Synthesis of sEMG Signals for Hand Gestures Using a 1DDCGAN.

The emergence of modern prosthetics controlled by bio-signals has been facilitated by AI and microchip technology innovations. AI algorithms are trained using sEMG produced by muscles during contractions. The data acquisition procedure may result in discomfort and fatigue, particularly for amputees. Furthermore, prosthetic companies restrict sEMG signal exchange, limiting data-driven research and reproducibility. GANs present a viable solution to the aforementioned concerns. GANs can generate high-quality sEMG, which can be utilised for data augmentation, decrease the training time required by prosthetic users, enhance classification accuracy and ensure research reproducibility. This research proposes the utilisation of a one-dimensional deep convolutional GAN (1DDCGAN) to generate the sEMG of hand gestures. This approach involves the incorporation of dynamic time wrapping, fast Fourier transform and wavelets as discriminator inputs. Two datasets were utilised to validate the methodology, where five windows and increments were utilised to extract features to evaluate the synthesised sEMG quality. In addition to the traditional classification and augmentation metrics, two novel metrics-the Mantel test and the classifier two-sample test-were used for evaluation. The 1DDCGAN preserved the inter-feature correlations and generated high-quality signals, which resembled the original data. Additionally, the classification accuracy improved by an average of 1.21-5%.

Non-invasive dual attention TCN for electromyography and motion data fusion in lower limb ambulation prediction.

Objective.Recent technological advances show the feasibility of fusing surface electromyography (sEMG) signals and movement data to predict lower limb ambulation intentions. However, since the invasive fusion of different signals is a major impediment to improving predictive performance, searching for a non-invasive (NI) fusion mechanism for lower limb ambulation pattern recognition based on different modal features is crucial.Approach. We propose an end-to-end sequence prediction model with NI dual attention temporal convolutional networks (NIDA-TCNs) as a core to elegantly address the essential deficiencies of traditional decision models with heterogeneous signal fusion. Notably, the NIDA-TCN is a weighted fusion of sEMG and inertial measurement units with time-dependent effective hidden information in the temporal and channel dimensions using TCN and self-attentive mechanisms. The new model can better discriminate between walking, jumping, downstairs, and upstairs four lower limb activities of daily living.Main results. The results of this study show that the NIDA-TCN models produce predictions that significantly outperform both frame-wise and TCN models in terms of accuracy, sensitivity, precision, F1 score, and stability. Particularly, the NIDA-TCN with sequence decision fusion (NIDA-TCN-SDF) models, have maximum accuracy and stability increments of 3.37% and 4.95% relative to the frame-wise model, respectively, without manual feature-encoding and complex model parameters.Significance. It is concluded that the results demonstrate the validity and feasibility of the NIDA-TCN-SDF models to ensure the prediction of daily lower limb ambulation activities, paving the way to the development of fused heterogeneous signal decoding with better prediction performance.

Motor Intention Decoding from the Upper Limb by Graph Convolutional Network Based on Functional Connectivity.

Decoding brain intention from noninvasively measured neural signals has recently been a hot topic in brain-computer interface (BCI). The motor commands about the movements of fine parts can increase the degrees of freedom under control and be applied to external equipment without stimulus. In the decoding process, the classifier is one of the key factors, and the graph information of the EEG was ignored by most researchers. In this paper, a graph convolutional network (GCN) based on functional connectivity was proposed to decode the motor intention of four fine parts movements (shoulder, elbow, wrist, hand). First, event-related desynchronization was analyzed to reveal the differences between the four classes. Second, functional connectivity was constructed by using synchronization likelihood (SL), phase-locking value (PLV), H index (H), mutual information (MI), and weighted phase-lag index (WPLI) to acquire the electrode pairs with a difference. Subsequently, a GCN and convolutional neural networks (CNN) were performed based on functional topological structures and time points, respectively. The results demonstrated that the proposed method achieved a decoding accuracy of up to 92.81% in the four-class task. Besides, the combination of GCN and functional connectivity can promote the development of BCI.

Acrophobia Quantified by EEG Based on CNN Incorporating Granger Causality.

The aim of this study is to quantify acrophobia and provide safety advices for high-altitude workers. Considering that acrophobia is a fuzzy quantity that cannot be accurately evaluated by conventional detection methods, we propose a comprehensive solution to quantify acrophobia. Specifically, this study simulates a virtual reality environment called High-altitude Plank Walking Challenge, which provides a safe and controlled experimental environment for subjects. Besides, a method named Granger Causality Convolutional Neural Network (GCCNN) combining convolutional neural network and Granger causality functional brain network is proposed to analyze the subjects' noninvasive scalp EEG signals. Here, the GCCNN method is used to distinguish the subjects with severe acrophobia, moderate acrophobia, and no acrophobia in a three-class classification task or no acrophobia and acrophobia in a two-class classification task. Compared with the mainstream methods, the GCCNN method achieves better classification performance, with an accuracy of 98.74% for the two-class classification task (no acrophobia versus acrophobia) and of 98.47% for the three-class classification task (no acrophobia versus moderate acrophobia versus severe acrophobia). Consequently, our proposed GCCNN method can provide more accurate quantitative results than the comparative methods, making it to be more competitive in further practical applications.

Decoding of voluntary and involuntary upper-limb motor imagery based on graph fourier transform and cross-frequency coupling coefficients.

OBJECTIVE: Brain-computer interface (BCI) technology based on motor imagery (MI) control has become a research hotspot but continues to encounter numerous challenges. BCI can assist in the recovery of stroke patients and serve as a key technology in robot control. Current research on MI almost exclusively focuses on the hands, feet, and tongue. Therefore, the purpose of this paper is to establish a four-class MI BCI system, in which the four types are the four articulations within the right upper limbs, involving the shoulder, elbow, wrist, and hand. APPROACH: Ten subjects were chosen to perform nine upper-limb analytic movements, after which the differences were compared in P300, movement-related potentials(MRPS), and event-related desynchronization/event-related synchronization under voluntary MI (V-MI) and involuntary MI (INV-MI). Next, the cross-frequency coupling (CFC) coefficient based on mutual information was extracted from the electrodes and frequency bands with interest. Combined with the image Fourier transform and twin bounded support vector machine classifier, four kinds of electroencephalography data were classified, and the classifier's parameters were optimized using a genetic algorithm. MAIN RESULTS: The results were shown to be encouraging, with an average accuracy of 93.2% and 92.2% for V-MI and INV-MI, respectively, and over 95% for any three classes and any two classes. In most cases, the accuracy of feature extraction using the proximal articulations as the basis was found to be relatively high and had better performance. SIGNIFICANCE: This paper discussed four types of MI according to three aspects under two modes and classed them by combining graph Fourier transform and CFC. Accordingly, the theoretical discussion and classification methods may provide a fundamental theoretical basis for BCI interface applications.

Accurate recognition of lower limb ambulation mode based on surface electromyography and motion data using machine learning.

Background and Objective The lower limb activity of recognition of the elderly, the weak, the disabled and the sick is an irreplaceable role in the caring of daily life. The main purpose of this study is to assess the feasibility of using the surface electromyography (sEMG) signal and inertial measurement units (IMUs) data to determine the optimal fusion features and classifier for the study of daily ambulation mode recognition. Methods We have carried out several steps of experiments to obtain and test the optimal combination of the sEMG data and the body motion data at the feature level and the most suitable machine learning classification algorithm. Firstly, the sEMG and IMUs signals of eighteen participants performing four different ambulatory activities have recorded using wearable sensors. Secondly, several features of the sEMG sensors and IMU data were extracted and tested by the Markov Random Field based Fisher-Markov feature selector. Finally, four ML classifiers with several feature combinations were estimated with sensitivity, precision and recognition accurate rate of ambulatory activity classification. Results The results of this work showed that all selected features were significantly statistical difference in four ambulation modes. The principal component analysis was used to reduce the dimension of selected sEMG features and IMU features to form a fusion feature input support vector machine classifier, which could predict ambulatory activities with good classification performance. Conclusions It is concluded that the results demonstrate the feasibility of the ML classification model, which could provide a more novel way to guarantee the recognition rate and effectiveness of monitor daily ambulatory activity.