BCI Competition 2003—Data Set IIb: Support Vector Machines for the P300 Speller Paradigm

Kaper, Matthias; Meinicke, Peter; Grossekathoefer, U.; Lingner, Thomas; Ritter, Helge

doi:10.1109/tbme.2004.826698

Cited by 430 publications

(263 citation statements)

References 8 publications

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“…These bit rates are comparable with previous online studies conducted with healthy participants (13.3 bits/ min; Serby et al, 2005) or healthy participants and wheelchair bound but otherwise healthy adults (5.5 bits/min; Donchin et al, 2000) 1 . Several previous P300-based BCI studies have presented bit rate with the time between characters selections removed (e.g., Donchin, et al, 2000;Kaper et al, 2004;Meinicke et al, 2002;Serby et al, 2005); therefore, we have presented it both ways. However, it seems that the appropriate way to report bit rate would be to include the necessary and actual time between character selections.…”

Section: Bit Ratementioning

confidence: 99%

A P300-based brain–computer interface for people with amyotrophic lateral sclerosis

Nijboer

Sellers

Mellinger

et al. 2008

Clinical Neurophysiology

553

421

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Objective-The current study evaluates the efficacy of a P300-based Brain-Computer Interface (BCI) communication device for individuals with advanced ALS.Methods-Participants attended to one cell of a N×N matrix while the N rows and N columns flashed randomly. Each cell of the matrix contained one character. Every flash of an attended character served as a rare event in an oddball sequence and elicited a P300 response. Classification coefficients derived using a stepwise linear discriminant function were applied to the data after each set of flashes. The character receiving the highest discriminant score was presented as feedback.Results-In Phase I, six participants used a 6×6 matrix on 12 separate days with a mean rate of 1.2 selections/min and mean online and offline accuracies of 62% and 82% respectively. In Phase II, four participants used either a 6×6 or a 7×7 matrix to produce novel and spontaneous statements with a mean online rate of 2.1 selections/min and online accuracy of 79%. The amplitude and latency of the P300 remained stable over 40 weeks.Conclusions-Participants could communicate with the P300-based BCI and performance was stable over many months.Significance-BCIs could provide an alternative communication and control technology in the in daily lives of people severely disabled by ALS.

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Section: Bit Ratementioning

confidence: 99%

A P300-based brain–computer interface for people with amyotrophic lateral sclerosis

Nijboer

Sellers

Mellinger

et al. 2008

Clinical Neurophysiology

553

421

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“…The latter comments probably apply to P300 BCIs as well, since they also rely on selective attention. Although P300 BCIs are considered independent 3 , this assumption has been questioned (Allison, 2003;Kaper et al, 2004).…”

Section: 1: Display Type and Gaze Shiftingmentioning

confidence: 99%

Towards an independent brain–computer interface using steady state visual evoked potentials

Allison

McFarland

Schalk

et al. 2008

Clinical Neurophysiology

285

183

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Objective-Brain -computer interface (BCI) systems using steady state visual evoked potentials (SSVEPs) have allowed healthy subjects to communicate. However, these systems may not work in severely disabled users because they may depend on gaze shifting. This study evaluates the hypothesis that overlapping stimuli can evoke changes in SSVEP activity sufficient to control a BCI. This would provide evidence that SSVEP BCIs could be used without shifting gaze.Methods-Subjects viewed a display containing two images that each oscillated at a different frequency. Different conditions used overlapping or non-overlapping images to explore dependence on gaze function. Subjects were asked to direct attention to one or the other of these images during each of twelve one-minute runs.Results-Half of the subjects produced differences in SSVEP activity elicited by overlapping stimuli that could support BCI control. In all remaining users, differences did exist at corresponding frequencies but were not strong enough to allow effective control.Conclusions-The data demonstrate that SSVEP differences sufficient for BCI control may be elicited by selective attention to one of two overlapping stimuli. Thus, some SSVEP-based BCI approaches may not depend on gaze control. The nature and extent of any BCI's dependence on muscle activity is a function of many factors, including the display, task, environment, and user.Significance-SSVEP BCIs might function in severely disabled users unable to reliably control gaze. Further research with these users is necessary to explore the optimal parameters of such a system and validate online performance in a home environment.

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“…A variety of feature extraction and classification procedures such as stepwise linear discriminate analysis (SWLDA) [6] [17], wavelets [5], support vector machines [11][13] [23], and matched filtering [19] have been implemented, improving the performance beyond that originally reported in [9]. Based on multiple studies in healthy volunteers [6][17] [19], and initial studies in persons with physical disability [25], the P300 speller has potential to serve as an effective communication device for persons who have lost or are losing the ability to write and speak.…”

Section: Introductionmentioning

confidence: 99%

Toward enhanced P300 speller performance

Krusienski

Sellers

McFarland

et al. 2008

Journal of Neuroscience Methods

647

513

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This study examines the effects of expanding the classical P300 feature space on the classification performance of data collected from a P300 speller paradigm [9]. Using stepwise linear discriminant analysis (SWLDA) to construct a classifier, the effects of spatial channel selection, channel referencing, data decimation, and maximum number of model features are compared with the intent of establishing a baseline for not only for the SWLDA classifier, but for related P300 speller classification methods in general. By supplementing the classical P300 recording locations with posterior locations, online classification performance of P300 speller responses can be significantly improved using SWLDA and the favorable parameters derived from the offline comparative analysis.

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BCI Competition 2003—Data Set IIb: Support Vector Machines for the P300 Speller Paradigm

Cited by 430 publications

References 8 publications

A P300-based brain–computer interface for people with amyotrophic lateral sclerosis

A P300-based brain–computer interface for people with amyotrophic lateral sclerosis

Towards an independent brain–computer interface using steady state visual evoked potentials

Toward enhanced P300 speller performance

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