F. Alexandre, N. Kerkeni, K. Ben Khalifa, and M.H. Bedoui (Tunisia)
Signal Processing, Data Representation and Visualization, EEG interpretation, Artificial Neural Networks
EEG signals are very difficult to interpret because they are dynamic, non-linear and non-stationary signals. Human ex pertise also indicates that multi-level analysis must be per formed to integrate various sources of knowledge. In this paper, we review these difficulties and propose that artifi cial neural networks could be good candidates to handle such a difficult problem.
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