Decoding spectrotemporal features of overt and covert speech from the human cortex.

TitleDecoding spectrotemporal features of overt and covert speech from the human cortex.
Publication TypeJournal Article
Year of Publication2014
AuthorsMartin, S, Brunner, P, Holdgraf, C, Heinze, H-J, Crone, NE, Rieger, J, Schalk, G, Knight, RT, Pasley, BN
JournalFrontiers in Neuroengineering
Date Published03/2014
Keywordscovert speech, decoding model, Electrocorticography, pattern recognition, speech production

Auditory perception and auditory imagery have been shown to activate overlapping brain regions. We hypothesized that these phenomena also share a common underlying neural representation. To assess this, we used electrocorticography intracranial recordings from epileptic patients performing an out loud or a silent reading task. In these tasks, short stories scrolled across a video screen in two conditions: subjects read the same stories both aloud (overt) and silently (covert). In a control condition the subject remained in a resting state. We first built a high gamma (70–150 Hz) neural decoding model to reconstruct spectrotemporal auditory features of self-generated overt speech. We then evaluated whether this same model could reconstruct auditory speech features in the covert speech condition. Two speech models were tested: a spectrogram and a modulation-based feature space. For the overt condition, reconstruction accuracy was evaluated as the correlation between original and predicted speech features, and was significant in each subject (p


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