Transfer Spectral Entropy and Application to Functional Corticomuscular Coupling

Chen, Xiaoling; Zhang, Yuanyuan; Cheng, Shengcui; Xie, Ping

doi:10.1109/tnsre.2019.2907148

Cited by 37 publications

(32 citation statements)

References 50 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the experimental paradigm of the present study, the stability of the grip was better during the pre-fatigue task, and the transmission of the descending control information was dominant during the continuous steady-state force output in the beta band. The beta-band results are consistent with those of previous studies on steady-state force output tasks (Mima et al, 2001;Chen et al, 2019). The synchronized oscillation of the gamma rhythm is considered to be related to the generation of dynamic forces and the integration of information such as attention, vision, and proprioception (Omlor et al, 2007;Mehrkanoon et al, 2014), which may also explain our observation that the ascending information flow in the gamma band was dominant.…”

Section: Discussionsupporting

confidence: 92%

“…As muscle fatigue progressed, the information transmission of the descending motor pathway for the subjects was inhibited and the production of voluntary force was reduced; however, relatively more sensory feedback information needed to be integrated to dynamically adapt to the decrease in grip stability. Chen et al (2019) previously reported that the beta rhythm oscillations can regulate the information transmission between the sensorimotor cortex and effector muscles. Therefore, it is reasonable to suggest that in order to complete the target force task, the beta-band information flow between the cortex and effector muscle may be dynamically adjusted as the task environment changes (e.g., caused by muscle fatigue in the present study).…”

Section: Discussionmentioning

confidence: 99%

“…Additionally, autonomous movement is a result of the cerebral cortex driving muscle actions and the coupling between the motor cortex and effector muscles is directional ( Mima et al, 2001 ; Witham et al, 2011 ). The information flow in the ascending (sEMG to EEG) and descending (EEG to sEMG) directions in FCMC has been shown to play a key role in neural information transmission for motor control and sensory feedback ( Baker, 2007 ; Yang et al, 2018 ; Chen et al, 2019 ). As a normal neurophysiological phenomenon induced by voluntary movement, muscle fatigue inevitably leads to changes in the information interaction within the motor nervous system.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Directed Information Flow Analysis Reveals Muscle Fatigue-Related Changes in Muscle Networks and Corticomuscular Coupling

et al. 2021

View full text Add to dashboard Cite

As a common neurophysiological phenomenon, voluntary muscle fatigue is accompanied by changes in both the central nervous system and peripheral muscles. Considering the effectiveness of the muscle network and the functional corticomuscular coupling (FCMC) in analyzing motor function, muscle fatigue can be analyzed by quantitating the intermuscular coupling and corticomuscular coupling. However, existing coherence-based research on muscle fatigue are limited by the inability of the coherence algorithm to identify the coupling direction, which cannot further reveal the underlying neural mechanism of muscle fatigue. To address this problem, we applied the time-delayed maximal information coefficient (TDMIC) method to quantitate the directional informational interaction in the muscle network and FCMC during a right-hand stabilized grip task. Eight healthy subjects were recruited to the present study. For the muscle networks, the beta-band information flow increased significantly due to muscle fatigue, and the information flow between the synergist muscles were stronger than that between the synergist and antagonist muscles. The information flow in the muscle network mainly flows to flexor digitorum superficialis (FDS), flexor carpi ulnar (FCU), and brachioradialis (BR). For the FCMC, muscle fatigue caused a significant decrease in the beta- and gamma-band bidirectional information flow. Further analysis revealed that the beta-band information flow was significantly stronger in the descending direction [electroencephalogram (EEG) to surface electromyography (sEMG)] than that in the ascending direction (sEMG to EEG) during pre-fatigue tasks. After muscle fatigue, the beta-band information flow in the ascending direction was significantly stronger than that in the descending direction. The present study demonstrates the influence of muscle fatigue on information flow in muscle networks and FCMC. We proposes that beta-band intermuscular and corticomuscular informational interaction plays an adjusting role in autonomous movement completion under muscle fatigue. Directed information flow analysis can be used as an effective method to explore the neural mechanism of muscle fatigue on the macroscopic scale.

show abstract

Section: Discussionsupporting

confidence: 92%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Directed Information Flow Analysis Reveals Muscle Fatigue-Related Changes in Muscle Networks and Corticomuscular Coupling

et al. 2021

View full text Add to dashboard Cite

show abstract

“…NMMs allow generating mildly nonlinear time series with properties that resemble the oscillatory dynamics of electrophysiological signals, such as EEG, and how they change as a result of coupling between different cortical areas. Therefore, NMMs are useful to study the behavior of brain connectivity measures that aim to capture such interactions [8,24,40,41]. Figure 1A shows a schematic representation of an NMM with two interacting cortical areas from which two signals, x and y (see Figure 1B), can be obtained.…”

Section: Neural Mass Modelsmentioning

confidence: 99%

“…Nonetheless, binning estimators for single trial-based estimation of information-theoretic measures exhibit systematic bias [8]. Furthermore, spectrally resolved TE estimation methods that can obtain single-trial TE estimates have been recently proposed in the literature [24,25]. Yet, phase TE is conceptually different from them [25], as they are not phase-specific metrics.…”

Section: Introductionmentioning

confidence: 99%

Kernel-Based Phase Transfer Entropy with Enhanced Feature Relevance Analysis for Brain Computer Interfaces

et al. 2021

View full text Add to dashboard Cite

Neural oscillations are present in the brain at different spatial and temporal scales, and they are linked to several cognitive functions. Furthermore, the information carried by their phases is fundamental for the coordination of anatomically distributed processing in the brain. The concept of phase transfer entropy refers to an information theory-based measure of directed connectivity among neural oscillations that allows studying such distributed processes. Phase TE is commonly obtained from probability estimations carried out over data from multiple trials, which bars its use as a characterization strategy in brain–computer interfaces. In this work, we propose a novel methodology to estimate TE between single pairs of instantaneous phase time series. Our approach combines a kernel-based TE estimator defined in terms of Renyi’s α entropy, which sidesteps the need for probability distribution computation with phase time series obtained by complex filtering the neural signals. Besides, a kernel-alignment-based relevance analysis is added to highlight relevant features from effective connectivity-based representation supporting further classification stages in EEG-based brain–computer interface systems. Our proposal is tested on simulated coupled data and two publicly available databases containing EEG signals recorded under motor imagery and visual working memory paradigms. Attained results demonstrate how the introduced effective connectivity succeeds in detecting the interactions present in the data for the former, with statistically significant results around the frequencies of interest. It also reflects differences in coupling strength, is robust to realistic noise and signal mixing levels, and captures bidirectional interactions of localized frequency content. Obtained results for the motor imagery and working memory databases show that our approach, combined with the relevance analysis strategy, codes discriminant spatial and frequency-dependent patterns for the different conditions in each experimental paradigm, with classification performances that do well in comparison with those of alternative methods of similar nature.

show abstract

Cross-frequency and iso-frequency estimation of functional corticomuscular coupling after stroke

et al. 2020

View full text Add to dashboard Cite

Transfer Spectral Entropy and Application to Functional Corticomuscular Coupling

Cited by 37 publications

References 50 publications

Directed Information Flow Analysis Reveals Muscle Fatigue-Related Changes in Muscle Networks and Corticomuscular Coupling

Directed Information Flow Analysis Reveals Muscle Fatigue-Related Changes in Muscle Networks and Corticomuscular Coupling

Kernel-Based Phase Transfer Entropy with Enhanced Feature Relevance Analysis for Brain Computer Interfaces

Cross-frequency and iso-frequency estimation of functional corticomuscular coupling after stroke

Contact Info

Product

Resources

About