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Majority Rule Using Collaborative P300 by Auditory Stimulation
Author(s) -
Kosuke Fujita,
Hideaki Touyama
Publication year - 2017
Publication title -
journal of advanced computational intelligence and intelligent informatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.172
H-Index - 20
eISSN - 1343-0130
pISSN - 1883-8014
DOI - 10.20965/jaciii.2017.p1312
Subject(s) - computer science , electroencephalography , brain–computer interface , signal (programming language) , artificial intelligence , speech recognition , pattern recognition (psychology) , signal averaging , machine learning , psychology , neuroscience , analog signal , programming language , signal transfer function , digital signal processing , computer hardware
In this study, a new method to realize majority rule is presented by using noninvasive brain activities. With the majority rule based on an electroencephalogram (EEG), a technique to determine the attention of multiple users is proposed. In general, a single-shot EEG ensures short-time response, but it is inevitably deteriorated by artifacts. To enhance the accuracy of the majority rule, the collaborative signals of P300 evoked potentials are focused. The collaborative P300 signal is prepared by averaging individual single-shot P300 signals among subjects. In experiments, the EEG signals of twelve volunteers were collected by using auditory stimuli. The subjects paid attention to target stimuli and no attention to standard stimuli. The collaborative P300 signal was used to evaluate the performance of the majority rule. The proposed algorithm enables us to estimate the degree of attention of the group. The classification is based on supervised machine learning, and the accuracy approximately 80%. The applications of this novel technique in multimedia content evaluations as well as neuromarketing and computer-supported co-operative work are discussed.

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