During speech perception, a listener's electroencephalogram (EEG) reflects acoustic-level processing as well as higher-level cognitive factors such as speech comprehension and attention. However, decoding speech from EEG recordings is challenging due to the low signal-to-noise ratios of EEG signals. We report on an approach developed for the ICASSP 2023 'Auditory EEG Decoding' Signal Processing Grand Challenge. A simple ensembling method is shown to considerably improve upon the baseline decoder performance. Even higher classification rates are achieved by jointly decoding the speech-evoked frequency-following response and responses to the temporal envelope of speech, as well as by fine-tuning the decoders to individual subjects. Our results could have applications in the diagnosis of hearing disorders or in cognitively steered hearing aids.
@article{arxiv.2303.06435,
title = {Relating EEG recordings to speech using envelope tracking and the speech-FFR},
author = {Mike Thornton and Danilo Mandic and Tobias Reichenbach},
journal= {arXiv preprint arXiv:2303.06435},
year = {2023}
}
Comments
2 pages, 3 figures. Accepted for ICASSP 2023 (challenge track)