TY - JOUR T1 - An exploration of BCI performance variations in people with amyotrophic lateral sclerosis using longitudinal EEG data JF - Journal of Neural Engineering Y1 - 2019 A1 - Shahriari, Yalda A1 - Vaughan, Theresa A1 - McCane, Lynn A1 - Allison, Brendan A1 - Wolpaw, Jonathan A1 - Krusienski, Dean KW - amyotrophic lateral sclerosis (ALS) KW - Brain-computer interface (BCI) KW - Longitudinal Electroencephalogram (EEG) KW - P300 speller AB - Objective. Brain-computer interface (BCI) technology enables people to use direct measures of brain activity for communication and control. The National Center for Adaptive Neurotechnologies (NCAN) and Helen Hayes Hospital are studying long-term independent home use of P300-based BCIs by people with amyotrophic lateral sclerosis (ALS). This BCI use takes place without technical oversight, and users can encounter substantial variation in their day-to-day BCI performance. The purpose of this study is to identify and evaluate features in the electroencephalogram (EEG) that correlate with successful BCI performance during home use with the goal of improving BCI for people with neuromuscular disorders. Approach. Nine people with ALS used a P300-based BCI at home over several months for communication and computer control. Sessions from a routine calibration task were categorized as successful (≥70%) or unsuccessful (<70%) BCI performance. The correlation of temporal and spectral EEG features with BCI performance was then evaluated. Main Results. BCI performance was positively correlated with an increase in alpha-band (8-14 Hz) activity at locations PO8, P3, Pz, and P4; and beta-band (15-30 Hz) activity at occipital locations. In addition, performance was significantly positively correlated with a positive deflection in EEG amplitude around 220 ms at frontal mid-line locations (i.e., Fz and Cz). BCI performance was negatively correlated with delta-band (1-3 Hz) activity recorded from occipital locations. Significance. These results highlight the variability found in the EEG and describe EEG features that correlate with successful BCI performance during day-to-day use of a P300-based BCI by people with ALS. These results should inform studies focused on improved BCI reliability for people with neuromuscular disorders. UR - https://iopscience.iop.org/article/10.1088/1741-2552/ab22ea ER - TY - JOUR T1 - Identifying the Attended Speaker Using Electrocorticographic (ECoG) Signals. JF - Journal of Neural Engineering Y1 - 2015 A1 - Dijkstra, K. A1 - Peter Brunner A1 - Gunduz, Aysegul A1 - Coon, W.G. A1 - A L Ritaccio A1 - Farquhar, Jason A1 - Gerwin Schalk KW - auditory attention KW - Brain-computer interface (BCI) KW - Cocktail Party KW - electrocorticography (ECoG) AB - People affected by severe neuro-degenerative diseases (e.g., late-stage amyotrophic lateral sclerosis (ALS) or locked-in syndrome) eventually lose all muscular control. Thus, they cannot use traditional assistive communication devices that depend on muscle control, or brain-computer interfaces (BCIs) that depend on the ability to control gaze. While auditory and tactile BCIs can provide communication to such individuals, their use typically entails an artificial mapping between the stimulus and the communication intent. This makes these BCIs difficult to learn and use. In this study, we investigated the use of selective auditory attention to natural speech as an avenue for BCI communication. In this approach, the user communicates by directing his/her attention to one of two simultaneously presented speakers. We used electrocorticographic (ECoG) signals in the gamma band (70–170 Hz) to infer the identity of attended speaker, thereby removing the need to learn such an artificial mapping. Our results from twelve human subjects show that a single cortical location over superior temporal gyrus or pre-motor cortex is typically sufficient to identify the attended speaker within 10 s and with 77% accuracy (50% accuracy due to chance). These results lay the groundwork for future studies that may determine the real-time performance of BCIs based on selective auditory attention to speech. UR - https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4776341/ ER - TY - JOUR T1 - P300-based brain-computer interface (BCI) event-related potentials (ERPs): People with amyotrophic lateral sclerosis (ALS) vs. age-matched controls. JF - Clin Neurophysiol Y1 - 2015 A1 - McCane, Lynn M A1 - Susan M Heckman A1 - Dennis J. McFarland A1 - Townsend, George A1 - Mak, Joseph N A1 - Sellers, Eric W A1 - Zeitlin, Debra A1 - Tenteromano, Laura M A1 - Jonathan Wolpaw A1 - Theresa M Vaughan KW - alternative and augmentative communication (AAC) KW - amyotrophic lateral sclerosis (ALS) KW - Brain-computer interface (BCI) KW - brain-machine interface (BMI) KW - electroencephalography (EEG) KW - event-related potentials (ERP) AB -

OBJECTIVE: Brain-computer interfaces (BCIs) aimed at restoring communication to people with severe neuromuscular disabilities often use event-related potentials (ERPs) in scalp-recorded EEG activity. Up to the present, most research and development in this area has been done in the laboratory with young healthy control subjects. In order to facilitate the development of BCI most useful to people with disabilities, the present study set out to: (1) determine whether people with amyotrophic lateral sclerosis (ALS) and healthy, age-matched volunteers (HVs) differ in the speed and accuracy of their ERP-based BCI use; (2) compare the ERP characteristics of these two groups; and (3) identify ERP-related factors that might enable improvement in BCI performance for people with disabilities.

METHODS: Sixteen EEG channels were recorded while people with ALS or healthy age-matched volunteers (HVs) used a P300-based BCI. The subjects with ALS had little or no remaining useful motor control (mean ALS Functional Rating Scale-Revised 9.4 (±9.5SD) (range 0-25)). Each subject attended to a target item as the items in a 6×6 visual matrix flashed. The BCI used a stepwise linear discriminant function (SWLDA) to determine the item the user wished to select (i.e., the target item). Offline analyses assessed the latencies, amplitudes, and locations of ERPs to the target and non-target items for people with ALS and age-matched control subjects.

RESULTS: BCI accuracy and communication rate did not differ significantly between ALS users and HVs. Although ERP morphology was similar for the two groups, their target ERPs differed significantly in the location and amplitude of the late positivity (P300), the amplitude of the early negativity (N200), and the latency of the late negativity (LN).

CONCLUSIONS: The differences in target ERP components between people with ALS and age-matched HVs are consistent with the growing recognition that ALS may affect cortical function. The development of BCIs for use by this population may begin with studies in HVs but also needs to include studies in people with ALS. Their differences in ERP components may affect the selection of electrode montages, and might also affect the selection of presentation parameters (e.g., matrix design, stimulation rate).

SIGNIFICANCE: P300-based BCI performance in people severely disabled by ALS is similar to that of age-matched control subjects. At the same time, their ERP components differ to some degree from those of controls. Attention to these differences could contribute to the development of BCIs useful to those with ALS and possibly to others with severe neuromuscular disabilities.

UR - http://www.ncbi.nlm.nih.gov/pubmed/25703940 ER - TY - JOUR T1 - The Plurality of Human Brain-Computer Interfacing. JF - Proceedings of the IEEE Y1 - 2015 A1 - Mueller-Putz, G. A1 - Millán, José del R A1 - Gerwin Schalk A1 - Mueller, K.R. KW - Brain-computer interface (BCI) AB - The articles in this special issue focus on brain-computer interfacing. The papers are dedicated to this growing and diversifying research enterprise, and features important review articles as well as some important current examples of research in this area. The field of brain-computer interface (BCI) research began to develop about 25 years ago and transformed from initially isolated demonstrations by a few groups into a large scientific enterprise that is currently producing hundreds of peer-reviewed articles and several dedicated conferences and workshops each year. This level of productivity is reflective of the large and continually growing enthusiasm by the scientific community, funding agencies, and the public. UR - http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7115302 ER - TY - JOUR T1 - Real-Time Functional Mapping with Electrocorticography in Pediatric Epilepsy: Comparison with fMRI and ESM Findings. JF - Clinical EEG and neuroscience Y1 - 2014 A1 - Korostenskaja, Milena A1 - Adam J Wilson A1 - Rose, Douglas F A1 - Peter Brunner A1 - Gerwin Schalk A1 - Leach, James A1 - Mangano, Francesco T A1 - Fujiwara, Hisako A1 - Rozhkov, Leonid A1 - Harris, Elana A1 - Chen, Po-Ching A1 - Seo, Joo-Hee A1 - Lee, Ki H KW - Brain-computer interface (BCI) KW - cortical stimulation KW - electrocorticography (ECoG) KW - epilepsy surgery KW - functional magnetic resonance imaging (fMRI) KW - functional mapping KW - pediatrics KW - SIGFRIED AB - SIGFRIED (SIGnal modeling For Real-time Identification and Event Detection) software provides real-time functional mapping (RTFM) of eloquent cortex for epilepsy patients preparing to undergo resective surgery. This study presents the first application of paradigms used in functional magnetic resonance (fMRI) and electrical cortical stimulation mapping (ESM) studies for shared functional cortical mapping in the context of RTFM. Results from the 3 modalities are compared. A left-handed 13-year-old male with intractable epilepsy participated in functional mapping for localization of eloquent language cortex with fMRI, ESM, and RTFM. For RTFM, data were acquired over the frontal and temporal cortex. Several paradigms were sequentially presented: passive (listening to stories) and active (picture naming and verb generation). For verb generation and story processing, fMRI showed atypical right lateralizing language activation within temporal lobe regions of interest and bilateral frontal activation with slight right lateralization. Left hemisphere ESM demonstrated no eloquent language areas. RTFM procedures using story processing and picture naming elicited activity in the right lateral and basal temporal regions. Verb generation elicited strong right lateral temporal lobe activation, as well as left frontal lobe activation. RTFM results confirmed atypical language lateralization evident from fMRI and ESM. We demonstrated the feasibility and usefulness of a new RTFM stimulation paradigm during presurgical evaluation. Block design paradigms used in fMRI may be optimal for this purpose. Further development is needed to create age-appropriate RTFM test batteries. UR - http://www.ncbi.nlm.nih.gov/pubmed/24293161 ER - TY - JOUR T1 - Brain-computer interfaces using electrocorticographic signals. JF - IEEE Rev Biomed Eng Y1 - 2011 A1 - Gerwin Schalk A1 - Leuthardt, E C KW - Brain-computer interface (BCI) KW - brain-machine interface (BMI) KW - electrocorticography (ECoG) AB -

Many studies over the past two decades have shown that people and animals can use brain signals to convey their intent to a computer using brain-computer interfaces (BCIs). BCI systems measure specific features of brain activity and translate them into control signals that drive an output. The sensor modalities that have most commonly been used in BCI studies have been electroencephalographic (EEG) recordings from the scalp and single-neuron recordings from within the cortex. Over the past decade, an increasing number of studies has explored the use of electrocorticographic (ECoG) activity recorded directly from the surface of the brain. ECoG has attracted substantial and increasing interest, because it has been shown to reflect specific details of actual and imagined actions, and because its technical characteristics should readily support robust and chronic implementations of BCI systems in humans. This review provides general perspectives on the ECoG platform; describes the different electrophysiological features that can be detected in ECoG; elaborates on the signal acquisition issues, protocols, and online performance of ECoG-based BCI studies to date; presents important limitations of current ECoG studies; discusses opportunities for further research; and finally presents a vision for eventual clinical implementation. In summary, the studies presented to date strongly encourage further research using the ECoG platform for basic neuroscientific research, as well as for translational neuroprosthetic applications.

VL - 4 UR - http://www.ncbi.nlm.nih.gov/pubmed/22273796 ER - TY - JOUR T1 - BCI Meeting 2005–workshop on BCI signal processing: feature extraction and translation. JF - IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society Y1 - 2006 A1 - Dennis J. McFarland A1 - Anderson, Charles W. A1 - Müller, Klaus-Robert A1 - Schlögl, Alois A1 - Krusienski, Dean J. KW - Brain-computer interface (BCI) KW - prediction KW - Signal Processing AB - This paper describes the outcome of discussions held during the Third International BCI Meeting at a workshop charged with reviewing and evaluating the current state of and issues relevant to brain-computer interface (BCI) feature extraction and translation. The issues discussed include a taxonomy of methods and applications, time-frequency spatial analysis, optimization schemes, the role of insight in analysis, adaptation, and methods for quantifying BCI feedback. VL - 14 UR - http://www.ncbi.nlm.nih.gov/pubmed/16792278 ER - TY - JOUR T1 - BCI Meeting 2005–workshop on signals and recording methods. JF - IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society Y1 - 2006 A1 - Jonathan Wolpaw A1 - Loeb, Gerald E. A1 - Brendan Z. Allison A1 - Emanuel Donchin A1 - do Nascimento, Omar Feix A1 - Heetderks, William J. A1 - Nijboer, Femke A1 - Shain, William G. A1 - Turner, James N. KW - Brain-computer interface (BCI) KW - electrophysiological signals KW - Rehabilitation AB - This paper describes the highlights of presentations and discussions during the Third International BCI Meeting in a workshop that evaluated potential brain-computer interface (BCI) signals and currently available recording methods. It defined the main potential user populations and their needs, addressed the relative advantages and disadvantages of noninvasive and implanted (i.e., invasive) methodologies, considered ethical issues, and focused on the challenges involved in translating BCI systems from the laboratory to widespread clinical use. The workshop stressed the critical importance of developing useful applications that establish the practical value of BCI technology. VL - 14 UR - http://www.ncbi.nlm.nih.gov/pubmed/16792279 ER - TY - JOUR T1 - Conversion of EEG activity into cursor movement by a brain-computer interface (BCI). JF - IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society Y1 - 2004 A1 - Fabiani, Georg E. A1 - Dennis J. McFarland A1 - Jonathan Wolpaw A1 - Pfurtscheller, Gert KW - augmentative communication KW - Brain-computer interface (BCI) KW - Electroencephalography KW - Feedback AB - The Wadsworth electroencephalogram (EEG)-based brain-computer interface (BCI) uses amplitude in mu or beta frequency bands over sensorimotor cortex to control cursor movement. Trained users can move the cursor in one or two dimensions. The primary goal of this research is to provide a new communication and control option for people with severe motor disabilities. Currently, cursor movements in each dimension are determined 10 times/s by an empirically derived linear function of one or two EEG features (i.e., spectral bands from different electrode locations). This study used offline analysis of data collected during system operation to explore methods for improving the accuracy of cursor movement. The data were gathered while users selected among three possible targets by controlling vertical [i.e., one-dimensional (1-D)] cursor movement. The three methods analyzed differ in the dimensionality of the cursor movement [1-D versus two-dimensional (2-D)] and in the type of the underlying function (linear versus nonlinear). We addressed two questions: Which method is best for classification (i.e., to determine from the EEG which target the user wants to hit)? How does the number of EEG features affect the performance of each method? All methods reached their optimal performance with 10-20 features. In offline simulation, the 2-D linear method and the 1-D nonlinear method improved performance significantly over the 1-D linear method. The 1-D linear method did not do so. These offline results suggest that the 1-D nonlinear or the 2-D linear cursor function will improve online operation of the BCI system. VL - 12 UR - http://www.ncbi.nlm.nih.gov/pubmed/15473195 ER - TY - JOUR T1 - Brain-computer interface technology: a review of the Second International Meeting. JF - IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society Y1 - 2003 A1 - Theresa M Vaughan A1 - Heetderks, William J. A1 - Trejo, Leonard J. A1 - Rymer, William Z. A1 - Weinrich, Michael A1 - Moore, Melody M. A1 - Kübler, Andrea A1 - Dobkin, Bruce H. A1 - Niels Birbaumer A1 - Emanuel Donchin A1 - Wolpaw, Elizabeth Winter A1 - Jonathan Wolpaw KW - augmentative communication KW - Brain-computer interface (BCI) KW - electroencephalography (EEG) KW - Rehabilitation AB - This paper summarizes the Brain-Computer Interfaces for Communication and Control, The Second International Meeting, held in Rensselaerville, NY, in June 2002. Sponsored by the National Institutes of Health and organized by the Wadsworth Center of the New York State Department of Health, the meeting addressed current work and future plans in brain-computer interface (BCI) research. Ninety-two researchers representing 38 different research groups from the United States, Canada, Europe, and China participated. The BCIs discussed at the meeting use electroencephalographic activity recorded from the scalp or single-neuron activity recorded within cortex to control cursor movement, select letters or icons, or operate neuroprostheses. The central element in each BCI is a translation algorithm that converts electrophysiological input from the user into output that controls external devices. BCI operation depends on effective interaction between two adaptive controllers, the user who encodes his or her commands in the electrophysiological input provided to the BCI, and the BCI that recognizes the commands contained in the input and expresses them in device control. Current BCIs have maximum information transfer rates of up to 25 b/min. Achievement of greater speed and accuracy requires improvements in signal acquisition and processing, in translation algorithms, and in user training. These improvements depend on interdisciplinary cooperation among neuroscientists, engineers, computer programmers, psychologists, and rehabilitation specialists, and on adoption and widespread application of objective criteria for evaluating alternative methods. The practical use of BCI technology will be determined by the development of appropriate applications and identification of appropriate user groups, and will require careful attention to the needs and desires of individual users. VL - 11 UR - http://www.ncbi.nlm.nih.gov/pubmed/12899247 ER - TY - JOUR T1 - Brain-computer interface technology: a review of the first international meeting. JF - IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society Y1 - 2000 A1 - Jonathan Wolpaw A1 - Niels Birbaumer A1 - Heetderks, W. J. A1 - Dennis J. McFarland A1 - Peckham, P. H. A1 - Gerwin Schalk A1 - Emanuel Donchin A1 - Quatrano, L. A. A1 - Robinson, C. J. A1 - Theresa M Vaughan KW - augmentative communication KW - Brain-computer interface (BCI) KW - electroencephalography (EEG) AB - Over the past decade, many laboratories have begun to explore brain-computer interface (BCI) technology as a radically new communication option for those with neuromuscular impairments that prevent them from using conventional augmentative communication methods. BCI's provide these users with communication channels that do not depend on peripheral nerves and muscles. This article summarizes the first international meeting devoted to BCI research and development. Current BCI's use electroencephalographic (EEG) activity recorded at the scalp or single-unit activity recorded from within cortex to control cursor movement, select letters or icons, or operate a neuroprosthesis. The central element in each BCI is a translation algorithm that converts electrophysiological input from the user into output that controls external devices. BCI operation depends on effective interaction between two adaptive controllers, the user who encodes his or her commands in the electrophysiological input provided to the BCI, and the BCI which recognizes the commands contained in the input and expresses them in device control. Current BCI's have maximum information transfer rates of 5-25 b/min. Achievement of greater speed and accuracy depends on improvements in signal processing, translation algorithms, and user training. These improvements depend on increased interdisciplinary cooperation between neuroscientists, engineers, computer programmers, psychologists, and rehabilitation specialists, and on adoption and widespread application of objective methods for evaluating alternative methods. The practical use of BCI technology depends on the development of appropriate applications, identification of appropriate user groups, and careful attention to the needs and desires of individual users. BCI research and development will also benefit from greater emphasis on peer-reviewed publications, and from adoption of standard venues for presentations and discussion. VL - 8 UR - http://www.ncbi.nlm.nih.gov/pubmed/10896178 ER -