Semi-automatic attenuation of cochlear implant artifacts for the evaluation of late auditory evoked potentials

Rapid bilateral improvement in auditory cortex activity in postlingually deafened adults following cochlear implantation

“…Components representing CI artifacts were individually identified by the centroid on the side of the implanted device, and by the time course of component activity. The latter typically shows a sharp onset, in this study around 30 ms after stimulus onset (see Viola et al, 2012 for discussion). After rejection of CI artifact-related components, missing channels located in proximity to the speech processor and transmitter coil were interpolated (mean: 5 electrodes; SEM: 0.2; range: 1-7 electrodes) using the EEGLAB function eeg_in-terp.m.…”

Validation of the cochlear implant artifact correction tool for auditory electrophysiology

“…However, recording and analyzing ERP data from CI users faces a great challenge. The responses are usually contaminated by large device-related electrical artifacts upon auditory stimulation [1,6,10,17,18]. These artifacts are often 5-10 times greater than the cortical evoked responses and are time-locked to the stimulus presentation in each trial, which can obscure the cortical ERP components such as the N1-P2 complex [6] that are frequently used to examine the neural processing of speech sounds in terms of response latency, amplitude, topography, or cortical source localization.…”

“…This manual process is laborious, subjective, and requires expert knowledge. Recently, Viola et al [17] developed the CI artifact correction (CIAC) algorithm which is an objective, semi-automatic approach to cluster and remove CI artifacts. The CIAC algorithm follows previous successful attempts to use ICA to remove CI artifacts from ERP data and apply source modeling to the reconstructed data [1].…”

“…The CIAC algorithm relies on both spatial and temporal properties of the independent components (ICs) at the subject group level. In their paper, Viola et al [17] validated the method by comparing the evoked responses reconstructed using a manual versus CIAC approach and found no significant differences in N1 or P2 latencies or N1-P2 peak-to-peak amplitudes across methods. As yet, there has been no independent validation of this CIAC approach from another lab.…”

“…Using independent component analysis (ICA), the artifacts can be manually identified and removed, and the ERP responses can be reconstructed from the remaining components. Viola et al [17] recently developed an efficient algorithm that uses spatial and temporal statistics of the components to automate CI artifact removal. The purpose of this study was to perform an independent validation of the algorithm.…”