Auditory EEG Decoding Challenge for ICASSP 2023

Abstract

This paper describes the auditory EEG challenge, organized as one of the Signal Processing Grand Challenges at ICASSP 2023. The challenge provides EEG recordings of 85 subjects who listened to continuous speech, as audiobooks or podcasts, while their brain activity was recorded. EEG recordings of 71 subjects were provided as a training set such that challenge participants could train their models on a relatively large dataset. The remaining 14 subjects were used as held-out subjects in evaluating the challenge. The challenge consists of two tasks that relate electroencephalogram (EEG) signals to the presented speech stimulus. The first task, match-mismatch, aims to determine which of two speech segments induced a given EEG segment. In the second regression task, the goal is to reconstruct the speech envelope from the EEG. For the match-mismatch task, the performance of different teams was close to the baseline model, and the models did generalize well to unseen subjects. In contrast, For the regression task, the top teams significantly improved over the baseline models in the held-out stories test set while failing to generalize to unseen subjects.

Keywords

• Backward modeling
• EEG
• match-mismatch
• neural tracking
• speech decoding