Effects of Motor Imagery Tasks on Brain Functional Networks Based on EEG Mu/Beta Rhythm

Yu, Hongli; Ba, Sidi; Guo, Yuxue; Guo, Lei; Xu, Guizhi

doi:10.3390/brainsci12020194

Cited by 21 publications

(12 citation statements)

References 48 publications

(48 reference statements)

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“…A popular topic in analyzing EEG is during motor imagery (MI) tasks, which are dynamic states of movement imagination during which primary sensorimotor areas exhibit patterns of neural activity that resembles an attenuated version of real executed movement [2,3]. From neurophysiological perspective, desynchronization of the neural populations during motor imagery tasks attenuates rhythms in the respective cortex and can be measured as a sign of brain activity [3,4]. MI tasks are extensively utilized in brain-computer interface (BCI) systems, in which mental imagination of a movement is translated to executive commands via classification of features extracted from acquired EEG data [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Spectral Representation of EEG Data using Learned Graphs with Application to Motor Imagery Decoding

Miri

Abootalebi

Saeedi-Sourck

et al. 2022

Preprint

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Objective: This paper presents a graph signal processing (GSP)-based approach for decoding two-class motor imagery EEG data via deriving task-specific discriminative features. Methods: First, a graph learning (GL) method is used to learn subject-specific graphs from EEG signals. Second, by diagonalizing the normalized Laplacian matrix of each subject graph, an orthonormal basis is obtained using which the graph Fourier transform (GFT) of the EEG signals is computed. Third, the GFT coefficients are mapped into a discriminative subspace for differentiating two class data using a projection matrix obtained by the Fukunaga-Koontz transform (FKT). Finally, an SVM classifier is trained and tested on the variance of the resulting features to differentiate motor imagery classes. Results: The proposed method is evaluated on Dataset IVa of the BCI Competition III and its performance is compared to i) using features extracted on a graph constructed by Pearson correlation coefficients and ii) three state-of-the-art alternative methods. Conclusion: Experimental results indicate the superiority of the proposed method over alternative methods, reflecting the added benefit of integrating elements from GL, GSP and FKT. Significance: The proposed method and results underpin the importance of integrating spatial and temporal characteristics of EEG signals in extracting features that can more powerfully differentiate motor imagery classes.

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Section: Introductionmentioning

confidence: 99%

Spectral Representation of EEG Data using Learned Graphs with Application to Motor Imagery Decoding

Miri

Abootalebi

Saeedi-Sourck

et al. 2022

Preprint

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“…Recently, several studies have found the motor mental practice can provide several benefits in patients with neurological disorders ( Kim and Lee, 2015 ; Gil-Bermejo-Bernardez-Zerpa et al, 2021 ). Changes in the functional network induced by motor imagery may contribute to the recovery from brain dysfunction ( Bajaj et al, 2015 ; Yu et al, 2022 ). Especially, imaging motors have been reported to have similar patterns of brain activity to real execution ( Kraeutner et al, 2014 ).…”

Section: Discussionmentioning

confidence: 99%

Neural oscillations during acupuncture imagery partially parallel that of real needling

et al. 2023

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IntroductionTasks involving mental practice, relying on the cognitive rehearsal of physical motors or other activities, have been reported to have similar patterns of brain activity to overt execution. In this study, we introduced a novel imagination task called, acupuncture imagery and aimed to investigate the neural oscillations during acupuncture imagery.MethodsHealthy volunteers were guided to watch a video of real needling in the left and right KI3 (Taixi point). The subjects were then asked to perform tasks to keep their thoughts in three 1-min states alternately: resting state, needling imagery left KI3, and needling imagery right KI3. Another group experienced real needling in the right KI3. A 31-channel-electroencephalography was synchronously recorded for each subject. Microstate analyses were performed to depict the brain dynamics during these tasks.ResultsCompared to the resting state, both acupuncture needling imagination and real needling in KI3 could introduce significant changes in neural dynamic oscillations. Moreover, the parameters involving microstate A of needling imagery in the right KI3 showed similar changes as real needling in the right KI3.DiscussionThese results confirm that needling imagination and real needling have similar brain activation patterns. Needling imagery may change brain network activity and play a role in neural regulation. Further studies are needed to explore the effects of acupuncture imagery and the potential application of acupuncture imagery in disease recovery.

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“…The frontal regions are involved in the processing of MI, and they may be differentially responsive ( Buch et al, 2012 ; Yu et al, 2022 ). The current study adds to this literature that all frontal regions were shown differently between groups while demonstrating greater activation in stroke with the impaired MI ability compared with that of healthy controls.…”

Section: Discussionmentioning

confidence: 99%

Brain Function and Upper Limb Deficit in Stroke With Motor Execution and Imagery: A Cross-Sectional Functional Magnetic Resonance Imaging Study

Hua

et al. 2022

Front. Neurosci.

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BackgroundMotor imagery training might be helpful in stroke rehabilitation. This study explored if a specific modulation of movement-related regions is related to motor imagery (MI) ability.MethodsTwenty-three patients with subcortical stroke and 21 age-matched controls were recruited. They were subjectively screened using the Kinesthetic and Visual Imagery Questionnaire (KVIQ). They then underwent functional magnetic resonance imaging (fMRI) while performing three repetitions of different motor tasks (motor execution and MI). Two separate runs were acquired [motor execution tasks (ME and rest) and motor imagery (MI and rest)] in a block design. For the different tasks, analyses of cerebral activation and the correlation of motor/imagery task-related activity and KVIQ scores were performed.ResultsDuring unaffected hand (UH) active grasp movement, we observed decreased activations in the contralateral precentral gyrus (PreCG), contralateral postcentral gyrus (PoCG) [p < 0.05, family wise error (FWE) corrected] and a positive correlation with the ability of FMA-UE (PreCG: r = 0.46, p = 0.028; PoCG: r = 0.44, p = 0.040). During active grasp of the affected hand (AH), decreased activation in the contralateral PoCG was observed (p < 0.05, FWE corrected). MI of the UH induced significant activations of the contralateral superior frontal gyrus, opercular region of the inferior frontal gyrus, and ipsilateral ACC and deactivation in the ipsilateral supplementary motor area (p < 0.05, AlphaSim correction). Ipsilateral anterior cingulate cortex (ACC) activity negatively correlated with MI ability (r = =–0.49, p = 0.022). Moreover, we found significant activation of the contralesional middle frontal gyrus (MFG) during MI of the AH.ConclusionOur results proved the dominant effects of MI dysfunction that exist in stroke during the processing of motor execution. In the motor execution task, the enhancement of the contralateral PreCG and PoCG contributed to reversing the motor dysfunction, while in the MI task, inhibition of the contralateral ACC can increase the impaired KVIQ ability. The bimodal balance recovery model can explain our results well. Recognizing neural mechanisms is critical to helping us formulate precise strategies when intervening with electrical or magnetic stimulation.

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Effects of Motor Imagery Tasks on Brain Functional Networks Based on EEG Mu/Beta Rhythm

Cited by 21 publications

References 48 publications

Spectral Representation of EEG Data using Learned Graphs with Application to Motor Imagery Decoding

Spectral Representation of EEG Data using Learned Graphs with Application to Motor Imagery Decoding

Neural oscillations during acupuncture imagery partially parallel that of real needling

Brain Function and Upper Limb Deficit in Stroke With Motor Execution and Imagery: A Cross-Sectional Functional Magnetic Resonance Imaging Study

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