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Various neuroimaging techniques can be used to investigate how the brain processes sound. Electroencephalography (EEG) is popular because it is relatively easy to conduct and has a high temporal resolution. An increasingly popular method in these fields is to relate a person’s electroencephalogram (EEG) to a feature of the natural speech signal they were listening to. This is typically done using linear regression or relatively simple neural networks to predict the EEG signal from the stimulus or to decode the stimulus from the EEG.
In the Auditory-EEG challenge, teams will compete to build the best model to relate speech to EEG. We provide a large auditory EEG dataset containing data from 85 subjects who listen on average to 108 minutes of single-speaker stimuli for a total of 157 hours of data. We define two tasks:
Task 1 match-mismatch: given two segments of speech and a segment of EEG, which segment of speech matches the EEG?
Task 2 regression: reconstruct the speech envelope from the EEG.
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