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Toward Automated Cochlear Implant Fitting Procedures Based on Event-Related Potentials

Finke, Mareike; Billinger, Martin; Büchner, Andreas

doi: 10.1097/AUD.0000000000000377
e-Research Articles

Objectives: Cochlear implants (CIs) restore hearing to the profoundly deaf by direct electrical stimulation of the auditory nerve. To provide an optimal electrical stimulation pattern the CI must be individually fitted to each CI user. To date, CI fitting is primarily based on subjective feedback from the user. However, not all CI users are able to provide such feedback, for example, small children. This study explores the possibility of using the electroencephalogram (EEG) to objectively determine if CI users are able to hear differences in tones presented to them, which has potential applications in CI fitting or closed loop systems.

Design: Deviant and standard stimuli were presented to 12 CI users in an active auditory oddball paradigm. The EEG was recorded in two sessions and classification of the EEG data was performed with shrinkage linear discriminant analysis. Also, the impact of CI artifact removal on classification performance and the possibility to reuse a trained classifier in future sessions were evaluated.

Results: Overall, classification performance was above chance level for all participants although performance varied considerably between participants. Also, artifacts were successfully removed from the EEG without impairing classification performance. Finally, reuse of the classifier causes only a small loss in classification performance.

Conclusions: Our data provide first evidence that EEG can be automatically classified on single-trial basis in CI users. Despite the slightly poorer classification performance over sessions, classifier and CI artifact correction appear stable over successive sessions. Thus, classifier and artifact correction weights can be reused without repeating the set-up procedure in every session, which makes the technique easier applicable. With our present data, we can show successful classification of event-related cortical potential patterns in CI users. In the future, this has the potential to objectify and automate parts of CI fitting procedures.

Department of Otolaryngology, Hannover Medical School, Germany and Cluster of Excellence “Hearing4all”, Hannover, Germany.

This study was supported by the DFG Cluster of Excellence EXC 1077/1 “Hearing4all” and Advanced Bionics.

The authors Finke and Billinger contributed equally to this study.

M.F. designed and performed experiments, analyzed data, and wrote the article; M.B. analyzed data, provided statistical analysis and the art work, and wrote the article. All authors discussed methods and results, as well as the implications drawn and commented on the manuscript at all stages.

The authors have no conflicts of interest to disclose.

Received March 15, 2016; accepted August 23, 2016.

Address for correspondence: Mareike Finke, Hannover Medical School, Carl-Neuberg-Str. 1, 30625 Hannover, Germany. E-mail: finke.mareike@mh-hannover.de

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