Electrophysiological brain activity during the control of a motor imagery-based brain–computer interface

Frolov, Alexander A.; Aziatskaya, G.; Bobrov, Pavel; Luykmanov, R. Kh.; Fedotova, I. R.; Húsek, Dušan; Snåšel, Václav

doi:10.1134/s036211971705005x

Cited by 7 publications

(16 citation statements)

References 36 publications

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“…Spatial filtration was performed by using independent component analysis (ICA) to identify the sensorimotor component of the EEG of each subject for the ipsi-and contralateral hemispheres (Frolov et al, 2017). Individual spatial patterns were identified and obtained for 11 subjects (one subject was excluded from further analysis), and showed the central and centroparietal activation in the corresponding EEG channels, overlaying the area of the sensorimotor cortex.…”

Section: Resultsmentioning

confidence: 99%

“…Within the theory of constructive operators (Pascual-Leone, 1970) mental-attention represents a domain general resource that enables problem solving and serves as the maturational component of working memory (Arsalidou et al, 2010). According to Pascual-Leone & Baillargeon (1994), the capacity of mental attention (M-operator) is limited at every developmental stage by the quantity of separate "chunks" of information, which a child can keep and operate in its own mind simultaneously or the number of schemes that can be picked from one's repertoire and activated by M-operator all at one time using "mental effort". A classic measure of mental attentional capacity is the Figural Intersection Task (FIT).…”

Section: Discussionmentioning

confidence: 99%

“…Participants are asked to look at all the figures on the right side and afterwards on the left side to put only one dot into all the figures that come from the right side while ignoring extra shapes, which are absent on the right side. The items with highest number of figures a child can solve correspond to his or her mental-attentional capacity level (Pascual-Leone & Baillargeon, 1994). Theoretically after the age of 3 years mental-attentional capacity increases by one unit every other year until the age of 15-16 years when it reaches 7 units (Pascual-Leone, 1970.…”

Section: Discussionmentioning

confidence: 99%

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Actual problems of psychological science

Volkova¹,

Petrosyan²,

Dubyshkin³

et al. 2019

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федеральное государственное автономное образовательное учреждение высшего образования «Национальный исследовательский университет «Высшая школа экономики» Актуальные проблемы психологической науки Сборник статей и выступлений международной научной конференции г. Москва, 10-12 мая 2018 г. Под редакцией Горбуновой Е.С. Научно-инновационный центр Красноярск, 2019 УДК 159.9 ББК 88.3 А43 А43 Актуальные проблемы психологической науки: Сборник статей и выступлений международной научной конференции (г. Москва, 10-12 мая 2018 г.) [Электронный ресурс] / под редакцией Горбуновой Е.С.-Электрон. текстовые дан.-Красноярск: Научно-инновационный центр, 2019.-432 с.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Actual problems of psychological science

Volkova¹,

Petrosyan²,

Dubyshkin³

et al. 2019

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“…The original EEG dataset for the work was obtained using the same procedure as described in earlier works (Frolov et al, 2012(Frolov et al, , 2017a. Each subject had to sit relaxed, looking at the center of the screen or had to imagine flexion of either his left or right hand according to visual cue presented on the screen.…”

Section: Methodsmentioning

confidence: 99%

Using Multiple Decomposition Methods and Cluster Analysis to Find and Categorize Typical Patterns of EEG Activity in Motor Imagery Brain–Computer Interface Experiments

et al. 2020

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In this study, the sources of EEG activity in motor imagery brain–computer interface (BCI) control experiments were investigated. Sixteen linear decomposition methods for EEG source separation were compared according to different criteria. The criteria were mutual information reduction between the source activities and physiological plausibility. The latter was tested by estimating the dipolarity of the source topographic maps, i.e., the accuracy of approximating the map by potential distribution from a single current dipole, as well as by the specificity of the source activity for different motor imagery tasks. The decomposition methods were also compared according to the number of shared components found. The results indicate that most of the dipolar components are found by the Independent Component Analysis Methods AMICA and PWCICA, which also provided the highest information reduction. These two methods also found the most task-specific EEG patterns of the blind source separation algorithms used. They are outperformed only by non-blind Common Spatial Pattern methods in terms of pattern specificity. The components found by all of the methods were clustered using the Attractor Neural Network with Increasing Activity. The results of the cluster analysis revealed the most frequent patterns of electrical activity occurring in the experiments. The patterns reflect blinking, eye movements, sensorimotor rhythm suppression during the motor imagery, and activations in the precuneus, supplementary motor area, and premotor areas of both hemispheres. Overall, multi-method decomposition with subsequent clustering and task-specificity estimation is a viable and informative procedure for processing the recordings of electrophysiological experiments.

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“…The EEG analysis was based on the previously developed methodology which was applied to find sources of electrophysiological brain activity the most specific for motor imagery during the BCI control by both healthy subjects and post-stroke patients with mild subcortical lesions (16–18). The hemodynamic activity was investigated through the comparative analysis of fMRI data before and after the course of rehabilitation during the final hospitalization period.…”

Section: Introductionmentioning

confidence: 99%

Electrical, Hemodynamic, and Motor Activity in BCI Post-stroke Rehabilitation: Clinical Case Study

et al. 2018

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The goal of the paper is to present an example of integrated analysis of electrical, hemodynamic, and motor activity accompanying the motor function recovery in a post-stroke patient having an extensive cortical lesion. The patient underwent a course of neurorehabilitation assisted with the hand exoskeleton controlled by brain-computer interface based on kinesthetic motor imagery. The BCI classifier was based on discriminating covariance matrices of EEG corresponding to motor imagery. The clinical data from three successive 2 weeks hospitalizations with 4 and 8 month intervals, respectively were under analysis. The rehabilitation outcome was measured by Fugl-Meyer scale and biomechanical analysis. Both measures indicate prominent improvement of the motor function of the paretic arm after each hospitalization. The analysis of brain activity resulted in three main findings. First, the sources of EEG activity in the intact brain areas, most specific to motor imagery, were similar to the patterns we observed earlier in both healthy subjects and post-stroke patients with mild subcortical lesions. Second, two sources of task-specific activity were localized in primary somatosensory areas near the lesion edge. The sources exhibit independent mu-rhythm activity with the peak frequency significantly lower than that of mu-rhythm in healthy subjects. The peculiarities of the detected source activity underlie changes in EEG covariance matrices during motor imagery, thus serving as the BCI biomarkers. Third, the fMRI data processing showed significant reduction in size of areas activated during the paretic hand movement imagery and increase for those activated during the intact hand movement imagery, shifting the activations to the same level. This might be regarded as the general index of the motor recovery. We conclude that the integrated analysis of EEG, fMRI, and motor activity allows to account for the reorganization of different levels of the motor system and to provide a comprehensive basis for adequate assessment of the BCI+ exoskeleton rehabilitation efficiency.

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Electrophysiological brain activity during the control of a motor imagery-based brain–computer interface

Cited by 7 publications

References 36 publications

Actual problems of psychological science

Actual problems of psychological science

Using Multiple Decomposition Methods and Cluster Analysis to Find and Categorize Typical Patterns of EEG Activity in Motor Imagery Brain–Computer Interface Experiments

Electrical, Hemodynamic, and Motor Activity in BCI Post-stroke Rehabilitation: Clinical Case Study

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