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Mu rhythm separation from the mix with alpha rhythm: Principal component analyses and factor topography

Z. Garakh, V. Novototsky-Vlasov, E. Larionova, Y. Zaytseva

. 2020 ; 346 (-) : 108892. [pub] 20200805

Language English Country Netherlands

Document type Journal Article, Research Support, Non-U.S. Gov't

Persistent link   https://www.medvik.cz/link/bmc21019745

BACKGROUND: EEG mu rhythm suppression is assessed in experiments on the execution, observation and imagination of movements. It is utilised for studying of actions, language, empathy in healthy individuals and preservation of sensorimotor system functions in patients with schizophrenia and autism spectrum disorders. While EEG alpha and mu rhythms are recorded in the same frequency range (8-13 Hz), their specification becomes a serious issue. THE NEW METHOD: is based on the spatial and functional characteristics of the mu wave, which are: (1) the mu rhythm is located over the sensorimotor cortex; (2) it desynchronises during movement processing and does not respond on the eyes opening. In EEG recordings, we analysed the mu rhythm under conditions with eyes opened and eyes closed (baseline), and during a motor imagery task with eyes closed. EEG recordings were processed by principal component analysis (PCA). RESULTS: The analysis of EEG data with the proposed approach revealed the maximum spectral power of mu rhythm localised in the sensorimotor areas. During motor imagery, mu rhythm was suppressed more in frontal and central sites than in occipital sites, whereas alpha rhythm was suppressed more in parietal and occipital sites. Mu rhythm desynchronization in sensorimotor sites during motor imagery was greater than alpha rhythm desynchronization. The proposed method enabled EEG mu rhythm separation from its mix with alpha rhythm. CONCLUSIONS: EEG mu rhythm separation with the proposed method satisfies its classical definition.

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$a BACKGROUND: EEG mu rhythm suppression is assessed in experiments on the execution, observation and imagination of movements. It is utilised for studying of actions, language, empathy in healthy individuals and preservation of sensorimotor system functions in patients with schizophrenia and autism spectrum disorders. While EEG alpha and mu rhythms are recorded in the same frequency range (8-13 Hz), their specification becomes a serious issue. THE NEW METHOD: is based on the spatial and functional characteristics of the mu wave, which are: (1) the mu rhythm is located over the sensorimotor cortex; (2) it desynchronises during movement processing and does not respond on the eyes opening. In EEG recordings, we analysed the mu rhythm under conditions with eyes opened and eyes closed (baseline), and during a motor imagery task with eyes closed. EEG recordings were processed by principal component analysis (PCA). RESULTS: The analysis of EEG data with the proposed approach revealed the maximum spectral power of mu rhythm localised in the sensorimotor areas. During motor imagery, mu rhythm was suppressed more in frontal and central sites than in occipital sites, whereas alpha rhythm was suppressed more in parietal and occipital sites. Mu rhythm desynchronization in sensorimotor sites during motor imagery was greater than alpha rhythm desynchronization. The proposed method enabled EEG mu rhythm separation from its mix with alpha rhythm. CONCLUSIONS: EEG mu rhythm separation with the proposed method satisfies its classical definition.
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$a Novototsky-Vlasov, Vladimir $u Institute of Higher Nervous Activity and Neurophysiology of Russian Academy of Science, Moscow, Russian Federation; Serbsky National Medical Research Centre for Psychiatry and Narcology, Moscow, Russian Federation
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$a Larionova, Ekaterina $u Institute of Higher Nervous Activity and Neurophysiology of Russian Academy of Science, Moscow, Russian Federation
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$a Zaytseva, Yuliya $u National Institute of Mental Health, Klecany, Czech Republic; Department of Psychiatry and Medical Psychology, 3rd Faculty of Medicine, Charles University in Prague, Prague, Czech Republic; Human Science Centre, Institute of Medical Psychology, Ludwig-Maximilian University, Munich, Germany. Electronic address: yuliya.zaytseva@gmail.com
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