Global EEG segmentation using singular value decomposition

In this paper, we propose a method based on singular value decomposition (SVD) for segmenting multichannel electroencephalography (EEG) data into temporal blocks during which the spatial distributions of the underlying active neuronal generators stay fixed. We locate segment boundaries by statistically comparing the residual error resulting from projecting the data under a reference window, on one hand, and a sliding window, on the other hand, onto a feature subspace. The basis of this subspace is the most significant left eigenvectors of the data block under the reference window. The statistical testing is performed using the Kolmogorov-Smirnov (K-S) test. To enhance the reliability of the K-S test, the consecutive K-S decisions are aggregated under a given decision window. Simulation results confirm that the proposed algorithm can successfully detect segment boundaries under a wide range of different conditions.