03835nas a2200301   4500008004100000022001400041245015300055210006900208260001200277520274700289653005303036653004003089653003503129653003403164653003303198653003503231100002003266700002203286700002603308700002103334700001903355700002103374700001903395700002603414700002103440700002403461856004803485       2015                        eng d  a1872-895200aP300-based brain-computer interface (BCI) event-related potentials (ERPs): People with amyotrophic lateral sclerosis (ALS) vs. age-matched controls.0 aP300based braincomputer interface BCI eventrelated potentials ER  c02/20153 a<p><b>OBJECTIVE: </b>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.</p><p><b>METHODS: </b>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.</p><p><b>RESULTS: </b>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).</p><p><b>CONCLUSIONS: </b>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).</p><p><b>SIGNIFICANCE: </b>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.</p>10aalternative and augmentative communication (AAC)10aamyotrophic lateral sclerosis (ALS)10aBrain-computer interface (BCI)10abrain-machine interface (BMI)10aelectroencephalography (EEG)10aevent-related potentials (ERP)1 aMcCane, Lynn, M1 aHeckman, Susan, M1 aMcFarland, Dennis, J.1 aTownsend, George1 aMak, Joseph, N1 aSellers, Eric, W1 aZeitlin, Debra1 aTenteromano, Laura, M1 aWolpaw, Jonathan1 aVaughan, Theresa, M  uhttp://www.ncbi.nlm.nih.gov/pubmed/2570394001200nas a2200229   4500008004100000022001400041245009400055210007100149260001200220300001400232490000700246520047400253653003500727653001500762653002200777100002600799700002600825700002600851700002000877700002500897856004800922       2006                        eng d  a1534-432000aBCI Meeting 2005–workshop on BCI signal processing: feature extraction and translation.0 aBCI Meeting 2005–workshop on BCI signal processing feature extra  c06/2006  a135–1380 v143 aThis 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.10aBrain-computer interface (BCI)10aprediction10aSignal Processing1 aMcFarland, Dennis, J.1 aAnderson, Charles, W.1 aMüller, Klaus-Robert1 aSchlögl, Alois1 aKrusienski, Dean, J.  uhttp://www.ncbi.nlm.nih.gov/pubmed/1679227802353nas a2200229   4500008004100000022001400041245008900055210006900144260001200213300001400225490000700239520162900246653003101875653003501906653002701941653001301968100002301981700002602004700002102030700002402051856004802075       2004                        eng d  a1534-432000aConversion of EEG activity into cursor movement by a brain-computer interface (BCI).0 aConversion of EEG activity into cursor movement by a braincomput  c09/2004  a331–3380 v123 aThe 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.10aaugmentative communication10aBrain-computer interface (BCI)10aElectroencephalography10aFeedback1 aFabiani, Georg, E.1 aMcFarland, Dennis, J.1 aWolpaw, Jonathan1 aPfurtscheller, Gert  uhttp://www.ncbi.nlm.nih.gov/pubmed/1547319502866nas a2200289   4500008004100000022001400041245008600055210006900141260001200210300001400222490000600236520197100242653003102213653003502244653003302279100002102312700002102333700002202354700002602376700002002402700001902422700002102441700002102462700002102483700002402504856004802528       2000                        eng d  a1063-652800aBrain-computer interface technology: a review of the first international meeting.0 aBraincomputer interface technology a review of the first interna  c06/2000  a164–1730 v83 aOver 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.10aaugmentative communication10aBrain-computer interface (BCI)10aelectroencephalography (EEG)1 aWolpaw, Jonathan1 aBirbaumer, Niels1 aHeetderks, W., J.1 aMcFarland, Dennis, J.1 aPeckham, P., H.1 aSchalk, Gerwin1 aDonchin, Emanuel1 aQuatrano, L., A.1 aRobinson, C., J.1 aVaughan, Theresa, M  uhttp://www.ncbi.nlm.nih.gov/pubmed/10896178