Knowledge-directed electroencephalography (EEG) signal analysis with recurrent context-learning neural networks

Li-Min Fu

doi:10.1117/12.179237

30 June 1994 Knowledge-directed electroencephalography (EEG) signal analysis with recurrent context-learning neural networks

Li-Min Fu

Author Affiliations +

Proceedings Volume 2304, Neural and Stochastic Methods in Image and Signal Processing III; (1994) https://doi.org/10.1117/12.179237
Event: SPIE's 1994 International Symposium on Optics, Imaging, and Instrumentation, 1994, San Diego, CA, United States

Abstract

EEG signal analysis is a key to the understanding of brain activities. Traditionally, this process involves quantifying the signal in terms of frequency and amplitude, on which basis a number of waveforms have been identified. The complexity of EEG signals warrants the construction of a computer program for automatic interpretation. Symbolic knowledge is being built up for correlating the quantity of certain waveforms and brain behavior, and this knowledge can be readily programmed into a knowledge-based system (expert system) for various purposes such as cognitive research, neurological evaluation, and clinical diagnosis. The presented approach employs a knowledge-based neural network in conjunction with a recurrent neural network model as a memory deice which conducts context processing. This research emphasizes the need for the exploitation of `knowledge' and `context' in signal analysis.

Citation Download Citation

Li-Min Fu "Knowledge-directed electroencephalography (EEG) signal analysis with recurrent context-learning neural networks", Proc. SPIE 2304, Neural and Stochastic Methods in Image and Signal Processing III, (30 June 1994); https://doi.org/10.1117/12.179237

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