Identification of discriminative features for decoding overt and imagined speech using stereotactic electroencephalography

Speech imagery is a mental strategy that paralyzed patients can use to control a brain-computer interface (BCI) at their own pace. Most studies that have attempted to decode speech have used scalp electroencephalography or electrocorticography. Only few studies have used stereotactic electroencephalography (SEEG), which enables the exploration of deeply located structures in the brain, in this context. In this paper, we aim to identify discriminative features for decoding speech perception and overt and imagined speech production from SEEG recordings in three patients with epilepsy. We report results for the detection of speech events and for the classification of the corresponding utterances. We propose that SEEG-based BCI systems with multiple degrees of freedom may be reliably controlled by selected phonetic features decoded from the superior temporal gyrus.