Detection and removal of ocular artifacts using Independent Component Analysis and wavelets

In this paper a novel approach for ocular artifact (OA) removal is proposed in which a combination of independent component analysis and wavelet-based noise reduction is utilized for detection and removal of OA. At the first stage, independent basis functions attributed to OA are computed using FastICA algorithm. This is followed by designing a wavelet basis function which is tuned to have sufficient similarity in its waveform to the independent basis functions of OA. We then utilize the designed wavelet for signal decomposition in a standard discrete wavelet transform where by deleting the approximation and summing up the details of signal decomposition, we arrive at a sufficiently artifact-free EEG signal. The approach excludes thresholding challenges of wavelets and works both for eye blinks and eye movements. Applying our algorithm to 420 4-s EEG epochs, the method exhibits high performance for the removal of OA artifacts. Our wavelet design method for noise reduction can be extended to the removal other types of EEG artifacts.