Word pair classification during imagined speech using direct brain recordings

TitleWord pair classification during imagined speech using direct brain recordings
Publication TypeJournal Article
Year of Publication2016
AuthorsMartin, S, Brunner, P, Iturrate, I, del Millán, JR, Schalk, G, Knight, RT, Pasley, BN
JournalScientific Reports
Volume6
Issue25803
Date Published05/2016
Abstract

People that cannot communicate due to neurological disorders would benefit from an internal speech decoder. Here, we showed the ability to classify individual words during imagined speech from electrocorticographic signals. In a word imagery task, we used high gamma (70–150 Hz) time features with a support vector machine model to classify individual words from a pair of words. To account for temporal irregularities during speech production, we introduced a non-linear time alignment into the SVM kernel. Classification accuracy reached 88% in a two-class classification framework (50% chance level), and average classification accuracy across fifteen word-pairs was significant across five subjects (mean = 58%; p 

URLhttp://www.nature.com/articles/srep25803
DOI10.1038/srep25803
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