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, Millán, JDel R, Schalk, G, Knight, RT, Pasley, BN
JournalScientific reports
Volume6
Pagination25803
Date PublishedMay
ISSN2045-2322
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 (70-150þinspaceHz) 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 (meanþinspace=þinspace58%; pþinspace

URLhttp://www.ncbi.nlm.nih.gov/pubmed/27165452
DOI10.1038/srep25803

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