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All Journal Mechatronics, Electrical Power, and Vehicular Technology
Demi Soetraprawata
Technical Implementation Unit for Instrumentation Development, Indonesian Institute of Sciences, Kompleks LIPI Gd. 30, Jl. Sangkuriang, Bandung
Electrical & Electronics Engineering

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Journal : Mechatronics, Electrical Power, and Vehicular Technology

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The Performance of EEG-P300 Classification using Backpropagation Neural Networks Arjon Turnip; Demi Soetraprawata
Journal of Mechatronics, Electrical Power and Vehicular Technology Vol 4, No 2 (2013)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | 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 Arjon Turnip