1. Source localization of low- and high-amplitude alpha activity: A segmental and DSS analysis 1 - Laboratory of Computer and Information Science, Helsinki University of Technology, Helsinki, Finland. 2 - Human Physiology dept., Biological faculty, Moscow State University, Moscow, Russia. S. Borisov 1,2, A. Ilin 1, R. Vigário 1, A. Kaplan 2 Contacts: sborisov@james.hut.fi; http://cis.hut.fi; http://brain.bio.msu.ru Introduction Due to the very well known problem of EEG nonstationarity [1], the segmental analysis has been proposed for representation of EEG signal as a sequence of quasi-stationary segments and estimation of their characteristics [2,3]. Using this method, we have shown the meaningful differences between the segment characteristics in the different functional states (sleep-awaking cycle [2], cognitive loads [4,5], influence of pharmacological agents [6], schizophrenia spectrum disorders [7]). We have proposed that the alpha-activity segment characteristics reflect the properties of the cortical neuronal ensembles. In particular, the average amplitude and duration of quasi-stationary segment are the reflection of the volume and lifespan of the neuronal ensemble localized in EEG registration area [3]. These facts motivated the hypothesis that high- and low-amplitude quasi-stationary segments of alpha activity could be used as a markers of the neuronal ensembles with the different cortex localization. We applied one kind of source separation techniques – denoising source separation (DSS) – to test this hypothesis. DSS is the recently developed powerful method for source separation, which comprises both blind techniques, e.g. independent component analysis, and more tuned methods, when additional information exists [8]. The assumed model is: X=A*S, where X is mixed signal (EEG), S is uncorrelated sources and A is mixing matrix. Results The average relative power spectra of the sources within the clusters. The relative spectral power of the sources in alpha band ± standard error of mean (in per cent to the spectral power in the range 1-30 Hz) is denoted in the right upper corner of the diagrams. The average (within the clusters) relative source contribution to overall variance of the native signal. The standard error of mean is marked. 2. Spectral characteristics of the extracted source clusters: 3. The power of the extracted source clusters: (I) n=8 (II) n=6 (III) n=5(IV) n=5 (V) n=7(VI) n=6 We found 6 different clusters of the extracted alpha sources with similar spatial patterns: 2 for high- and 4 for low-amplitude dataset. 1. Spatial patterns of the extracted source clusters: The averaged spatial patterns of the sources within the clusters. n – the number of sources in the clusters. All sources within the same cluster belong to different subjects (of 9). High-amplitude dataset Low-amplitude dataset The analysis of uncorrelated components extracted from EEG fragments corresponding to high- or low-amplitude alpha rhythm segments have shown that these components could be essentially different. Methods 1. EEG registration: 9 healthy volunteers in rest condition (eyes closed); 16 channels according to standard 10/20 scheme. 2. EEG data preprocessing : Removing artifacts; detecting the channel with most powerful alpha-activity (spectral analysis). applying the obtained borders of quasi-stationary segments for rest of the channels in the EEG recording. 3. Segmental analysis: Segmentation of the most alpha-rhythmical channel; 6. Cluster analysis: Clusterization of the spatial patterns of the first 8 most alpha- rhythmical extracted sources using K-means algorithm taking into account the sign indeterminacy of the extracted sources. References 1. Barlow JS. Methods of analysis of nonstationary EEGs, with emphasis on segmentation techniques: a comparative review. J Clin Neurophysiol. 1985 Jul;2(3):267-304. 2. Kaplan A, Roschke J, Darkhovsky B, Fell J. Macrostructural EEG characterization based on nonparametric change point segmentation: application to sleep analysis. J Neurosci Methods. 2001 Mar 30;106(1):81-9. 3. Kaplan AYa, Borisov SV, Shishkin SL, Ermolaev VA. Analysis of the segmental structure of EEG alpha-activity in humans. Ross Fiziol Zh Im I M Sechenova. 2002 Apr;88(4):432-42. 4. Kaplan AYa, Borisov SV. Dynamic properties of segmental characteristics of EEG alpha activity in rest conditions and during cognitive tasks. Zh Vyssh Nerv Deiat Im I P Pavlova. 2003 Jan-Feb;53(1):22-32. 5. Fingelkurts An.A., Fingelkurts Al.A., Krause C.M., Kaplan A.Ya., Borisov S.V. and Sams M. Structural (operational) synchrony of EEG alpha activity during an auditory memory task. NeuroImage. 2003. V.20. P.529-542. 6. Fingelkurts AA, Fingelkurts AA, Kivisaari R, Pekkonen E, Ilmoniemi RJ, Kahkonen S. The interplay of lorazepam- induced brain oscillations: microstructural electromagnetic study. Clin Neurophysiol. 2004 Mar;115(3):674-90. 7. Borisov S.V., Kaplan A.Ya., Gorbachevskaya N.L., Kozlova I.A. Segmental Structure of the EEG Alpha Activity in Adolescents With Schizophrenia-Spectrum Disorders. Zh Vyssh Nerv Deiat Im I P Pavlova. 2005, V 55. P.351-357. 8. Sarela J., Valpola H. Denoising Source Separation. Journal of machine learning research. 2005, 6. P.233–272. Conclusions Differences in the signal components found for the two datasets, suggest that EEG fragments corresponding to high- or low-amplitude quasi-stationary segments of occipital alpha-activity are produced by different neuronal sources. That means that high- and low-amplitude occipital alpha segments can be considered as markers of activation for different sets of neuronal ensembles. COPIES 5. DSS: Here, DSS was tuned to extract sources with most prominent alpha- activity. The sources are uncorrelated and ranked according to the amount of alpha frequencies in their power spectra. This algorithm includes 3 stages: Extracted sources (S)Spatial patterns (the columns of matrix A) Whitening PCA Filtering in the alpha frequency band (7-13 Hz) Low-amplitude EEG fragments Low-amplitude dataset High-amplitude EEG fragments High-amplitude dataset DSS 4. Data processing: Dividing EEG data into 2 datasets according to the average amplitude of occipital alpha-segments (above or below the median of average amplitude within all segments in the particular EEG channel).