TY - JOUR T1 - Word pair classification during imagined speech using direct brain recordings. JF - Scientific reports Y1 - 2016 A1 - Martin, Stéphanie A1 - Peter Brunner A1 - Iturrate, Iñaki A1 - Millán, José Del R. A1 - Gerwin Schalk A1 - Robert T. Knight A1 - Pasley, Brian N. AB - 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<þinspace0.05). 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 (meanþinspace=þinspace89% and 86%, respectively; pþinspace<þinspace0.0001), 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. VL - 6 UR - http://www.ncbi.nlm.nih.gov/pubmed/27165452 ER -