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Found 4 papers, 1 papers with code
Relating EEG to continuous speech using deep neural networks: a review
Linear models are presently used to relate the EEG recording to the corresponding speech signal.
CLASH: Contrastive learning through alignment shifting to extract stimulus information from EEG
Additionally, we show that in contrast to the baseline denoising techniques, our method can be used with data of unseen subjects and stimuli without retraining, improving decoding performance by 19\% and 34\% over raw EEG for two holdout datasets.
Extracting Different Levels of Speech Information from EEG Using an LSTM-Based Model
Decoding the speech signal that a person is listening to from the human brain via electroencephalography (EEG) can help us understand how our auditory system works.
Predicting speech intelligibility from EEG in a non-linear classification paradigm
The accuracy of the model's match/mismatch predictions can be used as a proxy for speech intelligibility without subject-specific (re)training.
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