Ocular Artifact Suppression in Multichannel EEG Using Dynamic Segmentation and Enhanced wICA
Kiransing Pratapsing Paradeshi, Uttam Dnyanu Kolekar · IETE Journal of Research · 2020
The artifacts such as ocular, muscle and certain electrical disturbances contaminate the Electroencephalogram (EEG). Wavelet enhanced independent component analysis (wICA) with static segmentation preserves little information of both spectral and coherent characteristics of neural activity. However, the considerable amount of valuable information can be preserved with novel automatic dynamic size segmentation of multichannel EEG signal with wICA. Most of the signal information lost in the threshold process (inappropriate threshold value) and this could be overcome with an adaptive threshold approach. It is assumed that the brain neural activity is a Gaussian random distribution with zero mean only for initial state.The 16 channel EEG signals are acquired with Ocular Artifact (OA). The subject is instructed to blink an eye during the time of recording. A National Instrument (NI) data acquisition card is used to acquire the EEG data in MATLAB with sampling rate of 1024 Hz. Statistical parameters like Standard deviation, mean power (PSD), root mean square error (RMSE) are used for analysis and comparison. The proposed dynamic segmentation method is better for suppression of ocular artifacts which preserves the brain neural activity as compared with static segmentation. The artifacts related with eye blinking are removed completely and successfully preserve spectral and coherent characteristics of EEG activity of interest. It is proved that automatic dynamic segmentation is a key tool. The presented method is best suitable for suppression of OA and estimation of brain neural activity.