Authors: Amin Zammouri; Abdelaziz Ait Moussa
Addresses: Department of Computer Science, Faculty of Sciences, Mohammed First University, Bd Med VI, B.P. 717, 60000, Oujda, Morocco ' Department of Computer Science, Faculty of Sciences, Mohammed First University, Bd Med VI, B.P. 717, 60000, Oujda, Morocco
Abstract: In clinical conditions, the EEG measurements are mainly influenced by muscles and ocular movements, especially eye blinks. In this work, we present a new method to detect and reject eye blinks from a single-channel EEG signal. In an offline use mode, the proposed approach is based on statistical computations. It aims, in a first time, at estimating the pure EEG data interval. In a second time, we seek to improve the errors that may occur during the first step by using the Fisher-Snedecor test. By using ROC performance metrics, kappa coefficient and signal-to-artefact ratio (SAR), the proposed method is compared to an expert detection and to single-channel independent component analysis (SC-ICA), one of the widely used and robust methods for artefacts rejection. Experimental results show large values even when using the expert's annotation and the comparison to the SC-ICA method. This reflects the efficiency of the proposed method in detecting and rejecting blinks from a single-channel EEG signal.
Keywords: ocular artefact; eye blink; brain signal; single-channel; electroencephalography; EEG; Fisher-Snedecor test; adaptive filtering; independent component analysis; ICA; principal component analysis; PCA; single-channel ICA.
International Journal of Embedded Systems, 2017 Vol.9 No.4, pp.321 - 327
Received: 09 Oct 2015
Accepted: 27 Mar 2016
Published online: 15 Aug 2017 *