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All Journal Mechatronics, Electrical Power, and Vehicular Technology Agricore: Jurnal Agribisnis dan Sosial Ekonomi Pertanian Unpad Teknologi Indonesia
Demi Soetraprawata
Technical Implementation Unit for Instrumentation Development Division – LIPI, Kompleks LIPI Gd. 30, Jalan Sangkuriang Bandung, 40135
Computer Science & IT Electrical & Electronics Engineering

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Journal : Mechatronics, Electrical Power, and Vehicular Technology

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Autoregressive Integrated Adaptive Neural Networks Classifier for EEG-P300 Classification Soetraprawata, Demi; Turnip, Arjon
Journal of Mechatronics, Electrical Power and Vehicular Technology Vol 4, No 1 (2013)
Publisher : Research Centre for Electrical Power and Mechatronics, Indonesian Istitutes of Sciences

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (494.761 KB) | DOI: 10.14203/j.mev.2013.v4.1-8

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%.
The Performance of EEG-P300 Classification using Backpropagation Neural Networks Turnip, Arjon; Soetraprawata, Demi
Journal of Mechatronics, Electrical Power and Vehicular Technology Vol 4, No 2 (2013)
Publisher : Research Centre for Electrical Power and Mechatronics, Indonesian Istitutes of Sciences

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (276.701 KB) | DOI: 10.14203/j.mev.2013.v4.81-88

Abstract

Electroencephalogram (EEG) recordings signal provide an important function of brain-computer communication, but the accuracy of their classification is very limited in unforeseeable signal variations relating to artifacts. In this paper, we propose a classification method entailing time-series EEG-P300 signals using backpropagation neural networks to predict the qualitative properties of a subject’s mental tasks by extracting useful information from the highly multivariate non-invasive recordings of brain activity. To test the improvement in the EEG-P300 classification performance (i.e., classification accuracy and transfer rate) with the proposed method, comparative experiments were conducted using Bayesian Linear Discriminant Analysis (BLDA). Finally, the result of the experiment showed that the average of the classification accuracy was 97% and the maximum improvement of the average transfer rate is 42.4%, indicating the considerable potential of the using of EEG-P300 for the continuous classification of mental tasks.
Co-Authors Adi Nugraha Aris Munandar Arjon Turnip Hariyadi , Hilman Syaeful Alam Kusumandari, Dwi Esti Mahra Arari Heryanto