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Detecting Post-Stroke Aphasia Via Brain Responses to Speech in a Deep Learning Framework
We modeled electroencephalography (EEG) responses to acoustic, segmentation, and linguistic speech representations of a story using convolutional neural networks trained on a large sample of healthy participants, serving as a model for intact neural tracking of speech.
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.
Relating the fundamental frequency of speech with EEG using a dilated convolutional network
To investigate how speech is processed in the brain, we can model the relation between features of a natural speech signal and the corresponding recorded electroencephalogram (EEG).
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