EEG denoising using narrow-band independent component selection in time domain

Electroencephalography (EEG) is the most frequently used technique to monitor functional activity of the brain. It has been widely employed in brain-computer interfaces based on the detection of P300 potentials. However, the P300 waves often contain physiological and non-physiological artifacts such as steady state visually evoked potential, power line or environment noise. The aim of this work is to eliminate undesirable periodic independent components from EEG, in order to enhance the P300 wave. The proposed method combines independent component analysis with a suitable selection of the most representative P300 components according to power features estimated from time measures using Parseval's theorem. The results show statistical differences (p<0.001) between the power spectral densities of raw and restored EEG, after Parseval-based component elimination. Additionally, the comparison of P300 latencies between raw and filtered EEG, showed statistical differences (p<0.001). Our findings suggest that this method can be helpful to eliminate undesirable components with significant narrow-band power, in order to preserve information required to enhance the P300 potential.