An Approach to Dynamic Modelling and Topographic Feature Extraction of Wake EEG
We consider advanced pattern processing approaches for extracting information from biomedical signals. Signals such as the electroencephalogram are characterised by nonstationarity, a poor signal-to-noise ratio, and a signal which is composed out of many subcomponents. In an application domain such as investigating ‘vigilance’ we have to develop techniques which are capable of addressing some of these problems. In this paper we develop a singular subspace and an independent components dynamical systems embedding of single channel EEG time series which are then analysed for data clustering by a topographic neural network approach known as NeuroScale.
The work is novel in several respects: the use of unsupervised data (most work in this area is through evoked potential response experiments), the treatment of EEG by short window embeddings directly from the time domain(most work averages 30 second segments of power spectra), the comparison of ICA and PCA embeddings on single channel data (most ICA work so far reported has employed multiple sensor channels) and the use of a ‘NeuroScale’ architecture for topographic feature extraction.
KeywordsManifold Coherence Bedding Sammon
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