Automatic Identification and Removal of Ocular Artifacts from EEG using Wavelet Transform

The Electroencephalogram (EEG) is a biological signal that represents the electrical activity of the brain. Eye-blinks and movement of the eyeballs produce electrical signals that are collectively known as Ocular Artifacts (OA). These are of the order of milli-volts and they contaminate the EEG signals which are of the order of micro-volts. The frequency range of EEG signal is 0 to 64 Hz and the OA occur within the range of 0 to 16 Hz. If the wavelet based EOG correction algorithm is applied to the entire length of the EEG signal, it results in thresholding of both low frequency and high frequency components even in the non-OA zones. This leads to considerable loss of valuable background EEG activity. Though the detection of OA zones can be done by visual inspection, the OA time zones need to be given as input to the EOG correction procedure, which is a laborious process. Hence there is a need for automatic detection of artifact zones. This paper discusses a method to automatically identify slow varying OA zones and applying wavelet based adaptive thresholding algorithm only to the identified OA zones, which avoids the removal of background EEG information. Adaptive thresholding applied only to the OA zone does not affect the low frequency components in the non-OA zones and also preserves the shape (waveform) of the EEG signal in nonartifact zones which is of very much importance in clinical diagnosis.