Massively Parallel Classification of Single-Trial EEG Signals Using a Min-Max Modular Neural Network
| Autor: | Jonghan Shin, Michinori Ichikawa, Bao-Liang Lu |
|---|---|
| Rok vydání: | 2004 |
| Předmět: |
Computer science
Models Neurological Biomedical Engineering Expert Systems Electroencephalography Machine learning computer.software_genre Computing Methodologies Hippocampus Cognition medicine Animals Diagnosis Computer-Assisted Evoked Potentials Massively parallel Signal processing Artificial neural network medicine.diagnostic_test business.industry Wavelet transform Signal Processing Computer-Assisted Pattern recognition Modular design Perceptron Modular neural network Rats ComputingMethodologies_PATTERNRECOGNITION Neural Networks Computer Artificial intelligence business computer Algorithms |
| Zdroj: | IEEE Transactions on Biomedical Engineering. 51:551-558 |
| ISSN: | 0018-9294 |
| DOI: | 10.1109/tbme.2003.821023 |
| Popis: | 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, the two-class subproblems are simply learned by individual smaller network modules in parallel. Finally, all the individual trained network modules are integrated into a hierarchical, parallel, and modular classifier according to two module combination laws. To demonstrate the effectiveness of the method, we perform simulations on fifteen different four-class EEG classification tasks, each of which consists of 1491 training and 636 test data. These EEG classification tasks were created using a set of non-averaged, single-trial hippocampal EEG signals recorded from rats; the features of the EEG signals are extracted using wavelet transform techniques. The experimental results indicate that the proposed method has several attractive features. 1) The method is appreciably faster than the existing approach that is based on conventional multilayer perceptrons. 2) Complete learning of complex EEG classification problems can be easily realized, and better generalization performance can be achieved. 3) The method scales up to large-scale, complex EEG classification problems. |
| Databáze: | OpenAIRE |
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