Advanced Electroencephalogram Processing: Automatic Clustering of EEG Components

Diana Rashidovna Golomolzina, Maxim Alexandrovich Gorodnichev, Evgeny Andreevich Levin, Alexander Nikolaevich Savostyanov, Ekaterina Pavlovna Yablokova, Arthur C. Tsai, Mikhail Sergeevich Zaleshin, Anna Vasil'evna Budakova, Alexander Evgenyevich Saprygin, Mikhail Anatolyevich Remnev and Nikolay Vladimirovich Smirnov
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Diana Rashidovna Golomolzina: Laboratory of Intel-NSU, Novosibirsk State University, Novosibirsk, Russia
Maxim Alexandrovich Gorodnichev: Institute of Computational Mathematics and Mathematical Geophysics SB RAS, Laboratory of Intel-NSU, Novosibirsk State University, Novosibirsk, Russia
Evgeny Andreevich Levin: Novosibirsk Research Institute of Circulation Pathology, Novosibirsk, Russia & Institute of Physiology and Fundamental Medicine, Novosibirsk, Russia
Alexander Nikolaevich Savostyanov: Institute of Physiology and Fundamental Medicine, Novosibirsk State University, Novosibirsk, Russia & Tomsk State University, Tomsk, Russia
Ekaterina Pavlovna Yablokova: Novosibirsk State University, Novosibirsk, Russia
Arthur C. Tsai: Institute of Statistical Science, Academia Sinica, Taipei, Taiwan
Mikhail Sergeevich Zaleshin: Tomsk State University, Tomsk, Russia
Anna Vasil'evna Budakova: Tomsk State University, Tomsk, Russia
Alexander Evgenyevich Saprygin: Novosibirsk State University, Novosibirsk, Russia
Mikhail Anatolyevich Remnev: Novosibirsk State University, Novosibirsk, Russia
Nikolay Vladimirovich Smirnov: Novosibirsk State University, Novosibirsk, Russia

International Journal of E-Health and Medical Communications (IJEHMC), 2014, vol. 5, issue 2, 49-69

Abstract: The study of electroencephalography (EEG) data can involve independent component analysis and further clustering of the components according to relation of the components to certain processes in a brain or to external sources of electricity such as muscular motion impulses, electrical fields inducted by power mains, electrostatic discharges, etc. At present, known methods for clustering of components are costly because require additional measurements with magnetic-resonance imaging (MRI), for example, or have accuracy restrictions if only EEG data is analyzed. A new method and algorithm for automatic clustering of physiologically similar but statistically independent EEG components is described in this paper. Developed clustering algorithm has been compared with algorithms implemented in the EEGLab toolbox. The paper contains results of algorithms testing on real EEG data obtained under two experimental tasks: voluntary movement control under conditions of stop-signal paradigm and syntactical error recognition in written sentences. The experimental evaluation demonstrated more than 90% correspondence between the results of automatic clustering and clustering made by an expert physiologist.

Date: 2014
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Persistent link: https://EconPapers.repec.org/RePEc:igg:jehmc0:v:5:y:2014:i:2:p:49-69

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