electrocorticography (ECoG)-based BCI system in an individual with tetraplegia caused by C4 level spinal cord injury. ECoG signals were recorded with a high-density 32-electrode grid over the hand and arm area of the left sensorimotor cortex. The participant was able to voluntarily activate his sensorimotor cortex using attempted movements, with distinct cortical activity patterns for different segments of the upper limb. Using only brain activity, the participant achieved robust control of 3D cursor movement. The ECoG grid was explanted 28 days post-implantation with no adverse effect. This study demonstrates that ECoG signals recorded from the sensorimotor cortex can be used for real-time device control in paralyzed individuals.","abstract_license":"CC0-1.0 (article license as recorded by Europe PMC; abstract reproduced unmodified apart from markup removal)","license":"CC0-1.0","is_open_access":true,"peer_reviewed":null,"categories":[],"details":null,"provenance":"europepmc","attribution":"Metadata: Europe PMC (EMBL-EBI)."},{"id":"doi:10.1038/s41392-024-01845-w","kind":"review","title":"Pathology of pain and its implications for therapeutic interventions","authors":["Cao B","Xu Q","Shi Y","Zhao R","Li H","Zheng J","Liu F","Wan Y","Wei B"],"author_count":9,"year":2024,"published":"2024-06-08","venue":"Signal transduction and targeted therapy","cited_by":153,"doi":"10.1038/s41392-024-01845-w","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1038/s41392-024-01845-w","archive_url":"https://space-kitty.com/papers/doi:10.1038/s41392-024-01845-w","updated":null,"pmid":"38851750","pmcid":"PMC11162504","openalex_id":null,"pdf_url":"https://europepmc.org/articles/PMC11162504/pdf","abstract":"Pain is estimated to affect more than 20% of the global population, imposing incalculable health and economic burdens. Effective pain management is crucial for individuals suffering from pain. However, the current methods for pain assessment and treatment fall short of clinical needs. Benefiting from advances in neuroscience and biotechnology, the neuronal circuits and molecular mechanisms critically involved in pain modulation have been elucidated. These research achievements have incited progress in identifying new diagnostic and therapeutic targets. In this review, we first introduce fundamental knowledge about pain, setting the stage for the subsequent contents. The review next delves into the molecular mechanisms underlying pain disorders, including gene mutation, epigenetic modification, posttranslational modification, inflammasome, signaling pathways and microbiota. To better present a comprehensive view of pain research, two prominent issues, sexual dimorphism and pain comorbidities, are discussed in detail based on current findings. The status quo of pain evaluation and manipulation is summarized. A series of improved and innovative pain management strategies, such as gene therapy, monoclonal antibody, brain-computer interface and microbial intervention, are making strides towards clinical application. We highlight existing limitations and future directions for enhancing the quality of preclinical and clinical research. Efforts to decipher the complexities of pain pathology will be instrumental in translating scientific discoveries into clinical practice, thereby improving pain management from bench to bedside.","abstract_license":"CC-BY (article license as recorded by Europe PMC; abstract reproduced unmodified apart from markup removal)","license":"CC-BY","is_open_access":true,"peer_reviewed":null,"categories":[],"details":null,"provenance":"europepmc","attribution":"Abstract of \"Pathology of pain and its implications for therapeutic interventions\" by Cao B, Xu Q, Shi Y et al., https://doi.org/10.1038/s41392-024-01845-w, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified."},{"id":"doi:10.1038/s41586-025-09127-3","kind":"article","title":"An instantaneous voice-synthesis neuroprosthesis","authors":["Maitreyee Wairagkar","Nicholas S. Card","Tyler S. Singer-Clark","Xianda Hou","Carrina Iacobacci","Lee M. Miller","Leigh R. Hochberg","David M. Brandman","Sergey D. Stavisky"],"author_count":9,"year":2025,"published":"2025-06-12","venue":"Nature","cited_by":98,"doi":"10.1038/s41586-025-09127-3","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1038/s41586-025-09127-3","archive_url":"https://space-kitty.com/papers/doi:10.1038/s41586-025-09127-3","updated":null,"pmid":"39229047","pmcid":null,"openalex_id":"W4411240941","pdf_url":null,"abstract":null,"abstract_license":null,"license":"cc-by-nc-nd","is_open_access":true,"peer_reviewed":null,"categories":["EEG and Brain-Computer Interfaces"],"details":null,"provenance":"openalex","attribution":"Metadata and citation counts: OpenAlex (CC0)."},{"id":"doi:10.1088/1741-2560/10/4/046003","kind":"article","title":"Quadcopter control in three-dimensional space using a noninvasive motor imagery-based brain-computer interface","authors":["LaFleur K","Cassady K","Doud A","Shades K","Rogin E","He B"],"author_count":6,"year":2013,"published":"2013-06-04","venue":"Journal of neural engineering","cited_by":229,"doi":"10.1088/1741-2560/10/4/046003","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1088/1741-2560/10/4/046003","archive_url":"https://space-kitty.com/papers/doi:10.1088/1741-2560/10/4/046003","updated":null,"pmid":"23735712","pmcid":"PMC3839680","openalex_id":null,"pdf_url":"https://europepmc.org/articles/PMC3839680/pdf","abstract":"Objective At the balanced intersection of human and machine adaptation is found the optimally functioning brain-computer interface (BCI). In this study, we report a novel experiment of BCI controlling a robotic quadcopter in three-dimensional (3D) physical space using noninvasive scalp electroencephalogram (EEG) in human subjects. We then quantify the performance of this system using metrics suitable for asynchronous BCI. Lastly, we examine the impact that the operation of a real world device has on subjects' control in comparison to a 2D virtual cursor task. Approach Five human subjects were trained to modulate their sensorimotor rhythms to control an AR Drone navigating a 3D physical space. Visual feedback was provided via a forward facing camera on the hull of the drone. Main results Individual subjects were able to accurately acquire up to 90.5% of all valid targets presented while travelling at an average straight-line speed of 0.69 m s(-1). Significance Freely exploring and interacting with the world around us is a crucial element of autonomy that is lost in the context of neurodegenerative disease. Brain-computer interfaces are systems that aim to restore or enhance a user's ability to interact with the environment via a computer and through the use of only thought. We demonstrate for the first time the ability to control a flying robot in 3D physical space using noninvasive scalp recorded EEG in humans. Our work indicates the potential of noninvasive EEG-based BCI systems for accomplish complex control in 3D physical space. The present study may serve as a framework for the investigation of multidimensional noninvasive BCI control in a physical environment using telepresence robotics.","abstract_license":"CC-BY (article license as recorded by Europe PMC; abstract reproduced unmodified apart from markup removal)","license":"CC-BY","is_open_access":true,"peer_reviewed":null,"categories":[],"details":null,"provenance":"europepmc","attribution":"Abstract of \"Quadcopter control in three-dimensional space using a noninvasive motor imagery-based brain-computer interface\" by LaFleur K, Cassady K, Doud A et al., https://doi.org/10.1088/1741-2560/10/4/046003, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified."},{"id":"doi:10.1038/srep38565","kind":"article","title":"Noninvasive Electroencephalogram Based Control of a Robotic Arm for Reach and Grasp Tasks","authors":["Meng J","Zhang S","Bekyo A","Olsoe J","Baxter B","He B"],"author_count":6,"year":2016,"published":"2016-12-14","venue":"Scientific reports","cited_by":216,"doi":"10.1038/srep38565","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1038/srep38565","archive_url":"https://space-kitty.com/papers/doi:10.1038/srep38565","updated":null,"pmid":"27966546","pmcid":"PMC5155290","openalex_id":null,"pdf_url":"https://europepmc.org/articles/PMC5155290/pdf","abstract":"Brain-computer interface (BCI) technologies aim to provide a bridge between the human brain and external devices. Prior research using non-invasive BCI to control virtual objects, such as computer cursors and virtual helicopters, and real-world objects, such as wheelchairs and quadcopters, has demonstrated the promise of BCI technologies. However, controlling a robotic arm to complete reach-and-grasp tasks efficiently using non-invasive BCI has yet to be shown. In this study, we found that a group of 13 human subjects could willingly modulate brain activity to control a robotic arm with high accuracy for performing tasks requiring multiple degrees of freedom by combination of two sequential low dimensional controls. Subjects were able to effectively control reaching of the robotic arm through modulation of their brain rhythms within the span of only a few training sessions and maintained the ability to control the robotic arm over multiple months. Our results demonstrate the viability of human operation of prosthetic limbs using non-invasive BCI technology.","abstract_license":"CC-BY (article license as recorded by Europe PMC; abstract reproduced unmodified apart from markup removal)","license":"CC-BY","is_open_access":true,"peer_reviewed":null,"categories":[],"details":null,"provenance":"europepmc","attribution":"Abstract of \"Noninvasive Electroencephalogram Based Control of a Robotic Arm for Reach and Grasp Tasks\" by Meng J, Zhang S, Bekyo A et al., https://doi.org/10.1038/srep38565, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified."},{"id":"doi:10.1162/imag.a.136","kind":"article","title":"The lab streaming layer for synchronized multimodal recording","authors":["Kothe C","Shirazi SY","Stenner T","Medine D","Boulay C","Grivich MI","Artoni F","Mullen T","Delorme A","Makeig S"],"author_count":10,"year":2025,"published":"2025-09-12","venue":"Imaging neuroscience (Cambridge, Mass.)","cited_by":132,"doi":"10.1162/imag.a.136","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1162/imag.a.136","archive_url":"https://space-kitty.com/papers/doi:10.1162/imag.a.136","updated":null,"pmid":"40959706","pmcid":"PMC12434378","openalex_id":null,"pdf_url":"https://europepmc.org/articles/PMC12434378/pdf","abstract":"Accurately recording the interactions of humans or other organisms with their environment and other agents requires synchronized data access via multiple instruments, often running independently using different clocks. Active, hardware-mediated solutions are often infeasible or prohibitively costly to build and run across arbitrary collections of input systems. The Lab Streaming Layer (LSL) framework offers a software-based approach to synchronizing data streams based on per-sample time stamps and time synchronization across a common local area network (LAN). Built from the ground up for neurophysiological applications and designed for reliability, LSL offers zero-configuration functionality and accounts for network delays and jitters, making connection recovery, offset correction, and jitter compensation possible. These features can ensure continuous, millisecond-precise data recording, even in the face of interruptions. In this paper, we present an overview of LSL architecture, core features, and performance in common experimental contexts. We also highlight practical considerations and known pitfalls when using LSL, including the need to take into account input device throughput delays that LSL cannot itself measure or correct. The LSL ecosystem has grown to support over 150 data acquisition device classes and to establish interoperability between client software written in several programming languages, including C/C++, Python, MATLAB, Java, C#, JavaScript, Rust, and Julia. The resilience and versatility of LSL have made it a major data synchronization platform for multimodal human neurobehavioral recording, now supported by a wide range of software packages, including major stimulus presentation tools, real-time analysis environments, and brain-computer interface applications. Beyond basic science, research, and development, LSL has been used as a resilient and transparent back-end in deployment scenarios, including interactive art installations, stage performances, and commercial products. In neurobehavioral studies and other neuroscience applications, LSL facilitates the complex task of capturing organismal dynamics and environmental changes occurring within and across multiple data streams on a common timeline.","abstract_license":"CC-BY (article license as recorded by Europe PMC; abstract reproduced unmodified apart from markup removal)","license":"CC-BY","is_open_access":true,"peer_reviewed":null,"categories":[],"details":null,"provenance":"europepmc","attribution":"Abstract of \"The lab streaming layer for synchronized multimodal recording\" by Kothe C, Shirazi SY, Stenner T et al., https://doi.org/10.1162/imag.a.136, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified."},{"id":"doi:10.1038/s41598-018-37359-z","kind":"article","title":"Towards reconstructing intelligible speech from the human auditory cortex","authors":["Hassan Akbari","Bahar Khalighinejad","Jose L. Herrero","Ashesh D. Mehta","Nima Mesgarani"],"author_count":5,"year":2019,"published":"2019-01-29","venue":"Scientific Reports","cited_by":221,"doi":"10.1038/s41598-018-37359-z","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1038/s41598-018-37359-z","archive_url":"https://space-kitty.com/papers/doi:10.1038/s41598-018-37359-z","updated":null,"pmid":"30696881","pmcid":"PMC6351601","openalex_id":"W2913144232","pdf_url":"https://www.nature.com/articles/s41598-018-37359-z.pdf","abstract":"Auditory stimulus reconstruction is a technique that finds the best approximation of the acoustic stimulus from the population of evoked neural activity. Reconstructing speech from the human auditory cortex creates the possibility of a speech neuroprosthetic to establish a direct communication with the brain and has been shown to be possible in both overt and covert conditions. However, the low quality of the reconstructed speech has severely limited the utility of this method for brain-computer interface (BCI) applications. To advance the state-of-the-art in speech neuroprosthesis, we combined the recent advances in deep learning with the latest innovations in speech synthesis technologies to reconstruct closed-set intelligible speech from the human auditory cortex. We investigated the dependence of reconstruction accuracy on linear and nonlinear (deep neural network) regression methods and the acoustic representation that is used as the target of reconstruction, including auditory spectrogram and speech synthesis parameters. In addition, we compared the reconstruction accuracy from low and high neural frequency ranges. Our results show that a deep neural network model that directly estimates the parameters of a speech synthesizer from all neural frequencies achieves the highest subjective and objective scores on a digit recognition task, improving the intelligibility by 65% over the baseline method which used linear regression to reconstruct the auditory spectrogram. These results demonstrate the efficacy of deep learning and speech synthesis algorithms for designing the next generation of speech BCI systems, which not only can restore communications for paralyzed patients but also have the potential to transform human-computer interaction technologies.","abstract_license":"CC-BY (publisher license, via OpenAlex)","license":"cc-by","is_open_access":true,"peer_reviewed":null,"categories":["EEG and Brain-Computer Interfaces"],"details":null,"provenance":"openalex+europepmc","attribution":"Abstract of \"Towards reconstructing intelligible speech from the human auditory cortex\" by Hassan Akbari, Bahar Khalighinejad, Jose L. Herrero et al., https://doi.org/10.1038/s41598-018-37359-z, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified."},{"id":"doi:10.1109/tnnls.2019.2946869","kind":"article","title":"Subject-Independent Brain–Computer Interfaces Based on Deep Convolutional Neural Networks","authors":["Oyeon Kwon","Min-Ho Lee","Cuntai Guan","Seong–Whan Lee"],"author_count":4,"year":2019,"published":"2019-11-13","venue":"IEEE Transactions on Neural Networks and Learning Systems","cited_by":351,"doi":"10.1109/tnnls.2019.2946869","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1109/tnnls.2019.2946869","archive_url":"https://space-kitty.com/papers/doi:10.1109/tnnls.2019.2946869","updated":null,"pmid":"31725394","pmcid":null,"openalex_id":"W2986984881","pdf_url":"https://ieeexplore.ieee.org/ielx7/5962385/9212662/08897723.pdf","abstract":"For a brain-computer interface (BCI) system, a calibration procedure is required for each individual user before he/she can use the BCI. This procedure requires approximately 20-30 min to collect enough data to build a reliable decoder. It is, therefore, an interesting topic to build a calibration-free, or subject-independent, BCI. In this article, we construct a large motor imagery (MI)-based electroencephalography (EEG) database and propose a subject-independent framework based on deep convolutional neural networks (CNNs). The database is composed of 54 subjects performing the left- and right-hand MI on two different days, resulting in 21 600 trials for the MI task. In our framework, we formulated the discriminative feature representation as a combination of the spectral-spatial input embedding the diversity of the EEG signals, as well as a feature representation learned from the CNN through a fusion technique that integrates a variety of discriminative brain signal patterns. To generate spectral-spatial inputs, we first consider the discriminative frequency bands in an information-theoretic observation model that measures the power of the features in two classes. From discriminative frequency bands, spectral-spatial inputs that include the unique characteristics of brain signal patterns are generated and then transformed into a covariance matrix as the input to the CNN. In the process of feature representations, spectral-spatial inputs are individually trained through the CNN and then combined by a concatenation fusion technique. In this article, we demonstrate that the classification accuracy of our subject-independent (or calibration-free) model outperforms that of subject-dependent models using various methods [common spatial pattern (CSP), common spatiospectral pattern (CSSP), filter bank CSP (FBCSP), and Bayesian spatio-spectral filter optimization (BSSFO)].","abstract_license":"CC-BY (publisher license, via OpenAlex)","license":"cc-by","is_open_access":true,"peer_reviewed":null,"categories":["EEG and Brain-Computer Interfaces"],"details":null,"provenance":"openalex+europepmc","attribution":"Abstract of \"Subject-Independent Brain–Computer Interfaces Based on Deep Convolutional Neural Networks\" by Oyeon Kwon, Min-Ho Lee, Cuntai Guan et al., https://doi.org/10.1109/tnnls.2019.2946869, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified."},{"id":"doi:10.3389/fnins.2017.00400","kind":"article","title":"Post-stroke Rehabilitation Training with a Motor-Imagery-Based Brain-Computer Interface (BCI)-Controlled Hand Exoskeleton: A Randomized Controlled Multicenter Trial","authors":["Frolov AA","Mokienko O","Lyukmanov R","Biryukova E","Kotov S","Turbina L","Nadareyshvily G","Bushkova Y"],"author_count":8,"year":2017,"published":"2017-07-20","venue":"Frontiers in neuroscience","cited_by":199,"doi":"10.3389/fnins.2017.00400","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.3389/fnins.2017.00400","archive_url":"https://space-kitty.com/papers/doi:10.3389/fnins.2017.00400","updated":null,"pmid":"28775677","pmcid":"PMC5517482","openalex_id":null,"pdf_url":"https://europepmc.org/articles/PMC5517482/pdf","abstract":"Repeated use of brain-computer interfaces (BCIs) providing contingent sensory feedback of brain activity was recently proposed as a rehabilitation approach to restore motor function after stroke or spinal cord lesions. However, there are only a few clinical studies that investigate feasibility and effectiveness of such an approach. Here we report on a placebo-controlled, multicenter clinical trial that investigated whether stroke survivors with severe upper limb (UL) paralysis benefit from 10 BCI training sessions each lasting up to 40 min. A total of 74 patients participated: median time since stroke is 8 months, 25 and 75% quartiles [3.0; 13.0]; median severity of UL paralysis is 4.5 points [0.0; 30.0] as measured by the Action Research Arm Test, ARAT, and 19.5 points [11.0; 40.0] as measured by the Fugl-Meyer Motor Assessment, FMMA. Patients in the BCI group ( n = 55) performed motor imagery of opening their affected hand. Motor imagery-related brain electroencephalographic activity was translated into contingent hand exoskeleton-driven opening movements of the affected hand. In a control group ( n = 19), hand exoskeleton-driven opening movements of the affected hand were independent of brain electroencephalographic activity. Evaluation of the UL clinical assessments indicated that both groups improved, but only the BCI group showed an improvement in the ARAT's grasp score from 0 [0.0; 14.0] to 3.0 [0.0; 15.0] points ( p < 0.01) and pinch scores from 0.0 [0.0; 7.0] to 1.0 [0.0; 12.0] points ( p < 0.01). Upon training completion, 21.8% and 36.4% of the patients in the BCI group improved their ARAT and FMMA scores respectively. The corresponding numbers for the control group were 5.1% (ARAT) and 15.8% (FMMA). These results suggests that adding BCI control to exoskeleton-assisted physical therapy can improve post-stroke rehabilitation outcomes. Both maximum and mean values of the percentage of successfully decoded imagery-related EEG activity, were higher than chance level. A correlation between the classification accuracy and the improvement in the upper extremity function was found. An improvement of motor function was found for patients with different duration, severity and location of the stroke.","abstract_license":"CC-BY (article license as recorded by Europe PMC; abstract reproduced unmodified apart from markup removal)","license":"CC-BY","is_open_access":true,"peer_reviewed":null,"categories":[],"details":null,"provenance":"europepmc","attribution":"Abstract of \"Post-stroke Rehabilitation Training with a Motor-Imagery-Based Brain-Computer Interface (BCI)-Controlled Hand Exoskeleton: A Randomized Controlled Multicenter Trial\" by Frolov AA, Mokienko O, Lyukmanov R et al., https://doi.org/10.3389/fnins.2017.00400, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified."},{"id":"doi:10.3389/fneng.2014.00030","kind":"article","title":"Brain-computer interface-based robotic end effector system for wrist and hand rehabilitation: results of a three-armed randomized controlled trial for chronic stroke","authors":["Ang KK","Guan C","Phua KS","Wang C","Zhou L","Tang KY","Ephraim Joseph GJ","Kuah CW","Chua KS"],"author_count":9,"year":2014,"published":"2014-07-29","venue":"Frontiers in neuroengineering","cited_by":194,"doi":"10.3389/fneng.2014.00030","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.3389/fneng.2014.00030","archive_url":"https://space-kitty.com/papers/doi:10.3389/fneng.2014.00030","updated":null,"pmid":"25120465","pmcid":"PMC4114185","openalex_id":null,"pdf_url":"https://europepmc.org/articles/PMC4114185/pdf","abstract":"The objective of this study was to investigate the efficacy of an Electroencephalography (EEG)-based Motor Imagery (MI) Brain-Computer Interface (BCI) coupled with a Haptic Knob (HK) robot for arm rehabilitation in stroke patients. In this three-arm, single-blind, randomized controlled trial; 21 chronic hemiplegic stroke patients (Fugl-Meyer Motor Assessment (FMMA) score 10-50), recruited after pre-screening for MI BCI ability, were randomly allocated to BCI-HK, HK or Standard Arm Therapy (SAT) groups. All groups received 18 sessions of intervention over 6 weeks, 3 sessions per week, 90 min per session. The BCI-HK group received 1 h of BCI coupled with HK intervention, and the HK group received 1 h of HK intervention per session. Both BCI-HK and HK groups received 120 trials of robot-assisted hand grasping and knob manipulation followed by 30 min of therapist-assisted arm mobilization. The SAT group received 1.5 h of therapist-assisted arm mobilization and forearm pronation-supination movements incorporating wrist control and grasp-release functions. In all, 14 males, 7 females, mean age 54.2 years, mean stroke duration 385.1 days, with baseline FMMA score 27.0 were recruited. The primary outcome measure was upper extremity FMMA scores measured mid-intervention at week 3, end-intervention at week 6, and follow-up at weeks 12 and 24. Seven, 8 and 7 subjects underwent BCI-HK, HK and SAT interventions respectively. FMMA score improved in all groups, but no intergroup differences were found at any time points. Significantly larger motor gains were observed in the BCI-HK group compared to the SAT group at weeks 3, 12, and 24, but motor gains in the HK group did not differ from the SAT group at any time point. In conclusion, BCI-HK is effective, safe, and may have the potential for enhancing motor recovery in chronic stroke when combined with therapist-assisted arm mobilization.","abstract_license":"CC-BY (article license as recorded by Europe PMC; abstract reproduced unmodified apart from markup removal)","license":"CC-BY","is_open_access":true,"peer_reviewed":null,"categories":[],"details":null,"provenance":"europepmc","attribution":"Abstract of \"Brain-computer interface-based robotic end effector system for wrist and hand rehabilitation: results of a three-armed randomized controlled trial for chronic stroke\" by Ang KK, Guan C, Phua KS et al., https://doi.org/10.3389/fneng.2014.00030, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified."},{"id":"doi:10.3389/fnbot.2020.00025","kind":"review","title":"Current Status, Challenges, and Possible Solutions of EEG-Based Brain-Computer Interface: A Comprehensive Review","authors":["Rashid M","Sulaiman N","P P Abdul Majeed A","Musa RM","Ab Nasir AF","Bari BS","Khatun S"],"author_count":7,"year":2020,"published":"2020-06-03","venue":"Frontiers in neurorobotics","cited_by":165,"doi":"10.3389/fnbot.2020.00025","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.3389/fnbot.2020.00025","archive_url":"https://space-kitty.com/papers/doi:10.3389/fnbot.2020.00025","updated":null,"pmid":"32581758","pmcid":"PMC7283463","openalex_id":null,"pdf_url":"https://europepmc.org/articles/PMC7283463/pdf","abstract":"Brain-Computer Interface (BCI), in essence, aims at controlling different assistive devices through the utilization of brain waves. It is worth noting that the application of BCI is not limited to medical applications, and hence, the research in this field has gained due attention. Moreover, the significant number of related publications over the past two decades further indicates the consistent improvements and breakthroughs that have been made in this particular field. Nonetheless, it is also worth mentioning that with these improvements, new challenges are constantly discovered. This article provides a comprehensive review of the state-of-the-art of a complete BCI system. First, a brief overview of electroencephalogram (EEG)-based BCI systems is given. Secondly, a considerable number of popular BCI applications are reviewed in terms of electrophysiological control signals, feature extraction, classification algorithms, and performance evaluation metrics. Finally, the challenges to the recent BCI systems are discussed, and possible solutions to mitigate the issues are recommended.","abstract_license":"CC-BY (article license as recorded by Europe PMC; abstract reproduced unmodified apart from markup removal)","license":"CC-BY","is_open_access":true,"peer_reviewed":null,"categories":[],"details":null,"provenance":"europepmc","attribution":"Abstract of \"Current Status, Challenges, and Possible Solutions of EEG-Based Brain-Computer Interface: A Comprehensive Review\" by Rashid M, Sulaiman N, P P Abdul Majeed A et al., https://doi.org/10.3389/fnbot.2020.00025, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified."},{"id":"doi:10.3389/fnhum.2017.00469","kind":"review","title":"Role of the Contralesional vs. Ipsilesional Hemisphere in Stroke Recovery","authors":["Dodd KC","Nair VA","Prabhakaran V"],"author_count":3,"year":2017,"published":"2017-09-21","venue":"Frontiers in human neuroscience","cited_by":168,"doi":"10.3389/fnhum.2017.00469","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.3389/fnhum.2017.00469","archive_url":"https://space-kitty.com/papers/doi:10.3389/fnhum.2017.00469","updated":null,"pmid":"28983244","pmcid":"PMC5613154","openalex_id":null,"pdf_url":"https://europepmc.org/articles/PMC5613154/pdf","abstract":"Following a stroke, the resulting lesion creates contralateral motor impairment and an interhemispheric imbalance involving hyperexcitability of the contralesional hemisphere. Neuronal reorganization may occur on both the ipsilesional and contralesional hemispheres during recovery to regain motor functionality and therefore bilateral activation for the hemiparetic side is often observed. Although ipsilesional hemispheric reorganization is traditionally thought to be most important for successful recovery, definitive conclusions into the role and importance of the contralesional motor cortex remain under debate. Through examining recent research in functional neuroimaging investigating motor cortex changes post-stroke, as well as brain-computer interface (BCI) and transcranial magnetic stimulation (TMS) therapies, this review attempts to clarify the contributions of each hemisphere toward recovery. Several functional magnetic resonance imaging studies suggest that continuation of contralesional hemisphere hyperexcitability correlates with lesser recovery, however a subset of well-recovered patients demonstrate contralesional motor activity and show decreased functional capability when the contralesional hemisphere is inhibited. BCI therapy may beneficially activate either the contralesional or ipsilesional hemisphere, depending on the study design, for chronic stroke patients who are otherwise at a functional plateau. Repetitive TMS used to excite the ipsilesional motor cortex or inhibit the contralesional hemisphere has shown promise in enhancing stroke patients' recovery.","abstract_license":"CC-BY (article license as recorded by Europe PMC; abstract reproduced unmodified apart from markup removal)","license":"CC-BY","is_open_access":true,"peer_reviewed":null,"categories":[],"details":null,"provenance":"europepmc","attribution":"Abstract of \"Role of the Contralesional vs. Ipsilesional Hemisphere in Stroke Recovery\" by Dodd KC, Nair VA, Prabhakaran V, https://doi.org/10.3389/fnhum.2017.00469, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified."},{"id":"doi:10.3390/brainsci8040057","kind":"review","title":"Brain-Computer Interface Spellers: A Review","authors":["Rezeika A","Benda M","Stawicki P","Gembler F","Saboor A","Volosyak I"],"author_count":6,"year":2018,"published":"2018-03-30","venue":"Brain sciences","cited_by":166,"doi":"10.3390/brainsci8040057","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.3390/brainsci8040057","archive_url":"https://space-kitty.com/papers/doi:10.3390/brainsci8040057","updated":null,"pmid":"29601538","pmcid":"PMC5924393","openalex_id":null,"pdf_url":"https://europepmc.org/articles/PMC5924393/pdf","abstract":"A Brain-Computer Interface (BCI) provides a novel non-muscular communication method via brain signals. A BCI-speller can be considered as one of the first published BCI applications and has opened the gate for many advances in the field. Although many BCI-spellers have been developed during the last few decades, to our knowledge, no reviews have described the different spellers proposed and studied in this vital field. The presented speller systems are categorized according to major BCI paradigms: P300, steady-state visual evoked potential (SSVEP), and motor imagery (MI). Different BCI paradigms require specific electroencephalogram (EEG) signal features and lead to the development of appropriate Graphical User Interfaces (GUIs). The purpose of this review is to consolidate the most successful BCI-spellers published since 2010, while mentioning some other older systems which were built explicitly for spelling purposes. We aim to assist researchers and concerned individuals in the field by illustrating the highlights of different spellers and presenting them in one review. It is almost impossible to carry out an objective comparison between different spellers, as each has its variables, parameters, and conditions. However, the gathered information and the provided taxonomy about different BCI-spellers can be helpful, as it could identify suitable systems for first-hand users, as well as opportunities of development and learning from previous studies for BCI researchers.","abstract_license":"CC-BY (article license as recorded by Europe PMC; abstract reproduced unmodified apart from markup removal)","license":"CC-BY","is_open_access":true,"peer_reviewed":null,"categories":[],"details":null,"provenance":"europepmc","attribution":"Abstract of \"Brain-Computer Interface Spellers: A Review\" by Rezeika A, Benda M, Stawicki P et al., https://doi.org/10.3390/brainsci8040057, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified."},{"id":"doi:10.1161/strokeaha.116.016304","kind":"article","title":"Contralesional Brain-Computer Interface Control of a Powered Exoskeleton for Motor Recovery in Chronic Stroke Survivors","authors":["Bundy DT","Souders L","Baranyai K","Leonard L","Schalk G","Coker R","Moran DW","Huskey T","Leuthardt EC"],"author_count":9,"year":2017,"published":"2017-05-26","venue":"Stroke","cited_by":162,"doi":"10.1161/strokeaha.116.016304","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1161/strokeaha.116.016304","archive_url":"https://space-kitty.com/papers/doi:10.1161/strokeaha.116.016304","updated":null,"pmid":"28550098","pmcid":"PMC5482564","openalex_id":null,"pdf_url":"https://europepmc.org/articles/PMC5482564/pdf","abstract":"Background and purpose There are few effective therapies to achieve functional recovery from motor-related disabilities affecting the upper limb after stroke. This feasibility study tested whether a powered exoskeleton driven by a brain-computer interface (BCI), using neural activity from the unaffected cortical hemisphere, could affect motor recovery in chronic hemiparetic stroke survivors. This novel system was designed and configured for a home-based setting to test the feasibility of BCI-driven neurorehabilitation in outpatient environments. Methods Ten chronic hemiparetic stroke survivors with moderate-to-severe upper-limb motor impairment (mean Action Research Arm Test=13.4) used a powered exoskeleton that opened and closed the affected hand using spectral power from electroencephalographic signals from the unaffected hemisphere associated with imagined hand movements of the paretic limb. Patients used the system at home for 12 weeks. Motor function was evaluated before, during, and after the treatment. Results Across patients, our BCI-driven approach resulted in a statistically significant average increase of 6.2 points in the Action Research Arm Test. This behavioral improvement significantly correlated with improvements in BCI control. Secondary outcomes of grasp strength, Motricity Index, and the Canadian Occupational Performance Measure also significantly improved. Conclusions The findings demonstrate the therapeutic potential of a BCI-driven neurorehabilitation approach using the unaffected hemisphere in this uncontrolled sample of chronic stroke survivors. They also demonstrate that BCI-driven neurorehabilitation can be effectively delivered in the home environment, thus increasing the probability of future clinical translation. Clinical trial registration URL: http://www.clinicaltrials.gov. Unique identifier: NCT02552368.","abstract_license":"CC-BY (article license as recorded by Europe PMC; abstract reproduced unmodified apart from markup removal)","license":"CC-BY","is_open_access":true,"peer_reviewed":null,"categories":[],"details":null,"provenance":"europepmc","attribution":"Abstract of \"Contralesional Brain-Computer Interface Control of a Powered Exoskeleton for Motor Recovery in Chronic Stroke Survivors\" by Bundy DT, Souders L, Baranyai K et al., https://doi.org/10.1161/strokeaha.116.016304, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified."},{"id":"doi:10.3389/fncom.2019.00087","kind":"review","title":"Intra- and Inter-subject Variability in EEG-Based Sensorimotor Brain Computer Interface: A Review","authors":["Saha S","Baumert M"],"author_count":2,"year":2019,"published":"2020-01-21","venue":"Frontiers in computational neuroscience","cited_by":146,"doi":"10.3389/fncom.2019.00087","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.3389/fncom.2019.00087","archive_url":"https://space-kitty.com/papers/doi:10.3389/fncom.2019.00087","updated":null,"pmid":"32038208","pmcid":"PMC6985367","openalex_id":null,"pdf_url":"https://europepmc.org/articles/PMC6985367/pdf","abstract":"Brain computer interfaces (BCI) for the rehabilitation of motor impairments exploit sensorimotor rhythms (SMR) in the electroencephalogram (EEG). However, the neurophysiological processes underpinning the SMR often vary over time and across subjects. Inherent intra- and inter-subject variability causes covariate shift in data distributions that impede the transferability of model parameters amongst sessions/subjects. Transfer learning includes machine learning-based methods to compensate for inter-subject and inter-session (intra-subject) variability manifested in EEG-derived feature distributions as a covariate shift for BCI. Besides transfer learning approaches, recent studies have explored psychological and neurophysiological predictors as well as inter-subject associativity assessment, which may augment transfer learning in EEG-based BCI. Here, we highlight the importance of measuring inter-session/subject performance predictors for generalized BCI frameworks for both normal and motor-impaired people, reducing the necessity for tedious and annoying calibration sessions and BCI training.","abstract_license":"CC-BY (article license as recorded by Europe PMC; abstract reproduced unmodified apart from markup removal)","license":"CC-BY","is_open_access":true,"peer_reviewed":null,"categories":[],"details":null,"provenance":"europepmc","attribution":"Abstract of \"Intra- and Inter-subject Variability in EEG-Based Sensorimotor Brain Computer Interface: A Review\" by Saha S, Baumert M, https://doi.org/10.3389/fncom.2019.00087, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified."},{"id":"doi:10.1093/gigascience/gix034","kind":"article","title":"EEG datasets for motor imagery brain-computer interface","authors":["Cho H","Ahn M","Ahn S","Kwon M","Jun SC"],"author_count":5,"year":2017,"published":"2017-07-01","venue":"GigaScience","cited_by":151,"doi":"10.1093/gigascience/gix034","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1093/gigascience/gix034","archive_url":"https://space-kitty.com/papers/doi:10.1093/gigascience/gix034","updated":null,"pmid":"28472337","pmcid":"PMC5493744","openalex_id":null,"pdf_url":"https://europepmc.org/articles/PMC5493744/pdf","abstract":"Background Most investigators of brain-computer interface (BCI) research believe that BCI can be achieved through induced neuronal activity from the cortex, but not by evoked neuronal activity. Motor imagery (MI)-based BCI is one of the standard concepts of BCI, in that the user can generate induced activity by imagining motor movements. However, variations in performance over sessions and subjects are too severe to overcome easily; therefore, a basic understanding and investigation of BCI performance variation is necessary to find critical evidence of performance variation. Here we present not only EEG datasets for MI BCI from 52 subjects, but also the results of a psychological and physiological questionnaire, EMG datasets, the locations of 3D EEG electrodes, and EEGs for non-task-related states. Findings We validated our EEG datasets by using the percentage of bad trials, event-related desynchronization/synchronization (ERD/ERS) analysis, and classification analysis. After conventional rejection of bad trials, we showed contralateral ERD and ipsilateral ERS in the somatosensory area, which are well-known patterns of MI. Finally, we showed that 73.08% of datasets (38 subjects) included reasonably discriminative information. Conclusions Our EEG datasets included the information necessary to determine statistical significance; they consisted of well-discriminated datasets (38 subjects) and less-discriminative datasets. These may provide researchers with opportunities to investigate human factors related to MI BCI performance variation, and may also achieve subject-to-subject transfer by using metadata, including a questionnaire, EEG coordinates, and EEGs for non-task-related states.","abstract_license":"CC-BY (article license as recorded by Europe PMC; abstract reproduced unmodified apart from markup removal)","license":"CC-BY","is_open_access":true,"peer_reviewed":null,"categories":[],"details":null,"provenance":"europepmc","attribution":"Abstract of \"EEG datasets for motor imagery brain-computer interface\" by Cho H, Ahn M, Ahn S et al., https://doi.org/10.1093/gigascience/gix034, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified."},{"id":"doi:10.1186/s12984-020-00686-2","kind":"review","title":"Immediate and long-term effects of BCI-based rehabilitation of the upper extremity after stroke: a systematic review and meta-analysis","authors":["Bai Z","Fong KNK","Zhang JJ","Chan J","Ting KH"],"author_count":5,"year":2020,"published":"2020-04-25","venue":"Journal of neuroengineering and rehabilitation","cited_by":138,"doi":"10.1186/s12984-020-00686-2","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1186/s12984-020-00686-2","archive_url":"https://space-kitty.com/papers/doi:10.1186/s12984-020-00686-2","updated":null,"pmid":"32334608","pmcid":"PMC7183617","openalex_id":null,"pdf_url":"https://europepmc.org/articles/PMC7183617/pdf","abstract":"Background A substantial number of clinical studies have demonstrated the functional recovery induced by the use of brain-computer interface (BCI) technology in patients after stroke. The objective of this review is to evaluate the effect sizes of clinical studies investigating the use of BCIs in restoring upper extremity function after stroke and the potentiating effect of transcranial direct current stimulation (tDCS) on BCI training for motor recovery. Methods The databases (PubMed, Medline, EMBASE, CINAHL, CENTRAL, PsycINFO, and PEDro) were systematically searched for eligible single-group or clinical controlled studies regarding the effects of BCIs in hemiparetic upper extremity recovery after stroke. Single-group studies were qualitatively described, but only controlled-trial studies were included in the meta-analysis. The PEDro scale was used to assess the methodological quality of the controlled studies. A meta-analysis of upper extremity function was performed by pooling the standardized mean difference (SMD). Subgroup meta-analyses regarding the use of external devices in combination with the application of BCIs were also carried out. We summarized the neural mechanism of the use of BCIs on stroke. Results A total of 1015 records were screened. Eighteen single-group studies and 15 controlled studies were included. The studies showed that BCIs seem to be safe for patients with stroke. The single-group studies consistently showed a trend that suggested BCIs were effective in improving upper extremity function. The meta-analysis (of 12 studies) showed a medium effect size favoring BCIs for improving upper extremity function after intervention (SMD = 0.42; 95% CI = 0.18-0.66; I 2 = 48%; P < 0.001; fixed-effects model), while the long-term effect (five studies) was not significant (SMD = 0.12; 95% CI = - 0.28 - 0.52; I 2 = 0%; P = 0.540; fixed-effects model). A subgroup meta-analysis indicated that using functional electrical stimulation as the external device in BCI training was more effective than using other devices (P = 0.010). Using movement attempts as the trigger task in BCI training appears to be more effective than using motor imagery (P = 0.070). The use of tDCS (two studies) could not further facilitate the effects of BCI training to restore upper extremity motor function (SMD = - 0.30; 95% CI = - 0.96 - 0.36; I 2 = 0%; P = 0.370; fixed-effects model). Conclusion The use of BCIs has significant immediate effects on the improvement of hemiparetic upper extremity function in patients after stroke, but the limited number of studies does not support its long-term effects. BCIs combined with functional electrical stimulation may be a better combination for functional recovery than other kinds of neural feedback. The mechanism for functional recovery may be attributed to the activation of the ipsilesional premotor and sensorimotor cortical network.","abstract_license":"CC-BY (article license as recorded by Europe PMC; abstract reproduced unmodified apart from markup removal)","license":"CC-BY","is_open_access":true,"peer_reviewed":null,"categories":[],"details":null,"provenance":"europepmc","attribution":"Abstract of \"Immediate and long-term effects of BCI-based rehabilitation of the upper extremity after stroke: a systematic review and meta-analysis\" by Bai Z, Fong KNK, Zhang JJ et al., https://doi.org/10.1186/s12984-020-00686-2, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified."}],"next":"https://space-kitty.com/api/v1/topics/brain-computer-interfaces/papers?sort=top&limit=25&offset=25"}