Neural networks for EEG signal decomposition and classification
The authors present a new method for the decomposition of time varying signals based on a principal component analysis implemented with unsupervised neural networks. The method is applied to EEG spike detection using neural networks directly fed with the signal components, avoiding the time consuming feature extraction phase. Results show good learning capability of the topologies used.