EEG correlates of verbal and conscious processing of motor control in sport and human movement: a systematic review

Parr, Johnny; Gallicchio, Germano; Wood, Greg

doi:10.1080/1750984x.2021.1878548

Cited by 24 publications

(16 citation statements)

References 82 publications

(152 reference statements)

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“…Specifically, Parr et al administered one week of "gaze training" designed to encourage learners to adopt a "target focused" gaze strategy and avoid visually fixating the prosthesis, a method shown to expedite the acquisition of laparoscopic surgical skills 60 . Compared to a group who received explicit technique focused instructions (i.e., "movement training"), the gaze training group visually focused on the prosthesis less, completed the tasks quicker, and displayed more efficient brain activity (as indexed by electroencephalography; see 61 ) at retention and delayed retention.…”

Section: Hand-eye Coordination and The Utility Of Gaze Trainingmentioning

confidence: 96%

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A scoping review of the application of motor learning principles to optimize myoelectric prosthetic hand control

Parr

Wright

Uiga

et al. 2021

Prosthetics &Amp; Orthotics International

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Although prosthetic hand rejection rates remain high, evidence suggests that effective training plays a major role in device acceptance. Receiving training early in the rehabilitation process also enhances functional prosthetic use, decreases the likelihood of developing an over-reliance on the intact limb and reduces amputation-related pain. Despite these obvious benefits, there is a current lack of evidence regarding the most effective training techniques to facilitate myoelectric prosthetic hand control and it remains unknown whether training is effective in facilitating the acquisition and transfer of prosthetic skill. In this scoping review, we introduce and summarise key motor learning principles related to attentional focus, implicit motor learning, training eye-hand coordination, practice variability, motor imagery and action observation, and virtual training and biofeedback. We then review the existing literature that has applied these principles for training prosthetic hand control before outlining future avenues for further research. The importance of optimising early and appropriate training cannot be overlooked. While the intuition and experience of clinicians holds enormous value, evidence-based guidelines based on well-established motor learning principles will also be crucial for training effective prosthetic hand control. While it is clear that more research is needed to form the basis of such guidelines, it is hoped that this review highlights the potential avenues for this work.

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Section: Hand-eye Coordination and The Utility Of Gaze Trainingmentioning

confidence: 96%

“…60 Compared with a group that received explicit technique-focused instructions (i.e. movement training), the gaze training group visually focused on the prosthesis less, completed the tasks quicker, and displayed more efficient brain activity (as indexed by electroencephalography (EEG); see 61) at retention and delayed retention.…”

Section: Introductionmentioning

confidence: 99%

A scoping review of the application of motor learning principles to optimize myoelectric prosthetic hand control

Parr

Wright

Uiga

et al. 2021

Prosthetics &Amp; Orthotics International

Self Cite

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“…After the test, the convolution layer used a 5×1 filter, the sliding step was 1, and the number of filters was set to 32 and 64 in the front and back stages, respectively. The expansion rate (the d in Formula 2 and 3) in the TCN residual block is set as [1,2,4,8,16,32,64], and the zigzag structure is repeated in the two-stage TCN. The receptive field of the front stage for time series was 509, while the receptive field of the back stage could be increased to 1017 by using two-stage TCN.…”

Section: Tcn Time Series Signal Prediction Modelmentioning

confidence: 99%

“…Thompson et al [ 7 ] in order to eliminate the problem of motion artifacts in ECG, a new feature extraction algorithm is proposed to improve the stability of ECG model. Parr et al [ 8 ] through the retrieval of electronic database, determined the introduction standard of 38 indexes of ECG, and established the mapping relationship between ECG and body learning and movement. These studies show that both sEMG and ECG can reflect many indicators of the human body.…”

Section: Introductionmentioning

confidence: 99%

Study on exercise muscle fatigue based on sEMG and ECG data fusion and temporal convolutional network

et al. 2022

PLoS ONE

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Background Muscle fatigue is a crucial indicator to determine whether training is in place and to protect trainers. Purpose To make full use of morphological information of surface EMG and ECG signals in the time domain, a new idea and method for the fatigue assessment of exercise muscles based on data fusion is proposed in this paper. Methods sEMG and ECG time series with the same length were obtained by signal preprocessing and sequence normalization, feature extraction of sequence tenses was realized by a deep learning network based on sequential convolution and signal fusion model of muscle fatigue evaluation was established by D-S evidence theory. Experiment Thirty volunteers were recruited and divided into three groups. ECG signals and sEMG signals at the biceps brachii of the right upper limb were monitored in a 20-minute exercise cycle. Results The prediction result of TCN based on time domain signal is better than the commonly used KNN and SVM recognition algorithm, and the recognition accuracy of relaxed, excessive and fatigue by D-S fusion was 89%, 86%, 88.5%. The accuracy was 0.9055, 0.9494 and 0.9269, respectively. The recall rates of the three conditions were 0.9303, 0.9570 and 0.9435. The F-score of the three conditions was 0.8911, 0.8764 and 0.8837, respectively. Conclusion Based on time series and time series convolutional network, sEMG and ECG fusion of motor muscle recognition method can better distinguish different state information and has certain practical value in the fields of muscle evaluation, clinical diagnosis, wearable devices and so on.

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“…The commonly used EEG features include the power spectral density (PSD), differential asymmetry features, phase synchronization, phase lag index, directed transfer function and entropies [29][30][31][32]. Meanwhile, the time-frequency domain features include wavelet transform and autoregressive models, establishing the functional connectivity network between brain regions [33][34][35]. The findings of subsequent studies on the usefulness of EEG signal analysis methods in the assessment of mental stress have been conflicting, impeding the development of further research.…”

Section: Introductionmentioning

confidence: 99%

A Review on Mental Stress Assessment Methods Using EEG Signals

Katmah

Al-Shargie

Tariq

et al. 2021

Preprint

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Mental stress is one of the serious factors that lead to many health problems. Scientists and physicians have developed various tools to assess the level of mental stress in its early stages. Several neuroimaging tools have been proposed in the literature to assess mental stress in the workplace. Electroencephalogram (EEG) signal is one important candidate because it contain rich information about mental states and condition. In this paper, we review the existing EEG signal analysis methods on the assessment of mental stress. The review highlights the critical differences between the research findings and argues that variations of the data analysis methods contribute to several contradictory results. The variations in results could be due to various factors including lack of standardized protocol, the brain region of interest, stressor type, experiment duration, proper EEG processing, feature extraction mechanism, and type of classifier. Therefore, the significant part related to mental stress recognition is choosing the most appropriate features. In particular, a complex and diverse range of EEG features, including time-varying, functional, and dynamic brain connections, requires integration of various methods to understand their associations with mental stress. Over this, the review suggests fusing the cortical activations with the connectivity network measures and deep learning approaches to improve the accuracy of mental stress level assessment.

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EEG correlates of verbal and conscious processing of motor control in sport and human movement: a systematic review

Cited by 24 publications

References 82 publications

A scoping review of the application of motor learning principles to optimize myoelectric prosthetic hand control

A scoping review of the application of motor learning principles to optimize myoelectric prosthetic hand control

Study on exercise muscle fatigue based on sEMG and ECG data fusion and temporal convolutional network

A Review on Mental Stress Assessment Methods Using EEG Signals

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