@article {3381, title = {P300-based brain-computer interface (BCI) event-related potentials (ERPs): People with amyotrophic lateral sclerosis (ALS) vs. age-matched controls.}, journal = {Clin Neurophysiol}, year = {2015}, month = {02/2015}, abstract = {

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 ({\textpm}9.5SD) (range 0-25)). Each subject attended to a target item as the items in a 6{\texttimes}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.

}, keywords = {alternative and augmentative communication (AAC), amyotrophic lateral sclerosis (ALS), Brain-computer interface (BCI), brain-machine interface (BMI), electroencephalography (EEG), event-related potentials (ERP)}, issn = {1872-8952}, doi = {10.1016/j.clinph.2015.01.013}, url = {http://www.ncbi.nlm.nih.gov/pubmed/25703940}, author = {McCane, Lynn M and Susan M Heckman and Dennis J. McFarland and Townsend, George and Mak, Joseph N and Sellers, Eric W and Zeitlin, Debra and Tenteromano, Laura M and Jonathan Wolpaw and Theresa M Vaughan} } @article {3216, title = {BCI Meeting 2005{\textendash}workshop on signals and recording methods.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {14}, year = {2006}, month = {06/2006}, pages = {138{\textendash}141}, abstract = {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.}, keywords = {Brain-computer interface (BCI), electrophysiological signals, Rehabilitation}, issn = {1534-4320}, doi = {10.1109/TNSRE.2006.875583}, url = {http://www.ncbi.nlm.nih.gov/pubmed/16792279}, author = {Jonathan Wolpaw and Loeb, Gerald E. and Brendan Z. Allison and Emanuel Donchin and do Nascimento, Omar Feix and Heetderks, William J. and Nijboer, Femke and Shain, William G. and Turner, James N.} } @article {3140, title = {Conversion of EEG activity into cursor movement by a brain-computer interface (BCI).}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {12}, year = {2004}, month = {09/2004}, pages = {331{\textendash}338}, abstract = {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.}, keywords = {augmentative communication, Brain-computer interface (BCI), Electroencephalography, Feedback}, issn = {1534-4320}, doi = {10.1109/TNSRE.2004.834627}, url = {http://www.ncbi.nlm.nih.gov/pubmed/15473195}, author = {Fabiani, Georg E. and Dennis J. McFarland and Jonathan Wolpaw and Pfurtscheller, Gert} } @article {3226, title = {Brain-computer interface technology: a review of the Second International Meeting.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {11}, year = {2003}, month = {06/2003}, pages = {94{\textendash}109}, abstract = {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.}, keywords = {augmentative communication, Brain-computer interface (BCI), electroencephalography (EEG), Rehabilitation}, issn = {1534-4320}, doi = {10.1109/TNSRE.2003.814799}, url = {http://www.ncbi.nlm.nih.gov/pubmed/12899247}, author = {Theresa M Vaughan and Heetderks, William J. and Trejo, Leonard J. and Rymer, William Z. and Weinrich, Michael and Moore, Melody M. and K{\"u}bler, Andrea and Dobkin, Bruce H. and Niels Birbaumer and Emanuel Donchin and Wolpaw, Elizabeth Winter and Jonathan Wolpaw} } @article {3236, title = {Brain-computer interface technology: a review of the first international meeting.}, journal = {IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {8}, year = {2000}, month = {06/2000}, pages = {164{\textendash}173}, abstract = {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{\textquoteright}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{\textquoteright}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{\textquoteright}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.}, keywords = {augmentative communication, Brain-computer interface (BCI), electroencephalography (EEG)}, issn = {1063-6528}, doi = {10.1109/TRE.2000.847807}, url = {http://www.ncbi.nlm.nih.gov/pubmed/10896178}, author = {Jonathan Wolpaw and Niels Birbaumer and Heetderks, W. J. and Dennis J. McFarland and Peckham, P. H. and Gerwin Schalk and Emanuel Donchin and Quatrano, L. A. and Robinson, C. J. and Theresa M Vaughan} }