Autoregressive Integrated Adaptive Neural Networks Classifier for EEG-P300 Classification

Demi Soetraprawata, Arjon Turnip

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 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%.




Keywords


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

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Cited-By

1. An efficient word typing P300-BCI system using a modified T9 interface and random forest classifier
Faraz Akram, Seung Moo Han, Tae-Seong Kim
Computers in Biology and Medicine  vol: 56  first page: 30  year: 2015  
doi: 10.1016/j.compbiomed.2014.10.021