Classifying action intention understanding EEG signals based on weighted brain network metric features

Frontal Cortex Acts as Causality Transition Hub from Mirror Network to Mentalizing Network During Action Intention Understanding

Introduction: While understanding other's action intention, mirror and mentalizing systems of human brain are successively activated in action perception and intention inference processes. Methods: To reveal the relationship between mirror and mentalizing systems during the two stages, this electroencephalogram study adopted the method of time-varying orthogonalized partial directed coherence (OPDC) to assess causal interaction between mirror and mentalizing networks during a "hand-cup interaction" action intention understanding task. Results: Task-related causal connectivity was found in gamma frequency band (30-45 Hz), primarily manifested as directed edges from sensorimotor to frontal areas in poststimulus 400-600 ms interval and directed links from frontal to parietal and temporal regions in 600-800 ms period. The analysis of event-related potential and source currents suggests that the change of inter-regional causality is related with functional transition of the brain from mirror matching to intention inference. The OPDC network modeling further finds that frontal area contains more inflow nodes in mirror network, whereas more outflow nodes in mentalizing network, with high betweenness centrality in temporally changing functional communities. Compared with intention-oriented actions, identification of unintelligible action intention particularly induces stronger OPDC from right superior frontal to inferior frontal gyrus and from sensorimotor to right frontotemporal regions during mentalizing inference process. Conclusion: These findings collectively suggest that, in the time ordering of information transfer within the directed networks, frontal area plays an important role of bridging hub between mirror and mentalizing systems, from maintaining and supervising perceptual information for mirror matching to controlling the mentalizing process for decoding other's action intention.

Global and multi-partition local network analysis of scalp EEG in West syndrome before and after treatment

West syndrome is an epileptic disease that seriously affects the normal growth and development of infants in early childhood. Based on the methods of brain topological network and graph theory, this article focuses on three clinical states of patients before and after treatment. In addition to discussing bidirectional and unidirectional global networks from the perspective of computational principles, a more in-depth analysis of local intra-network and inter-network characteristics of multi-partitioned networks is also performed. The spatial feature distribution based on feature path length is introduced for the first time. The results show that the bidirectional network has better significant differentiation. The rhythmic feature change trend and spatial characteristic distribution of this network can be used as a measure of the impact on global information processing in the brain after treatment. And localized brain regions variability in features and differences in the ability to interact with information between brain regions have potential as biomarkers for medication assessment in WEST syndrome. The above shows specific conclusions on the interaction relationship and consistency of macro-network and micro-network, which may have a positive effect on patients' treatment and prognosis management.

Functional connectivity of EEG motor rhythms after spinal cord injury

Spinal cord injury (SCI), which is the injury of the spinal cord site resulting in motor dysfunction, has prompted the use of motor imagery (MI)-based brain computer interface (BCI) systems for motor function reconstruction. However, analyzing electroencephalogram signals and brain function mechanisms for SCI patients is challenging. This is due to their low signal-to-noise ratio and high variability. We propose using the phase locking value (PLV) to construct the brain network in α and β rhythms for both SCI patients and healthy individuals. This approach aims to analyze the changes in brain network connectivity and brain function mechanisms following SCI. The results show that the connection strength of the α rhythm in the healthy control (HC) group is stronger than that in the SCI group, and the connection strength in the β rhythm of the SCI group is stronger than that in the HC group. Moreover, we extract the PLV with common spatial pattern (PLV-CSP) feature from the MI data of the SCI group. The experimental results for 12 SCI patients include that the peak classification accuracy is 100%, and the average accuracy of the ten-fold cross-verification is 95.6%. Our proposed approach can be used as a potential valuable method for SCI pathological studies and MI-based BCI rehabilitation systems.

Action Intention Understanding EEG Signal Classification Based on Improved Discriminative Spatial Patterns

Objective

Action intention understanding EEG signal classification is indispensable for investigating human-computer interactions and intention understanding mechanisms. Numerous investigations on classification tasks extract classification features by using graph theory metrics; however, the classification results are usually not good.

Method

To effectively implement the task of action intention understanding EEG signal classification, we proposed a new feature extraction method by improving discriminative spatial patterns.

Results

The whole frequency band and fusion band achieved satisfactory classification accuracies. Compared with other authors' methods for action intention understanding EEG signal classification, the new method performs more satisfactorily in some aspects.

Conclusions

The new feature extraction method not only effectively avoids complex values when solving the generalized eigenvalue problem but also perfectly realizes appreciable classification accuracies. Fusing the classification features of different frequency bands is a useful strategy for the classification task.

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.