Abstract
An enhanced blind source separation algorithm based on Stone's BSS approach is proposed, to reject the Electrooculogram (EOG) artifact and power line noise (50Hz) from simulated and real human Electroencephalography (EEG) signals without the notch filter, in order not to lose any useful EEG data around the 50-Hz. The proposed algorithm which called efficient Stones BSS (ESBSS) has been compared with four well-known BSS algorithms over super-Gaussian, sub-Gaussian artifacts and EEG signals with a linear mixture. In Original Stones BSS, the half-life values taken as a constant, typically (hlong≥100 hShort), but in the proposed work, an optimization procedure is used to change these values until the maximum temporal predictability is found. The real EEG data are taken from Imperial College London using a computerized EEG device with eight electrodes placed according to the 10-20 system.
Published Version
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