A novel wavelet based technique for detection and de-noising of ocular artifact in normal and epileptic electroencephalogram

Abstract

The Electroencephalogram (EEG) is a biological signal that represents the electrical activity of the brain. Typical EEG instrumentation settings used are low pass filtering at 75Hz and paper recording at 100 µ V /cm and 30mm/s for 10 to 20 minutes over 8 to 16 simultaneous channels. A commonly encountered problem in clinical practice during EEG recording is the 'blanking' of the EEG signal due to blinking of the user's eyes. Eye-blinks and movements of the eyeballs produce electrical signals that are collectively known as Ocular Artifacts and these are 10 to 100 times stronger than the EEG signal which is being recorded. The effective filtering of these Ocular artifacts is extremely difficult owing to the fact that their frequency spread (1Hz-50Hz) is observed to be overlapping with that of the EEG. Another major drawback of the existing frequency based de- noising techniques is that they require continuous recording of the Electrooculargram (EOG) signals as well. In this paper, we present a novel and simple technique for the detection and subsequent de-noising of these ocular artifacts using Haar wavelets of high orders. A comprehensive error analysis has been carried out, both in the time domain based artifact detection as well as the frequency domain based de-noising of EEG. This procedure has also got the advantage of being highly artifact selective and so we have applied it to detect and de-noise Epileptic EEG signals.