30 June 1994 Knowledge-directed electroencephalography (EEG) signal analysis with recurrent context-learning neural networks
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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.
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Li-Min Fu, 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); doi: 10.1117/12.179237; https://doi.org/10.1117/12.179237
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