SSVEP-gated EMG-based decoding of elbow angle during goal-directed reaching movement

Davarinia, Fatemeh; Maleki, Ali

doi:10.1016/j.bspc.2021.103222

Cited by 10 publications

(13 citation statements)

References 50 publications

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“…Naturally, BCI is an essential part of bio-robotics research. The fuse of sEMG and EEG is emerging as research interests to provide neural prosthetic control for elbow-above amputees [ 114 , 115 ].…”

Section: Non-invasive Bci In Neural Prosthetics and Bio-robotics Controlmentioning

confidence: 99%

“…A BCI could be built based on either exogenous stimulus or endogenous events. Since both exogenous BCIs [ 115 ] and endogenous BCI [ 114 ] how demonstrations of prosthetic control in combination with sEMG, both exogenous BCIs and endogenous BCI are reviewed, respectively. Visual stimulus is the most-applied exogenous stimulus.…”

Section: Non-invasive Bci In Neural Prosthetics and Bio-robotics Controlmentioning

confidence: 99%

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Bio-robotics research for non-invasive myoelectric neural interfaces for upper-limb prosthetic control: a 10-year perspective review

Jiang¹,

Chen²,

He³

et al. 2023

National Science Review

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A decade ago, a group of researchers from academia and industry identified a dichotomy between the industrial and academic state-of-the-art in upper-limb prosthesis control, a widely used bio-robotics application. They proposed four key technical challenges, if addressed, could bridge this gap and translate academic research into clinically and commercially viable products. These challenges are unintuitive control schemes, lack of sensory feedback, poor robustness, and single sensor modality. Here, we provide a perspective review on the research effort that occurred in the last decade, aiming at addressing these challenges. In addition, we discussed three research areas essential to the recent development in upper-limb prosthetic control research but were not envisioned in the review ten years ago: deep learning methods, sEMG decomposition and open-source databases. To conclude the review, we provided an outlook into the near future of the research and development in upper-limb prosthetic control and beyond.

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Section: Non-invasive Bci In Neural Prosthetics and Bio-robotics Controlmentioning

confidence: 99%

Section: Non-invasive Bci In Neural Prosthetics and Bio-robotics Controlmentioning

confidence: 99%

Bio-robotics research for non-invasive myoelectric neural interfaces for upper-limb prosthetic control: a 10-year perspective review

Jiang¹,

Chen²,

He³

et al. 2023

National Science Review

View full text Add to dashboard Cite

show abstract

“…Secondly, muscle fatigue that impacts sEMG-based motion estimations will not interfere EEG-based motion estimation. Therefore, the combination of sEMG and EEG now becomes a research interest [68][69][70][71][72][73], where features of two signals can either be processed sequentially or simultaneously. In [69], an artificial arm for above-elbow amputees was controlled based on the parallel processing of sEMG and EEG signals, with forearm pronation/supination estimated using EEG and elbow flexion/extension decoded by sEMG.…”

Section: A Multi-modal Sensing Fusionmentioning

confidence: 99%

“…A similar study can be found in [73] to provide precise control to the prosthesis for transhumeral cases. Differently, in cascaded prediction, either sEMG signals were used as a switching mechanism to the EEG-based control or vice versa [68,72]. For instance, in [68] EEG was firstly used to recognise the intentional voluntary movements of subjects, after which sEMG signals were used to identify the tremor onset.…”

Section: A Multi-modal Sensing Fusionmentioning

confidence: 99%

“…For instance, in [68] EEG was firstly used to recognise the intentional voluntary movements of subjects, after which sEMG signals were used to identify the tremor onset. In [72], EEG signals acted as a gate to choose only the dedicated decoders of sEMG for estimation of the trajectory angle. In addition, the designs in [70,71] represent another popular fusion method, in which the data of two signals were combined to enhance the estimation accuracy.…”

Section: A Multi-modal Sensing Fusionmentioning

confidence: 99%

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Toward Robust, Adaptiveand Reliable Upper-Limb Motion Estimation Using Machine Learning and Deep Learning–A Survey in Myoelectric Control

Bao

Xie

Yang

et al. 2022

IEEE J. Biomed. Health Inform.

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To develop multi-functional human-machine interfaces that can help disabled people reconstruct lost functions of upper-limbs, machine learning (ML) and deep learning (DL) techniques have been widely implemented to decode human movement intentions from surface electromyography (sEMG) signals. However, due to the high complexity of upper-limb movements and the inherent non-stable characteristics of sEMG, the usability of ML/DL based control schemes is still greatly limited in practical scenarios. To this end, tremendous efforts have been made to improve model robustness, adaptation, and reliability. In this article, we provide a systematic review on recent achievements, mainly from three categories: multi-modal sensing fusion to gain additional information of the user, transfer learning (TL) methods to eliminate domain shift impacts on estimation models, and post-processing approaches to obtain more reliable outcomes. Special attention is given to fusion strategies, deep TL frameworks, and confidence estimation. Research challenges and emerging opportunities, with respect to hardware development, public resources, and decoding strategies, are also analysed to provide perspectives for future developments.

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Comparing the efficiency of recurrent neural networks to EMG-based continuous estimation of the elbow angle

Davarinia,

Maleki

2024

Neural Comput & Applic

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SSVEP-gated EMG-based decoding of elbow angle during goal-directed reaching movement

Cited by 10 publications

References 50 publications

Bio-robotics research for non-invasive myoelectric neural interfaces for upper-limb prosthetic control: a 10-year perspective review

Bio-robotics research for non-invasive myoelectric neural interfaces for upper-limb prosthetic control: a 10-year perspective review

Toward Robust, Adaptiveand Reliable Upper-Limb Motion Estimation Using Machine Learning and Deep Learning–A Survey in Myoelectric Control

Comparing the efficiency of recurrent neural networks to EMG-based continuous estimation of the elbow angle

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