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Found 3 papers, 2 papers with code
ConvConcatNet: a deep convolutional neural network to reconstruct mel spectrogram from the EEG
To investigate the processing of speech in the brain, simple linear models are commonly used to establish a relationship between brain signals and speech features.
Self-supervised speech representation and contextual text embedding for match-mismatch classification with EEG recording
Contrastive learning was used to relate EEG features to speech features.
A DenseNet-based method for decoding auditory spatial attention with EEG
ASAD methods are inspired by the brain lateralization of cortical neural responses during the processing of auditory spatial attention, and show promising performance for the task of auditory attention decoding (AAD) with neural recordings.
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