Studium molekulovej podstaty akutnych porfyrii

Available from STL Prague, CZ / NTK - National Technical LibrarySIGLECZCzech Republi

Robust feature extraction and classification of EEG spectra for realtime classification of cognitive state

We developed an algorithm to extract and combine EEG spectral features, which effectively classifies cognitive states and is robust in the presence of sensor noise. The algorithm uses a partial-least squares (PLS) algorithm to decompose multi-sensor EEG spectra into a small set of components. These components are chosen such that they are linearly orthogonal to each other and maximize the covariance between the EEG input variables and discrete output variables, such as different cognitive states. A second stage of the algorithm uses robust cross-validation methods to select the optimal number of components for classification. The algorithm can process practically unlimited input channels and spectral resolutions. No a priori information about the spatial or spectral distributions of the sources is required. A final stage of the algorithm uses robust cross-validation methods to reduce the set of electrodes to the minimum set that does not sacrifice classification accuracy. We tested the algorithm with simulated EEG data in which mental fatigue was represented by increases frontal theta and occipital alpha band power. We synthesized EEG from bilateral pairs of frontal theta sources and occipital alpha sources generated by second-order autoregressive processes. We then excited the sources with white noise and mixed the source signals into a 19channel sensor array (10-20 system) with the three-sphere head model of the BESA Dipole Simulator. We generated synthetic EEG for 60 2-second long epochs. Separate EEG series represented the alert and fatigued states, betwee