Dynamic Fusion of Electromyographic and Electroencephalographic Data towards Use in Robotic Prosthesis Control

Pritchard, Michael; Weinberg, Abraham Itzhak; Williams, J.A.R.; Campelo, Felipe; Goldingay, Harry; Faria, Diego R.

doi:10.1088/1742-6596/1828/1/012056

Cited by 3 publications

(3 citation statements)

References 4 publications

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“…Workers from Aston University have recently reported encouraging results by combining EMG with EEG data sets as shown in Figure 4 (Pritchard et al, 2021). EMG signals were obtained from a Thalmic Labs' Myo armband, a low-cost EMG device with eight sensing electrodes, located on the subject's forearm (Figure 5).…”

Section: Brain-computer Interface Developmentsmentioning

confidence: 99%

Advances in robotic prostheses

Bogue

2021

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Purpose This paper aims to provide details of recent advances in robotic prostheses with the emphasis on the control and sensing technologies. Design/methodology/approach Following a short introduction, this paper first discusses the main robotic prosthesis control strategies. It then provides details of recent research and developments using non-invasive and invasive brain–computer interfaces (BCIs). These are followed by examples of studies that seek to confer robotic prostheses with sensory feedback. Finally, brief conclusions are drawn. Findings A significant body of research is underway involving electromyographic and BCI technologies, often in combination with advanced data processing and analysis schemes. This has the potential to yield robotic prostheses with advanced capabilities such as greater dexterity and sensory feedback. Originality/value This illustrates how electromyographic, BCI, signal processing and sensor technologies are being used to create robotic prostheses with enhanced functionality.

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Section: Brain-computer Interface Developmentsmentioning

confidence: 99%

Advances in robotic prostheses

Bogue

2021

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“…RAIN computer interface (BCI) is a technology that establishes a communication system between the human brain and external devices [1]. It has been applied in many fields, such as stroke rehabilitation [2,3], prosthetic control [4], quadcopter control [5], speech synthesis [6], and emotion recognition [7]. The techniques aiming to reconstruct hand motor function have been extensively studied [8][9][10] and are expected to enable rehabilitation training for stroke patients.…”

Section: Introductionmentioning

confidence: 99%

Decoding Multi-Class EEG Signals of Hand Movement Using Multivariate Empirical Mode Decomposition and Convolutional Neural Network

Tao

Wang

et al. 2022

IEEE Trans. Neural Syst. Rehabil. Eng.

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Brain-computer interface (BCI) is a technology that connects the human brain and external devices. Many studies have shown the possibility of using it to restore motor control in stroke patients. One specific challenge of such BCI is that the classification accuracy is not high enough for multi-class movements. In this study, by using Multivariate Empirical Mode Decomposition (MEMD) and Convolutional Neural Network (CNN), a novel algorithm (MECN) was proposed to decode EEG signals for four kinds of hand movements. Firstly, the MEMD was used to decompose the movement-related electroencephalogram (EEG) signals to obtain the multivariate intrinsic empirical functions (MIMFs). Then, the optimal MIMFs fusion was performed based on sequential forward selection algorithm. Finally, the selected MIMFs were input to the CNN model for discriminating four kinds of hand movements. The average classification accuracy of thirteen subjects over the six-fold cross-validation reached 81.14% for 2s-data before the movement onset and 81.08% for 2s-data after

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“…Tortora et al proposed and evaluated a hybrid HMI to decode walking phases of both legs from the Bayesian fusion of EEG and EMG signals; the results showed that the fusion of EEG and EMG information helps keep a stable recognition rate of each gait phase of more than 80% independently on the permanent level of EMG degradation (Tortora et al, 2020). Using low-cost sEMG and EEG devices in tandem can achieve high accuracy with decision-level fusion, which reported accuracies of up to 99% (Pritchard et al, 2021). So, the fusion of EEG and sEMG can keep a stable recognition rate of tasks with high accuracy.…”

Section: Introductionmentioning

confidence: 99%

Electroencephalogram and surface electromyogram fusion-based precise detection of lower limb voluntary movement using convolution neural network-long short-term memory model

Zhang

Dong

et al. 2022

Front. Neurosci.

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The electroencephalogram (EEG) and surface electromyogram (sEMG) fusion has been widely used in the detection of human movement intention for human–robot interaction, but the internal relationship of EEG and sEMG signals is not clear, so their fusion still has some shortcomings. A precise fusion method of EEG and sEMG using the CNN-LSTM model was investigated to detect lower limb voluntary movement in this study. At first, the EEG and sEMG signal processing of each stage was analyzed so that the response time difference between EEG and sEMG can be estimated to detect lower limb voluntary movement, and it can be calculated by the symbolic transfer entropy. Second, the data fusion and feature of EEG and sEMG were both used for obtaining a data matrix of the model, and a hybrid CNN-LSTM model was established for the EEG and sEMG-based decoding model of lower limb voluntary movement so that the estimated value of time difference was about 24 ∼ 26 ms, and the calculated value was between 25 and 45 ms. Finally, the offline experimental results showed that the accuracy of data fusion was significantly higher than feature fusion-based accuracy in 5-fold cross-validation, and the average accuracy of EEG and sEMG data fusion was more than 95%; the improved average accuracy for eliminating the response time difference between EEG and sEMG was about 0.7 ± 0.26% in data fusion. In the meantime, the online average accuracy of data fusion-based CNN-LSTM was more than 87% in all subjects. These results demonstrated that the time difference had an influence on the EEG and sEMG fusion to detect lower limb voluntary movement, and the proposed CNN-LSTM model can achieve high performance. This work provides a stable and reliable basis for human–robot interaction of the lower limb exoskeleton.

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Dynamic Fusion of Electromyographic and Electroencephalographic Data towards Use in Robotic Prosthesis Control

Cited by 3 publications

References 4 publications

Advances in robotic prostheses

Advances in robotic prostheses

Decoding Multi-Class EEG Signals of Hand Movement Using Multivariate Empirical Mode Decomposition and Convolutional Neural Network

Electroencephalogram and surface electromyogram fusion-based precise detection of lower limb voluntary movement using convolution neural network-long short-term memory model

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