Cross-association analysis of EEG and EMG signals according to movement intention state

Kim, Byeongnam; Kim, Laehyun; Kim, Yun-Hee; Yoo, Sun Kook

doi:10.1016/j.cogsys.2017.02.001

Cited by 55 publications

(51 citation statements)

References 29 publications

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“…By examining cerebral cortical changes after intervention, Kim et al. 10 ) found positive changes in EEG signals of stroke patients after passive exercise. Repeated passive exercise led to an increase in β-waves in the C3 and C4 areas, i.e., the primary motor areas in the brain, confirming that passive movement is effective 11 ) .…”

Section: Discussionmentioning

confidence: 99%

Effects of dynamic neural mobilization on cerebral cortical activity in patients with stroke

Kang

Moon²,

Jeong

et al. 2018

J Phys Ther Sci

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[Purpose] The current study aimed to identify the effects of dynamic neural mobilization on cerebral cortical activity in patients with stroke, and to present efficient intervention methods for stroke management. [Subjects and Methods] A total of 20 hemiplegic patients diagnosed with stroke over the past 6 months were sampled, and randomly divided into groups I (n=10) and II (n=10). Groups I and II underwent neural mobilization and dynamic neural mobilization, respectively, on the paralyzed arm. Both interventions were administered for 30 min, once a day, for 4 days a week, over a course of 4 weeks. β-waves and μ-rhythms in the C3 and C4 areas of the cerebral cortex were measured using electroencephalography, both before and after the intervention. [Results] After the intervention, both groups showed significant changes in the β-waves and μ-rhythms in the C3 area alone. Further, significant inter-group differences in the β-waves and μ-rhythms were only present in the C3 area. [Conclusion] Dynamic neural mobilization is an efficient intervention because it increases β-waves and μ-rhythms in the cerebral cortex. Therefore, the effects of continuous intervention programs involving dynamic neural mobilization in patients with stroke should be investigated in the future.

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Section: Discussionmentioning

confidence: 99%

Effects of dynamic neural mobilization on cerebral cortical activity in patients with stroke

Kang

Moon²,

Jeong

et al. 2018

J Phys Ther Sci

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“…The functional interaction between two signals x and y in the frequency domain is estimated by the coherence values calculated for the frequencies of interest. The magnitudesquared coherence value between x and y at frequency f can be calculated by the following the standard formulation [49].…”

Section: Cmc-based Featurementioning

confidence: 99%

An EEG-EMG correlation-based brain-computer interface for hand orthosis supported neuro-rehabilitation

Chowdhury

Raza

Meena

et al. 2019

Journal of Neuroscience Methods

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Background: Corticomuscular coupling has been investigated for long, to find out the underlying mechanisms behind cortical drives to produce different motor tasks. Although important in rehabilitation perspective, the use of corticomuscular coupling for driving braincomputer interface (BCI)-based neurorehabilitation is much ignored. This is primarily due to the fact that the EEG-EMG coherence popularly used to compute corticomuscular coupling, fails to produce sufficient accuracy in single-trial based prediction of motor tasks in a BCI system. New Method: In this study, we have introduced a new corticomuscular feature extraction method based on the correlation between band-limited power time-courses (CBPT) associated with EEG and EMG. 16 healthy individuals and 8 hemiplegic patients participated in a BCI-based hand orthosis triggering task, to test the performance of the CBPT method. The healthy population was equally divided into two groups; one experimental group for CBPT-based BCI experiment and another control group for EEG-EMG coherence based BCI experiment. Results: The classification accuracy of the CBPT-based BCI system was found to be 92.81± 2.09% for the healthy experimental group and 84.53 ± 4.58% for the patients' group. Comparison with existing method: The CBPT method significantly (p−value < 0.05) outperformed the conventional EEG-EMG coherence method in terms of classification accuracy. Conclusions: The experimental results clearly indicate that the EEG-EMG CBPT is a better alternative as a corticomuscular feature to drive a BCI system. Additionally, it is also feasible to use the proposed method to design BCI-based robotic neurorehabilitation paradigms.

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“…Previously [10,11], coupling analysis between muscle activities and the motor cortex has been researched based on EMG and EEG data. The objective of these investigations is to better understand the brain-muscle connectivity in relation to motor function, particularly on repetitive movement.…”

Section: A Emg and Eeg Signal Characteristic On Motoricmentioning

confidence: 99%

“…Segmentation of EEG signal with synchronizing muscle activities during contraction and relaxation is important to better understand the quantification of brain-muscle connectivity in periodic motoric activities [10,11].…”

Section: Introductionmentioning

confidence: 99%

Automatic Segmentation Scheme for Effective Synchronization of EMG-EEG Quantification

2020

International Journal of Biology and Biomedical Engineering

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Effective segmentation of electromyography (EMG) burst that synchronizes with electroencephalography (EEG) for long-duration recording is important steps to better understand the quantification of brain-muscle connectivity in periodic motoric activities. The work proposes an alternative automatic EMG segmentation scheme consists of four main steps, i.e. denoising of EMG burst signal using discrete wavelet transform, enveloping signal using time-windows averaging of RMS amplitude, an adaptive threshold to detect start/end burst envelope with accommodation of muscle contraction characteristic and the final step is conversion enveloping signal to binary segmentation signal.The proposed scheme is evaluated to detect contraction period/duration of EMG for the subject under repetitive holding and releasing grasp using a physiotherapy device. During exercise, the bio-amplifier board is customized to acquire simultaneous EEG and EMG from the region of flexor digitorum superficialis (FDS) of muscle and cortical motor of the brain, with total 284 EMG burst that counting by manual segmentation. The automatic segmentation can detect the total EMG burst by 6.25% error of false burst detection.The usefulness of proposed scheme is also tested to association analysis according to the power of EMG burst and the power of mu-wave of EEG recorded on the motor cortex. The changing trend of the power of mu-wave associated with muscle relaxation, muscle contraction strength and the synchronization level on the motor cortex during exercise are analyzed with integrated information that is relevant with biofeedback concept. The results demonstrate that proposed scheme has potential to be an effective method for the evaluation of biofeedback rehabilitation exercise.

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Cross-association analysis of EEG and EMG signals according to movement intention state

Cited by 55 publications

References 29 publications

Effects of dynamic neural mobilization on cerebral cortical activity in patients with stroke

Effects of dynamic neural mobilization on cerebral cortical activity in patients with stroke

An EEG-EMG correlation-based brain-computer interface for hand orthosis supported neuro-rehabilitation

Automatic Segmentation Scheme for Effective Synchronization of EMG-EEG Quantification

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