P300 detection based on extraction and classification in online BCI

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.