Brain-Computer Interface Based on Generation of Visual Images
Pavel Bobrov,
Alexander Frolov,
Charles Cantor,
Irina Fedulova,
Mikhail Bakhnyan and
Alexander Zhavoronkov
PLOS ONE, 2011, vol. 6, issue 6, 1-12
Abstract:
This paper examines the task of recognizing EEG patterns that correspond to performing three mental tasks: relaxation and imagining of two types of pictures: faces and houses. The experiments were performed using two EEG headsets: BrainProducts ActiCap and Emotiv EPOC. The Emotiv headset becomes widely used in consumer BCI application allowing for conducting large-scale EEG experiments in the future. Since classification accuracy significantly exceeded the level of random classification during the first three days of the experiment with EPOC headset, a control experiment was performed on the fourth day using ActiCap. The control experiment has shown that utilization of high-quality research equipment can enhance classification accuracy (up to 68% in some subjects) and that the accuracy is independent of the presence of EEG artifacts related to blinking and eye movement. This study also shows that computationally-inexpensive Bayesian classifier based on covariance matrix analysis yields similar classification accuracy in this problem as a more sophisticated Multi-class Common Spatial Patterns (MCSP) classifier.
Date: 2011
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0020674
DOI: 10.1371/journal.pone.0020674
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