Decoding Selective Attention in Normal Hearing Listeners and Bilateral Cochlear Implant Users With Concealed Ear EEG

• Published in: Frontiers in neuroscience Vol. 13; p. 720
• Main Authors: Nogueira, Waldo, Dolhopiatenko, Hanna, Schierholz, Irina, Büchner, Andreas, Mirkovic, Bojana, Bleichner, Martin G, Debener, Stefan
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Research Foundation 18-07-2019; Frontiers Media S.A
• Subjects: Algorithms; Attention Deficit Hyperactivity Disorder; cEEGrid; Cochlea; cochlear implant; Cochlear implants; Ear; EEG; Electroencephalography; Neuroscience; selective attention; Sensors; Speech; speech decoding; Germany; speech decoding; cochlear implant; cEEGrid; selective attention; EEG
• ISSN: 1662-4548; 1662-453X; 1662-453X
• DOI: 10.3389/fnins.2019.00720

Electroencephalography (EEG) data can be used to decode an attended speech source in normal-hearing (NH) listeners using high-density EEG caps, as well as around-the-ear EEG devices. The technology may find application in identifying the target speaker in a cocktail party like scenario and steer speech enhancement algorithms in cochlear implants (CIs). However, the worse spectral resolution and the electrical artifacts introduced by a CI may limit the applicability of this approach to CI users. The goal of this study was to investigate whether selective attention can be decoded in CI users using an around-the-ear EEG system (cEEGrid). The performances of high-density cap EEG recordings and cEEGrid EEG recordings were compared in a selective attention paradigm using an envelope tracking algorithm. Speech from two audio books was presented through insert earphones to NH listeners and via direct audio cable to the CI users. 10 NH listeners and 10 bilateral CI users participated in the study. Participants were instructed to attend to one out of the two concurrent speech streams while data were recorded by a 96-channel scalp EEG and an 18-channel cEEGrid setup simultaneously. Reconstruction performance was evaluated by means of parametric correlations between the reconstructed speech and both, the envelope of the attended and the unattended speech stream. Results confirm the feasibility to decode selective attention by means of single-trial EEG data in NH and CI users using a high-density EEG. All NH listeners and 9 out of 10 CI achieved high decoding accuracies. The cEEGrid was successful in decoding selective attention in 5 out of 10 NH listeners. The same result was obtained for CI users.