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.

Effective recognition of human lower limb jump locomotion phases based on multi-sensor information fusion and machine learning.

Jump locomotion is the basic movement of human. However, no thorough research on the recognition of jump sub-phases has been carried so far. This paper aims to use multi-sensor information fusion and machine learning to recognize the human jump phase, which is crucial to the development of exoskeleton that assists jumping. The method of information fusion for sensors including sEMG, IMU, and footswitch sensor is studied. The footswitch signals are filtered by median filter. A processing method of synthesizing Euler angles into phase angle is proposed, which is beneficial to data integration. The jump locomotion is creatively segmented into five phases. The onset and offset of active segment are detected by sample entropy of sEMG and standard deviation of acceleration signal. The features are extracted from analysis windows using multi-sensor information fusion, and the dimension of feature matrix is selected. By comparing the performances of state-of-the-art machine learning classifiers, feature subsets of sEMG, IMU, and footswitch signals are selected from time domain features in a series of analysis window parameters. The average recognition accuracy of sEMG and IMU is 91.76% and 97.68%, respectively. When using the combination of sEMG, IMU, and footswitch signals, the average accuracy is 98.70%, which outperforms the combination of sEMG and IMU (97.97%, p < 0.01). Graphical Abstract The sub-phases of human locomotion are recognized based on multi-sensor information fusion and machine learning method. The feature data of the sub-phases is visualized in 3-dimensional space. The predicted states and the true states in a complete jump are compared along the time axis.

Using convolutional neural networks to decode EEG-based functional brain network with different severity of acrophobia.

Objective.The prevalence of acrophobia is high, especially with the rise of many high-rise buildings. In the recent few years, researchers have begun to analyze acrophobia from the neuroscience perspective, especially to improve the virtual reality exposure therapy (VRET). Electroencephalographic (EEG) is an informative neuroimaging technique, but it is rarely used for acrophobia. The purpose of this study is to evaluate the effectiveness of using EEGs to identify the degree of acrophobia objectively.Approach.EEG data were collected by virtual reality (VR) exposure experiments. We classified all subjects' degrees of acrophobia into three categories, where their questionnaire scores and behavior data showed significant differences. Using synchronization likelihood, we computed the functional connectivity between each pair of channels and then obtained complex networks named functional brain networks (FBNs). Basic topological features and community structure features were extracted from the FBNs. Statistical results demonstrated that FBN features can be used to distinguish different groups of subjects. We trained machine learning (ML) algorithms with FBN features as inputs and trained convolutional neural networks (CNNs) with FBNs directly as inputs.Main results.It turns out that using FBN to identify the severity of acrophobia is feasible. For ML algorithms, the community structure features of some cerebral cortex regions outperform typical topological features of the whole brain, in terms of classification accuracy. The performances of CNN algorithms are better than ML algorithms. The CNN with ResNet performs the best (accuracy reached 98.46 ± 0.42%).Significance.These observations indicate that community structures of certain cerebral cortex regions could be used to identify the degree of acrophobia. The proposed CNN framework can provide objective feedback, which could help build closed-loop VRET portable systems.

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.