EEG-Based Functional Connectivity Representation using Phase Locking Value for Brain Network Based Applications

Studying the decoding process of complex grasping movement is of great significance to the field of motor rehabilitation. This study aims to decode five natural reach-and-grasp types using sources of movement-related cortical potential (MRCP) and investigate their difference in cortical signal characteristics and network structures. Electroencephalogram signals were gathered from 40 channels of eight healthy subjects. In an audio cue-based experiment, subjects were instructed to keep no-movement condition or perform five natural reach-and-grasp movements: palmar, pinch, push, twist and plug. We projected MRCP into source space and used average source amplitudes in 24 regions of interest as classification features. Besides, functional connectivity was calculated using phase locking value. Six-class classification results showed that a similar grand average peak performance of 49.35% can be achieved using source features, with only two-thirds of the number of channel features. Besides, source imaging maps and brain networks presented different patterns between each condition. Grasping pattern analysis indicated that the modules in the execution stage focus more on internal communication than in the planning stage. The former stage was related to the parietal lobe, whereas the latter was associated with the frontal lobe. This study demonstrates the superiority and effectiveness of source imaging technology and reveals the spread mechanism and network structure of five natural reach-and-grasp movements. We believe that our work will contribute to the understanding of the generation mechanism of grasping movement and promote a natural and intuitive control of brain–computer interface.

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Learning curve of a short time neurofeedback training: Reflection of brain network dynamics based on phase locking value

IEEE Transactions on Cognitive and Developmental Systems ◽

10.1109/tcds.2021.3125948 ◽

2021 ◽

pp. 1-1

Author(s):

Ze Wang ◽

Chi Man Wong ◽

Wenya Nan ◽

Qi Tang ◽

Agostinho C. Rosa ◽

...

Keyword(s):

Learning Curve ◽

Phase Locking ◽

Network Dynamics ◽

Brain Network ◽

Neurofeedback Training ◽

Phase Locking Value ◽

Short Time

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Preservation of electrophysiological functional connectivity after partial corpus callosotomy: case report

Journal of Neurosurgery Pediatrics ◽

10.3171/2018.2.peds17549 ◽

2018 ◽

Vol 22 (2) ◽

pp. 214-219 ◽

Cited By ~ 3

Author(s):

Kaitlyn Casimo ◽

Fabio Grassia ◽

Sandra L. Poliachik ◽

Edward Novotny ◽

Andrew Poliakov ◽

...

Keyword(s):

Functional Connectivity ◽

Corpus Callosum ◽

Seizure Activity ◽

Diffusion Tensor ◽

Phase Locking ◽

Corpus Callosotomy ◽

Left And Right ◽

Phase Locking Value ◽

Anatomical Mri ◽

Electrophysiological Methods

Prior studies of functional connectivity following callosotomy have disagreed in the observed effects on interhemispheric functional connectivity. These connectivity studies, in multiple electrophysiological methods and functional MRI, have found conflicting reductions in connectivity or patterns resembling typical individuals. The authors examined a case of partial anterior corpus callosum connection, where pairs of bilateral electrocorticographic electrodes had been placed over homologous regions in the left and right hemispheres. They sorted electrode pairs by whether their direct corpus callosum connection had been disconnected or preserved using diffusion tensor imaging and native anatomical MRI, and they estimated functional connectivity between pairs of electrodes over homologous regions using phase-locking value. They found no significant differences in any frequency band between pairs of electrodes that had their corpus callosum connection disconnected and those that had an intact connection. The authors’ results may imply that the corpus callosum is not an obligatory mediator of connectivity between homologous sites in opposite hemispheres. This interhemispheric synchronization may also be linked to disruption of seizure activity.

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

International Journal of Neural Systems ◽

10.1142/s0129065721500477 ◽

2021 ◽

Author(s):

Naishi Feng ◽

Fo Hu ◽

Hong Wang ◽

Bin Zhou

Keyword(s):

Functional Connectivity ◽

Degrees Of Freedom ◽

Phase Locking ◽

Phase Lag ◽

Neural Signals ◽

Convolutional Network ◽

Synchronization Likelihood ◽

Motor Intention ◽

External Equipment ◽

Phase Locking Value

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.

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