Decoding Auditory EEG Responses Using an Adapted Wavenet

We introduce a WaveNet-based model that placed second in the Auditory EEG Challenge on the regression subtask of the ICASSP Signal Processing Grand Challenge 2023. The model achieved the highest score on the held-out subjects test set with 341k trainable parameters. In this paper, we present our net...

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Published inICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) pp. 1 - 2
Main Authors Van Dyck, Bob, Yang, Liuyin, Van Hulle, Marc M.
Format Conference Proceeding
LanguageEnglish
Published IEEE 04.06.2023
Subjects
Adaptation models
Auditory EEG
Brain modeling
Electroencephalography
Network architecture
Signal processing
Sociology
Speech Envelope
Training
WaveNet
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Summary:We introduce a WaveNet-based model that placed second in the Auditory EEG Challenge on the regression subtask of the ICASSP Signal Processing Grand Challenge 2023. The model achieved the highest score on the held-out subjects test set with 341k trainable parameters. In this paper, we present our network architecture, and training strategies along with a short discussion.
ISSN:2379-190X
DOI:10.1109/ICASSP49357.2023.10095420