Autoregressive Integrated Adaptive Neural Networks Classifier for EEG-P300 Classification

Demi Soetraprawata, Arjon Turnip


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 non-stationary 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%.


brain computer interface, feature extraction, classification accuracy, autoregressive, adaptive neural networks, EEG-based P300, transfer rate

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