An Approach for Brain-Controlled Prostheses Based on a Facial Expression Paradigm

et al. 2021

Front. Neurosci.

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Electroencephalogram (EEG) modeling in brain-computer interface (BCI) provides a theoretical foundation for its development. However, limited by the lack of guidelines in model parameter selection and the inability to obtain personal tissue information in practice, EEG modeling in BCI is mainly focused on the theoretical qualitative level which shows a gap between the theory and its application. Based on such problems, this work combined the surface EEG simulation with a converter based on the generative adversarial network (GAN), to establish the connection from simulated EEG to its application in BCI classification. For the scalp EEGs modeling, a mathematical model was built according to the physics of surface EEG, which consisted of the parallel 3-population neural mass model, the equivalent dipole, and the forward computation. For application, a converter based on the conditional GAN was designed, to transfer the simulated theoretical-only EEG to its practical version, in the lack of individual bio-information. To verify the feasibility, based on the latest microexpression-assisted BCI paradigm proposed by our group, the converted simulated EEGs were used in the training of BCI classifiers. The results indicated that, compared with training with insufficient real data, by adding the simulated EEGs, the overall performance showed a significant improvement (P = 0.04 < 0.05), and the test performance can be improved by 2.17% ± 4.23, in which the largest increase was up to 12.60% ± 1.81. Through this work, the link from theoretical EEG simulation to BCI classification has been initially established, providing an enhanced novel solution for the application of EEG modeling in BCI.

Section: The Head Models and The Dipole Positionsupporting

confidence: 63%

Realizing the Application of EEG Modeling in BCI Classification: Based on a Conditional GAN Converter

et al. 2021

Front. Neurosci.

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“…The research on the relationship between emotional processing and facial action has increased. Previous studies proved that brain activity from the prefrontal and motor cortexes provides a biological foundation to distinguish the movements of facial actions [ 11 , 14 , 24 , 29 , 32 , 33 ]. For expected facial muscle contraction, many factors contribute to the mechanism of a person’s facial action.…”

Section: Methodsmentioning

confidence: 99%

“…The mechanism of the EMG-based facial action and its control model was analyzed from the brain responses of the cortexes particular to facial muscle contractions. Our previous study demonstrated that the prefrontal, motor, and limbic cortexes have a fundamental function in the completion of different facial actions [24]. Hence, the bio-signals from different facial muscles contain abundant and sophisticated information during their actions.…”

Section: Mechanism Of Emg-based Facial Actionmentioning

confidence: 99%

Novel approach for electromyography-controlled prostheses based on facial action

et al. 2020

Med Biol Eng Comput

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Individuals with severe tetraplegia frequently require to control their complex assistive devices using body movement with the remaining activity above the neck. Electromyography (EMG) signals from the contractions of facial muscles enable people to produce multiple command signals by conveying information about attempted movements. In this study, a novel EMG-controlled system based on facial actions was developed. The mechanism of different facial actions was processed using an EMG control model. Four asymmetric and symmetry actions were defined to control a two-degree-of-freedom (2-DOF) prosthesis. Both indoor and outdoor experiments were conducted to validate the feasibility of EMG-controlled prostheses based on facial action. The experimental results indicated that the new paradigm presented in this paper yields high performance and efficient control for prosthesis applications.

“…For example, when only using EEG, it is difficult to achieve a satisfactory accuracy, and when only using EMG it is hard to guarantee the stability of recognition. Rui et al classified the facial action to control a prosthesis, and the results showed that the performance of EMG-based control is better than EEG-based control (Rui et al, 2018b;Xiaodong et al, 2020). Therefore, the fusion method of EEG and EMG signals emerge as the times require, it can improve decoding performance and stability (Tejedor et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Homology Characteristics of EEG and EMG for Lower Limb Voluntary Movement Intention

et al. 2021

Front. Neurorobot.

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In the field of lower limb exoskeletons, besides its electromechanical system design and control, attention has been paid to realizing the linkage of exoskeleton robots to humans via electroencephalography (EEG) and electromyography (EMG). However, even the state of the art performance of lower limb voluntary movement intention decoding still faces many obstacles. In the following work, focusing on the perspective of the inner mechanism, a homology characteristic of EEG and EMG for lower limb voluntary movement intention was conducted. A mathematical model of EEG and EMG was built based on its mechanism, which consists of a neural mass model (NMM), neuromuscular junction model, EMG generation model, decoding model, and musculoskeletal biomechanical model. The mechanism analysis and simulation results demonstrated that EEG and EMG signals were both excited by the same movement intention with a response time difference. To assess the efficiency of the proposed model, a synchronous acquisition system for EEG and EMG was constructed to analyze the homology and response time difference from EEG and EMG signals in the limb movement intention. An effective method of wavelet coherence was used to analyze the internal correlation between EEG and EMG signals in the same limb movement intention. To further prove the effectiveness of the hypothesis in this paper, six subjects were involved in the experiments. The experimental results demonstrated that there was a strong EEG-EMG coherence at 1 Hz around movement onset, and the phase of EEG was leading the EMG. Both the simulation and experimental results revealed that EEG and EMG are homologous, and the response time of the EEG signals are earlier than EMG signals during the limb movement intention. This work can provide a theoretical basis for the feasibility of EEG-based pre-perception and fusion perception of EEG and EMG in human movement detection.