Topic: deep-learning decoder

During speech perception, a listener’s electroencephalogram (EEG) reflects low-level acoustic processing as well as higher-level cognitive processes such as speech comprehension and selective attention. However, relating EEG signals to speech remains challenging, owing in part to the low SNRs of EEG signals, as well as the fact that EEG signals are highly specific to individuals. To address this, we developed a deep-learning decoder which targeted the match-mismatch problem: given two candidate speech segments and a short segment of EEG measurements, the task is to identify which of the speech