Literature DB >> 20205299

N1 wave in the P300 BCI is not sensitive to the physical characteristics of stimuli.

Sergey L Shishkin1, Ilya P Ganin, Ivan A Basyul, Alexander Y Zhigalov, Alexander Ya Kaplan.   

Abstract

One of the widely used paradigms for the brain-computer interface (BCI), the P300 BCI, was proposed by Farwell and Donchin as a variation of the classical visual oddball paradigm, known to elicit the P300 component of the brain event-related potentials (ERP). We show that this paradigm, unlike the standard oddball paradigm, elicit not only the P300 wave but also a strong posterior N1 wave. Moreover, we present evidence that the sensitivity of this ERP component to targets cannot be explained by the variations of the perceived stimuli energy. This evidence is based on comparing the ERP obtained for usual P300 BCI stimuli and for the "inverted" stimulation scheme with low stimulus related variations of light energy (gray letters on the light gray background, "highlighted" by very light darkening). Despite the dramatic difference between the stimuli in the standard and "inverted" schemes, no difference between N1 amplitudes were found, supporting the view that this component's sensitivity to targets cannot be based simply on "foveating" the target, but may be related to spatial attention mechanisms, which involvement is natural for the P300 BCI. Efforts to optimize the P300 BCI should address better use of both P300 and N1 waves.

Mesh:

Year:  2009        PMID: 20205299     DOI: 10.1142/s0219635209002320

Source DB:  PubMed          Journal:  J Integr Neurosci        ISSN: 0219-6352            Impact factor:   2.117


  7 in total

1.  Navigation of a telepresence robot via covert visuospatial attention and real-time fMRI.

Authors:  Patrik Andersson; Josien P W Pluim; Max A Viergever; Nick F Ramsey
Journal:  Brain Topogr       Date:  2012-09-11       Impact factor: 3.020

2.  Comparison of classification methods for P300 brain-computer interface on disabled subjects.

Authors:  Nikolay V Manyakov; Nikolay Chumerin; Adrien Combaz; Marc M Van Hulle
Journal:  Comput Intell Neurosci       Date:  2011-09-18

3.  Exploring Combinations of Different Color and Facial Expression Stimuli for Gaze-Independent BCIs.

Authors:  Long Chen; Jing Jin; Ian Daly; Yu Zhang; Xingyu Wang; Andrzej Cichocki
Journal:  Front Comput Neurosci       Date:  2016-01-29       Impact factor: 2.380

Review 4.  Neurofeedback Therapy for Enhancing Visual Attention: State-of-the-Art and Challenges.

Authors:  Mehdi Ordikhani-Seyedlar; Mikhail A Lebedev; Helge B D Sorensen; Sadasivan Puthusserypady
Journal:  Front Neurosci       Date:  2016-08-03       Impact factor: 4.677

5.  The Role of the Interplay between Stimulus Type and Timing in Explaining BCI-Illiteracy for Visual P300-Based Brain-Computer Interfaces.

Authors:  Roberta Carabalona
Journal:  Front Neurosci       Date:  2017-06-30       Impact factor: 4.677

6.  A P300-based brain-computer interface with stimuli on moving objects: four-session single-trial and triple-trial tests with a game-like task design.

Authors:  Ilya P Ganin; Sergei L Shishkin; Alexander Y Kaplan
Journal:  PLoS One       Date:  2013-10-31       Impact factor: 3.240

7.  Spatial and Time Domain Feature of ERP Speller System Extracted via Convolutional Neural Network.

Authors:  Jaehong Yoon; Jungnyun Lee; Mincheol Whang
Journal:  Comput Intell Neurosci       Date:  2018-05-15
  7 in total

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