An automatic algorithm for blink-artifact suppression based on iterative template matching: application to single channel recording of cortical auditory evoked potentials.

Objective. Artifact reduction in electroencephalogram (EEG) signals is usually necessary to carry out data analysis appropriately. Despite the large amount of denoising techniques available with a multichannel setup, there is a lack of efficient algorithms that remove (not only detect) blink-artifacts from a single channel EEG, which is of interest in many clinical and research applications. This paper describes and evaluates the iterative template matching and suppression (ITMS), a new method proposed for detecting and suppressing the artifact associated with the blink activity from a single channel EEG. Approach. The approach of ITMS consists of (a) an iterative process in which blink-events are detected and the blink-artifact waveform of the analyzed subject is estimated, (b) generation of a signal modeling the blink-artifact, and (c) suppression of this signal from the raw EEG. The performance of ITMS is compared with the multi-window summation of derivatives within a window (MSDW) technique using both synthesized and real EEG data. Main results. Results suggest that ITMS presents an adequate performance in detecting and suppressing blink-artifacts from a single channel EEG. When applied to the analysis of cortical auditory evoked potentials (CAEPs), ITMS provides a significant quality improvement in the resulting responses, i.e. in a cohort of 30 adults, the mean correlation coefficient improved from 0.37 to 0.65 when the blink-artifacts were detected and suppressed by ITMS. Significance. ITMS is an efficient solution to the problem of denoising blink-artifacts in single-channel EEG applications, both in clinical and research fields. The proposed ITMS algorithm is stable; automatic, since it does not require human intervention; low-invasive, because the EEG segments not contaminated by blink-artifacts remain unaltered; and easy to implement, as can be observed in the Matlab script implemeting the algorithm provided as supporting material.