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Word pair classification during imagined speech using direct brain recordings.

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?

SUBMITTER: Martin S

PROVIDER: S-EPMC4863149 | biostudies-literature | 2016 May

REPOSITORIES: biostudies-literature

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Word pair classification during imagined speech using direct brain recordings.

Martin Stephanie S Brunner Peter P Iturrate Iñaki I Millán José Del R Jdel R Schalk Gerwin G Knight Robert T RT Pasley Brian N BN

Scientific reports 20160511

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 ...[more]

PMID: 27165452

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