A New Method to Test the Efficiency of Cochlear Implant Artifacts Removal From Auditory Evoked Potentials
Virginie Attina, Faten Mina, Pierre Stahl, Yvan Duroc, Evelyne Veuillet, Éric Truy, Hung Thai Van · IEEE Transactions on Neural Systems and Rehabilitation Engineering · 2017
Auditory evoked potentials are of great interest to objectively evaluate the audition in cochlear implant (CI) recipients. However, these measures are impeded by CI stimulation electrical artifacts present in the EEG. In the first part, this paper investigates the use of a hybrid model approximating CI patient data. This model gives access to both uncontaminated and denoised data, thus allowing for the evaluation of CI artifact removal methods. Here the efficiency of independent component analysis (ICA) is evaluated in the context of auditory steady-state responses (ASSRs). A dedicated experimental setup was developed to simultaneously record EEG data from a normal hearing (NH) participant and CI artifact data from a phantom equipped with a CI. Hybrid data were obtained as a linear mixture of both sources. Amplitude-modulated continuous tones were used as stimuli to elicit ASSRs. After denoising, the comparison of denoised hybrid data and original NH data showed high correlations between the two datasets, demonstrating the efficiency of ICA. In the second part, the ICA was applied to real clinical CI ASSR data. Results support the usefulness of the methodology as regards the performance evaluation of signal processing methods applied to CI patient data prior to clinical application.