A Aleman

A Flinker

A Ritaccio

AW Toga

BN Pasley

C-C Chang

CJ Price

Correia Geva

D Hermes

E Smith

ED Palmer

FH Guenther

FZ Yetkin

G Schalk

H Sakoe

HJ Rosen

J Huang

J Kubanek

J-P Lachaux

JW Mauchly

KJ Miller

L Aziz-Zadeh

M Perrone-Bertolotti

Mesgarani

NE Crone

PK McGuire

RM Hinke

SS Shergill

TL Hubbard

TW Boonstra

VL Towle

X Pei

Y Benjamini

Scientific Reports

AbstractPeople 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 &lt; 0.05). We also compared classification accuracy between imagined speech, overt speech and listening. As predicted, higher classification accuracy was obtained in the listening and overt speech conditions (mean = 89% and 86%, respectively; p &lt; 0.0001), where speech stimuli were directly presented. The results provide evidence for a neural representation for imagined words in the temporal lobe, frontal lobe and sensorimotor cortex, consistent with previous findings in speech perception and production. These data represent a proof of concept study for basic decoding of speech imagery, and delineate a number of key challenges to usage of speech imagery neural representations for clinical applications.</jats:p

Word pair classification during imagined speech using direct brain recordings

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