Characterization of kinesthetic motor imagery compared with visual motor imageries

• Published in: Scientific reports Vol. 11; no. 1; p. 3751
• Main Authors: Yang, Yu Jin, Jeon, Eun Jeong, Kim, June Sic, Chung, Chun Kee
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 12.02.2021; Nature Publishing Group; Nature Portfolio
• Subjects: 631/378/116; 631/378/2629; Brain; Classification; EEG; Electroencephalography; Experiments; Feedback; Handedness; Humanities and Social Sciences; Illiteracy; Mental task performance; multidisciplinary; Neural networks; Proprioception; Science; Science (multidisciplinary); Sensorimotor integration; Visual system
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-021-82241-0

Motor imagery (MI) is the only way for disabled subjects to robustly use a robot arm with a brain-machine interface. There are two main types of MI. Kinesthetic motor imagery (KMI) is proprioceptive (OR somato-) sensory imagination and Visual motor imagery (VMI) represents a visualization of the corresponding movement incorporating the visual network. Because these imagery tactics may use different networks, we hypothesized that the connectivity measures could characterize the two imageries better than the local activity. Electroencephalography data were recorded. Subjects performed different conditions, including motor execution (ME), KMI, VMI, and visual observation (VO). We tried to classify the KMI and VMI by conventional power analysis and by the connectivity measures. The mean accuracies of the classification of the KMI and VMI were 98.5% and 99.29% by connectivity measures (alpha and beta, respectively), which were higher than those by the normalized power (p < 0.01, Wilcoxon paired rank test). Additionally, the connectivity patterns were correlated between the ME-KMI and between the VO-VMI. The degree centrality (DC) was significantly higher in the left-S1 at the alpha-band in the KMI than in the VMI. The MI could be well classified because the KMI recruits a similar network to the ME. These findings could contribute to MI training methods.