Robust adaptive techniques for minimization of EOG artefacts from EEG signals

In this paper, we propose the application of H∞ techniques for minimization of electrooculogram (EOG) artefacts from corrupted electroencephalographic (EEG) signals. Two adaptive algorithms (time-varying and exponentially-weighted) based on the H∞ principles are proposed. The idea of applying H∞ techniques is motivated by the fact that they are robust to model uncertainties and lack of statistical information with respect to noise [B. Hassibi, A.H. Sayed, T. Kailath, Linear estimation in Krein spaces--part 1: theory & Part II: applications, IEEE Trans. Automat. Control 41 (1996) 18-49]. Studies are performed on simulated as well as real recorded signals. Performance of the proposed techniques are then compared with the well-known least-mean square (LMS) and recursive least-square (RLS) algorithms. Improvements in the output signal-to-noise ratio (SNR) along with the time plots are used as criteria for comparing the performance of the algorithms. It is found that the proposed H∞-based algorithms work slightly better than the RLS algorithm (especially when the input SNR is very low) and always outperform the LMS algorithm in minimizing the EOG artefacts from corrupted EEG signals.

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