Abstract
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.
Highlights
Brain–computer interface (BCI) is a control system, which enables users to directly communicate with the external environment through electroencephalogram (EEG) (Wolpaw et al, 2000)
We investigated five natural grasp types and no-movement condition using movement-related cortical potential (MRCP), and five-class classification accuracy was significantly better than significance level (Xu et al, 2021)
The 0 s in the figure corresponds to the subject-specific movement onset
Summary
Brain–computer interface (BCI) is a control system, which enables users to directly communicate with the external environment through electroencephalogram (EEG) (Wolpaw et al, 2000). To extend the BCI instruction set and realize a natural and intuitive control, many researchers have investigated the possibility of decoding complicated grasping information using movement-related cortical potentials (MR) (Jochumsen et al, 2015; Ofner et al, 2016; Pereira et al, 2017; Shiman et al, 2017). Ofner et al (2019) investigated MRCPs of five attempted arm and hand movements, and the peak accuracy of five-class classification was 45%. They found discriminative signals originated from central motor areas based on pattern analysis. We investigated five natural grasp types and no-movement condition using MRCP, and five-class classification accuracy was significantly better than significance level (Xu et al, 2021)
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