On-line method for enhancement of electroencephalogram signals in presence of electro-oculogram artefacts using nonlinear recursive least squares technique

A record of the human scalp potentials (EEG) is one of the most commonly used techniques for understanding the brain functions. While recording EEG signals, other undesired signals called artefacts get superimposed on the EEG signals. Of these, electro-oculogram generated by eye movements is found to be the most significant and common. Conventional artefact minimisation schemes use linear estimation models. However, the result in sub-optimal schemes as signal processing in the human brain is highly nonlinear. In this paper therefore an on-line technique is suggested for artefact minimisation using nonlinear recursive least squares. Results confirm the efficacy of the method.

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