Congratulations to Philemon!

Philemon Roussel, successfully defended his thesis, entitled Analysis of cortical activity for the development of brain-computer interfaces for speech on Friday 11th.

Abstract:

Brain-computer interfaces for speech have the potential to restore the ability to speak in people with severe paralysis. Such a system would decode features of what the user wants to say from their brain activity and synthesize the associated sounds in real time. This thesis aims to contribute to the development of brain-computer interfaces and to the understanding of cortical activity underlying speech.

First, we highlighted a phenomenon of acoustic contamination of electrophysiological signals during speech. Our analyses revealed that this phenomenon was present in several recordings from different laboratories around the world and that it could have an important impact on speech decoding studies by biasing the obtained performances.

Understanding the differences between overt and covert speech is important to know if the models currently developed on overt speech would be easily applicable to paralyzed people. Using an intracortical recording, we showed that the dynamics of the activity of a population of neurons in Broca's area were different during overt and covert speech. Moreover, we found that the spiking activity recorded during the pronunciation of sentences allowed to discriminate these sentences. The similarity of spike trains appeared to be correlated with the phonological similarity of the sentences. Using the same recording, we showed that the low frequency component of local field potentials was modulated by overt speech and to a lesser extent by covert speech.

Finally, we tested, in a non-paralyzed participant, a protocol allowing to build a covert speech decoder from electrophysiological activity. Our offline analyses showed that it was possible to decode acoustic features of imagined phonemes with performances statistically superior to chance. We also performed a preliminary test of real-time, closed-loop decoding in the same participant.

Keywords: Electrophysiology, Speech, Broca's area, Neural decoding