Training state and performance evaluation of working memory based on task-related EEG

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.

Scaling Analysis of Phase Fluctuations of Brain Networks in Dynamic Constrained Object Manipulation

In this study, we investigated the dynamic properties of oscillatory activities in the scalp electro-encephalographs (EEGs) of 20 participants involved in a novel dynamic manipulating task using a physical interface and a virtual feedback. The complexity of such a task a rises from the unexpected relationship between the magnitude of the motion and the feedback. The characterization of complex patterns arising from EEG is an important problem in identifying different mental intentions. We proposed a scaling analysis of phase fluctuation in the scalp EEG to discriminate the network states related to different EEG patterns, which correspond to manipulating the task with right or left movement intention. These intentions are generated while the participant is engaged in such a complex task. The phase characterization method was used to calculate the instantaneous phase from the operational EEG. Then, functional brain networks (FBNs) of 20 subjects based on the task-related EEG were constructed by phase synchronization. The degree features representing the structures and scaling components of brain networks are sensitive to the EEG patterns with left or right motor intention. The correlation between features and mental intentions was investigated by discriminant analysis. For 20 subjects, the average accuracy of state detection is [Formula: see text], and the average mean-squared error (MSE) is [Formula: see text]. The brain state depicted by the results is related to high awareness, the phase characterization is of the effectiveness in EEG processing and FBN construction and the difference of control intentions can be explored by the phase characterization method. This finding may be relevant to understanding some neuronal mechanisms underlying the attention and some applications of closed-loop control for the safety operation of tools.