Detecting Gamma-Band Responses to the Speech Envelope for the ICASSP 2024 Auditory EEG Decoding Signal Processing Grand Challenge - Thornton, Mike; Auernheimer, Jonas; Jehn, Constantin; Mandic, Danilo; Reichenbach, Tobias (2024 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops, ICASSPW 2024 - Proceedings, 2024)
The 2024 ICASSP Auditory EEG Signal Processing Grand Challenge concerns the decoding of electroencephalography (EEG) measurements taken from participants who listened to speech material. This work details our solution to the match-mismatch sub-task: given a short temporal segment of EEG recordings and several candidate speech segments, the task is to classify which of the speech segments was time-aligned with the EEG signals. We show that high-frequency gamma-band responses to the speech envelope can be detected with a high accuracy. By jointly assessing gamma-band responses and low-frequency envelope tracking, we develop a match-mismatch decoder which placed first in this task.
CRIS-Team Scopus Importer:2024-09-06
Keywords: deep learning; EEG decoding; speech