P300 detection using nonlinear independent component analysis

Abstract

In this paper, a nonlinear independent component analysis (NICA) extraction method for brain signal based EEG-P300 are proposed. The performance of the proposed method is investigated through a comparison of well-known extraction methods (i.e., AAR, JADE, and SOBI algorithms). Finally, the promising results reported here reflect the considerable potential of EEG for the continuous classification of mental states. Index Terms-Brain computer interface (BCI), Classification accuracy, Transfer rate, Nonlinear, ICA Electroencephalogram (EEG).

Progress in EEG-Based Brain Robot Interaction Systems

“…The activation status of these 3 ICs in different channels was used as the feature for P300 identification. Turnip et al put forward a nonlinear independent components analysis (NICA) extraction method for P300 [ 108 ]. With the NICA method, a level of accuracy was attained after about 240 iterations, which were less than 1800 iterations in the same level without using the proposed feature extraction.…”

A review and experimental study on the application of classifiers and evolutionary algorithms in EEG-based brain–machine interface systems

“…In order to decrease the amplitude of outliers, the amplitude of obtained signals from each electrode was calculated, from which those with amplitudes higher than 90% or lower than 10% were clipped and ultimately normalized. Numerous studies have used this database in the fields of signal processing and feature extraction [240,241], feature selection [239,242], and classifiers [243][244][245]. In the present study, the acquired signals from eight electrodes, namely P8, P4, P3, P7, Oz, Pz, Cz and Fz are used.…”

Removing ocular artifact of EEG signal using SOBI-RO on motor imagery experiment

“…They are featured by high amplitude, but the high amplitude peaks are mainly seen on the frontopolar channels in the combination with the occipital channels. These peaks areconsidered as one of the most considerable artifacts in EEG studies [7][8][9]. Due to the presence of ocular artifacts, it is difficult to analyze EEG signal because of their spikes.…”