Toward Decoding Selective Attention From Single-Trial EEG Data in Cochlear Implant Users

Previous results showed that it is possible to decode an attended speech source from EEG data via the reconstruction of the speech envelope in normal hearing (NH) listeners. However, so far it is unknown that how the performance of such a decoder is affected by the decrease in spectral resolution and the electrical artifacts introduced by a cochlear implant (CI) in users of these prostheses. NH listeners and bilateral CI users participated in the present study. Speeches from two audio books, one uttered by a male voice and one by a female voice, were presented to NH listeners and CI users. Participants were instructed to attend to one of the two speech streams presented dichotically while a 96-channel EEG was recorded. Speech envelope reconstruction from the EEG data was obtained by training decoders using a regularized least square estimation method. Decoding accuracy was defined as the percentage of accurately reconstructed trials for each subject. For NH listeners, the experiment was repeated using a vocoder to reduce spectral resolution and simulate speech perception with a CI in NH listeners. The results showed a decoding accuracy of 80.9% using the original sound files in NH listeners. The performance dropped to 73.2% in the vocoder condition and to 71.5% in the group of CI users. In sum, although the accuracy drops when the spectral resolution becomes worse, the results show the feasibility to decode the attended sound source in NH listeners with a vocoder simulation, and even in CI users, albeit more training data are needed.