EEG Data Mining Using PCA

ABsTRAcT This BLOCKINchapter BLOCKINdeals BLOCKINwith BLOCKINthe BLOCKINapplication BLOCKINof BLOCKINprincipal BLOCKINcomponents BLOCKINanalysis BLOCKIN(PCA) BLOCKINto BLOCKINthe BLOCKINfield BLOCKINof BLOCKINdata BLOCKINmin-ing in electroencephalogram (EEG) processing. The principal components are estimated from the signal by eigen decomposition of the covariance estimate of the input. Alternatively, they can be estimated by a BLOCKINneural BLOCKINnetwork BLOCKIN(NN) BLOCKINconfigured BLOCKINfor BLOCKINextracting BLOCKINthe BLOCKINfirst BLOCKINprincipal BLOCKINcomponents. BLOCKINInstead BLOCKINof BLOCKINperforming computationally complex operations for eigenvector estimation, the neural network can be trained to produce BLOCKINordered BLOCKINfirst BLOCKINprincipal BLOCKINcomponents. BLOCKINPossible BLOCKINapplications BLOCKINinclude BLOCKINseparation BLOCKINof BLOCKINdifferent BLOCKINsignal components for feature BLOCKINextraction BLOCKINin BLOCKINthe BLOCKINfield BLOCKINof BLOCKINEEG signal processing, adaptive segmentation, epileptic spike detection, and long-term EEG monitoring evaluation of patients in a coma.

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