Massively parallel classification of single-trial EEG signals using a min-max Modular neural network

Abstract

This paper presents a method for classifying single-trial electroencephalogram (EEG) signals using min-max modular neural networks implemented in a massively parallel way. The method has three main steps. First, a large-scale, complex EEG classification problem is simply divided into a reasonable number of two-class subproblems, as small as needed. Second… (More)
DOI: 10.1109/TBME.2003.821023

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Cite this paper

@article{Lu2004MassivelyPC, title={Massively parallel classification of single-trial EEG signals using a min-max Modular neural network}, author={Bao-Liang Lu and Jonghan Shin and Michinori Ichikawa}, journal={IEEE Transactions on Biomedical Engineering}, year={2004}, volume={51}, pages={551-558} }