Adaptive filtering based artifact removal from electroencephalogram (EEG) signals

The electroencephalogram (EEG) is an important bioelectric signal for studying human brain characteristics as well as detection of abnormalities like epilepsy. However, the EEG recorded from frontal channels, often contain strong artifacts produced by eye movements. Existing regression-based methods for removing artifacts require various procedures for pre-processing and calibration that are inconvenient and time consuming. This paper describes a method for removing the EOG artifacts contained in EEG signal based on adaptive filtering. The method uses separately recorded noisy EEG and clean EEG as two reference inputs. The noisy EEG signals with three types of EOG artifacts-horizontal eye movement, vertical eye movement and eye blinks have been recorded for five subjects. The adaptive filter, based on a least mean square (LMS) algorithm, adapts its coefficients to produce an output which matches the reference input.