A comparative study of application of different non-conventional filters on electroencephalogram.

Electroencephalography (EEG) is an electrophysiological monitoring technique to capture the brains electrical activity. It is a noninvasive recording technique in which electrodes are placed over the scalp. EEG records the variations in voltage resulting from ionic current inside the brain neurons. In a clinical perspective, EEG corresponds to the recording of the brain's impulsive electrical activity during a given time period. EEG is used to diagnose epilepsy, sleep disorders, encephalopathies, etc. Hence, EEG signals are very useful in detecting abnormalities in the human brain. But the main problem in the analysis of these signals is due to the noise that gets added to them. These artefacts arise due to various reasons such as due to power line disturbance (50 or 60 Hz) or due to other natural rhythms of the body like the heartbeat, muscle movement, blinking of eyes etc. These signals get added as noise while recording of the EEG signal and pose difficulty in correct clinical analysis. Therefore, it is necessary to develop methods that are efficient in removing noise from these signals. Several methods such as those based on time and frequency have been earlier utilized but failed due to their inability to remove lowfrequency noise. In this work, several pre-processing filters have been tested on EEG data. An attempt has been made to find the best pre-processing technique that can be used for effective cleaning of EEG signals. From the results obtained it can be concluded that the proposed modified self-filter shows the best results when compared with other filtering techniques such as Kalman filter, recurrent quantum neural network (RQNN) filter, moving average (MA) filter, modified self-filter, Savitzky Golay (SG) filter and Weiner filter.