Autoregressive Integrated Adaptive Neural Networks Classifier for EEG-P300 Classification

Abstract

Brain Computer Interface has a potency to be applied in mechatronics apparatus and vehicles in the future. Compared to the other techniques, EEG is the most preferred for BCI designs. In this paper, a new adaptive neural network classifier of different mental activities from EEG-based P300 signals is proposed. To overcome the over-training that is caused by noisy and nonstationary data, the EEG signals are filtered and extracted using autoregressive models before passed to the adaptive neural networks classifier. To test the improvement in the EEG classification performance with the proposed method, comparative experiments were conducted using Bayesian Linear Discriminant Analysis. The experiment results show that the all subjects achieve a classification accuracy of 100%.

An efficient word typing P300-BCI system using a modified T9 interface and random forest classifier

“…To improve classification accuracy, many techniques for P300 extraction and classification have been used, including support vector machine (SVM) [14,15], linear discriminant analysis (LDA) [16], neural network [17], independent component analysis (ICA) [18], and, lately, constrained independent component analysis (cICA) [19], and adaptive neural networks [20].…”

An Experiment of Ocular Artifacts Elimination from EEG Signals using ICA and PCA Methods

“…Not only signal recording methods is diverse, signal processing methods are also diverse. Among of them is independent component analysis (ICA) [15], principal component analysis (PCA) [18], adaptive filters, autoregressive models [19], non-liniear PCA [20], neural networks [21][22][23][24][25], wavelet denoising [1,15], gyroscope signal [16] etc. All of methods will be more powerfull when the artifacts are well identified.…”