P300 detection based on extraction and classification in online BCI
Sutrisno Salomo Hutagalung, Arjon Turnip, Aris Munandar · 2013
In this paper, an application of nonlinear principal component analysis for online P300 extraction and classification is proposed. In order to cover the nonlinearity between the variables, a five-layer neural network is applied for feature extraction. The experimental results in this work show that the implementation of the proposed method achieves a very significant statistical improvement in extracting and classifying P300 components. After a short time of practice, most participants could learn to extract and classify the P300 wave with greater than 80% accuracy.