Electrographic Correlates of Actual and Imagined Movements: Spectral Analysis

Kiroi, V. N.; Vladimirskii, B. M.; Aslanyan, E. V.; Bakhtin, O. M.; Minyaeva, N. R.

doi:10.1007/s11055-011-9527-1

Cited by 8 publications

(6 citation statements)

References 12 publications

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“…We have also shown earlier [12,56] that mental representation of movements leads to additional activation, not only of the central motor cortex but also of the frontal and temporal cortical areas. At the same time, sufficiently pronounced phenomena associated with motor imagination were recorded at gamma frequencies.…”

Section: Discussionsupporting

confidence: 72%

“…On the one hand, within this paradigm, it is convenient to configure and test computational methods for recognizing control EEG commands and monitoring the user's training for their reproduction. On the other hand, control stimuli affect the EEG of a movement's mental performance, since the MI signal and the components of the evoked potential are superposed [11,12]. Subsequently, this complicates the operation of the BCIs configured in this way in the control mode, in which the MI activity is performed by the user in an arbitrary manner without reference to external stimuli.…”

Section: Introductionmentioning

confidence: 99%

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Discriminative Frequencies and Temporal EEG Segmentation in the Motor Imagery Classification Approach

et al. 2022

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A linear discriminant analysis transformation-based approach to the classification of three different motor imagery types for brain–computer interfaces was considered. The study involved 16 conditionally healthy subjects (12 men, 4 women, mean age of 21.5 years). First, the search for subject-specific discriminative frequencies was conducted in the task of movement-related activity. This procedure was shown to increase the classification accuracy compared to the conditional common spatial pattern (CSP) algorithm, followed by a linear classifier considered as a baseline approach. In addition, an original approach to finding discriminative temporal segments for each motor imagery was tested. This led to a further increase in accuracy under the conditions of using Hjorth parameters and interchannel correlation coefficients as features calculated for the EEG segments. In particular, classification by the latter feature led to the best accuracy of 71.6%, averaged over all subjects (intrasubject classification), and, surprisingly, it also allowed us to obtain a comparable value of intersubject classification accuracy of 68%. Furthermore, scatter plots demonstrated that two out of three pairs of motor imagery were discriminated by the approach presented.

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Section: Discussionsupporting

confidence: 72%

Section: Introductionmentioning

confidence: 99%

Discriminative Frequencies and Temporal EEG Segmentation in the Motor Imagery Classification Approach

et al. 2022

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“…The purpose of the paper is to implement a brain-computer interface and it does not report ERS values. In a study by [22], greater GBA activity is observed during the imaginary movement than during the actual movement. This contrasts with our results, in which we found greater activity in the actual movement (ERSacM (%) = 27.479 ± 13.256, ERSimM (%) = 15.983 (14.313), with no significant difference).…”

Section: Discussionmentioning

confidence: 90%

Induced Gamma-Band Activity during Actual and Imaginary Movements: EEG Analysis

Usanos

Boquete

Santiago

et al. 2020

Sensors

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The purpose of this paper is to record and analyze induced gamma-band activity (GBA) (30–60 Hz) in cerebral motor areas during imaginary movement and to compare it quantitatively with activity recorded in the same areas during actual movement using a simplified electroencephalogram (EEG). Brain activity (basal activity, imaginary motor task and actual motor task) is obtained from 12 healthy volunteer subjects using an EEG (Cz channel). GBA is analyzed using the mean power spectral density (PSD) value. Event-related synchronization (ERS) is calculated from the PSD values of the basal GBA (GBAb), the GBA of the imaginary movement (GBAim) and the GBA of the actual movement (GBAac). The mean GBAim and GBAac values for the right and left hands are significantly higher than the GBAb value (p = 0.007). No significant difference is detected between mean GBA values during the imaginary and actual movement (p = 0.242). The mean ERS values for the imaginary movement (ERSimM (%) = 23.52) and for the actual movement (ERSacM = 27.47) do not present any significant difference (p = 0.117). We demonstrated that ERS could provide a useful way of indirectly checking the function of neuronal motor circuits activated by voluntary movement, both imaginary and actual. These results, as a proof of concept, could be applied to physiology studies, brain–computer interfaces, and diagnosis of cognitive or motor pathologies.

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“…Mental images of the same movements engaged multiple regions with greater gamma activation. 5 A recent study conducted in Russia on 24 healthy subjects found that the sensorimotor and associative areas of both hemispheres of the brain were actively involved in the execution of real voluntary as well as mental imagery movements, especially in rapid gamma frequencies. The EEG power of the delta (δ) and theta (θ) frequencies also increase, on the background with marked reductions in the alpha (α) and beta (β1) frequencies during both movements.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Spectral Analysis of Gamma Band Activity During Actual and Imagined Movements as a Cognitive Tool

Kumawat,

Yadav,

Yadav

et al. 2023

Clin EEG Neurosci

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Background. Imagined motor movement is a cognitive process in which a subject imagines a movement without doing it, which activates similar brain regions as during actual motor movement. Brain gamma band activity (GBA) is linked to cognitive functions such as perception, attention, memory, awareness, synaptic plasticity, motor control, and Imagination. Motor imagery can be used in sports to improve performance, raising the possibility of using it as a rehabilitation method through brain plasticity through mirror neurons. Method. A comparative observational study was conducted on 56 healthy male subjects after obtaining clearance from the Ethics Committee. EEG recordings for GBA were taken for resting, real, and imaginary motor movements and compared. The power spectrum of gamma waves was analyzed using the Kruskal–Wallis test; a p-value <.05 was considered significant. Results. The brain gamma rhythm amplitude was statistically increased during both actual and imaginary motor movement compared to baseline (resting stage) in most of the regions of the brain except the occipital region. There was no significant difference in GBA between real and imaginary movements. Conclusions. Increased gamma rhythm amplitude during both actual and imaginary motor movement than baseline (resting stage) indicating raised brain cognitive activity during both types of movements. There was no potential difference between real and imaginary movements suggesting that the real movement can be replaced by the imaginary movement to enhance work performance through mirror therapy.

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Electrographic Correlates of Actual and Imagined Movements: Spectral Analysis

Cited by 8 publications

References 12 publications

Discriminative Frequencies and Temporal EEG Segmentation in the Motor Imagery Classification Approach

Discriminative Frequencies and Temporal EEG Segmentation in the Motor Imagery Classification Approach

Induced Gamma-Band Activity during Actual and Imaginary Movements: EEG Analysis

Comparison of Spectral Analysis of Gamma Band Activity During Actual and Imagined Movements as a Cognitive Tool

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