Sources of the Electrical Activity of Brain Areas Involving in Imaginary Movements
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Sources of the Electrical Activity of Brain Areas Involving in Imaginary Movements Ya. V. Kerechanin,1,2 D. Husek,3 P. D. Bobrov,1,2 I. R. Fedotova,1 and A. A. Frolov1,2
UDC 612.821
Translated from Zhurnal Vysshei Nervnoi Deyatel’nosti imeni I. P. Pavlova, Vol. 69, No. 6, pp. 711–725, November–December, 2019. Original article submitted February 15, 2019. Revised version received April 22, 2019. Accepted June 3, 2019. We describe the most significant sources of electrophysiological brain activity identified during use of a brain–computer interface based on recognition of EEG patterns during imaginary movements. The main tool for identifying sources consisted of six independent components analysis (ICA) methods based on different criteria of independence. Measures of the significance of sources were: their occurrence rates in different experimental sessions; repetition of extraction in each session using different ICA methods; the effects of each source on of recognition accuracy for EEG patterns corresponding to different imaginary movements, and the potential for approximating the activity of an individual current dipole. The overall set of indicators identified five sources located in the primary somatosensory cortex of both hemispheres, in the left premotor area, the supplementary motor area, and the precuneus. The functional significance of these sources in the framework of the contemporary concepts of the interaction of brain areas supporting the execution of motor functions is discussed. Keywords: brain–computer interface, neurointerface, EEG, imaginary movements, synchronization and desynchronization of EEG activity, independent components analysis methods, EEG inverse problem.
Introduction. Brain–computer interfaces (BCI) are programmable systems allowing external technical devices to be controlled directly by brain signals, bypassing the muscle activity natural for this. The widest use of BCI is linked with motor rehabilitation of poststroke and posttraumatic patients. Several large studies have now been carried out demonstrating the effectiveness of training to control BCI based on imaginary movements for recovery of motor functions in such patients [Ang et al., 2015; Frolov et al., 2017; Cervera et al., 2018]. However, apart from applied value, the importance of this training for studies of the fundamental manifestations of brain activity, first noted
by Frolov et al. [2012], should be noted. Using biological feedback, subjects are trained to stabilize and contrast brain activity patterns on performance of mental tasks used for controlling BCI [Sitaram et al., 2016]. This provides the most reliable, stable, and significant extraction and investigation of these patterns and the best understanding of the characteristics of brain operation on performance of the corresponding mental tasks. The main tools used in our studies for investigating brain activity during control of BCI based on various imaginary movements are independent components analysis (ICA) methods with solution of the EEG inverse problem t
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