2020/02/10 by Jae-Geun Yoon, Jae‐Geun Yoon, Yoon, Jae-Geun +2
Biochemistry, Genetics and Molecular Biology · Computer Science · Engineering · Medicine · Neuroscience · Psychology · #Advanced Memory and Neural Computing #Brain activity and meditation #Brain–computer interface #Computer science #Default mode network #EEG and Brain-Computer Interfaces #Electroencephalography #FOS: Biological sciences #FOS: Computer and information sciences #Functional Brain Connectivity Studies #Functional connectivity #Functional illiteracy #Functional magnetic resonance imaging #Internal medicine #Medicine #Motor imagery #Neural and Evolutionary Computing (cs.NE) #Neurons and Cognition (q-bio.NC) #Neuroscience #Perception #Psychology #Resting state fMRI #Significant difference #Supplementary motor area #Visual perception #Visual spatial attention #cs.NE #q-bio.NC
paper · pdf · doi:10.48550/arxiv.2002.08468
openalex publication_date 2020/02/10 · arxiv created 2020/02/11 · arxiv updated 2020/02/21 · openalex created_date 2022/07/26 · openalex updated_date 2026/07/28
Motor imagery based brain-computer interfaces (MI-BCIs) allow the control of\ndevices and communication by imagining different muscle movements. However,\nmost studies have reported a problem of "BCI-illiteracy" that does not have\nenough performance to use MI-BCI. Therefore, understanding subjects with poor\nperformance and finding the cause of performance variation is still an\nimportant challenge. In this study, we proposed predictors of MI performance\nusing effective connectivity in resting-state EEG. As a result, the high and\nlow MI performance groups had a significant difference as 23% MI performance\ndifference. We also found that connection from right lateral parietal to left\nlateral parietal in resting-state EEG was correlated significantly with MI\nperformance (r = -0.37). These findings could help to understand BCI-illiteracy\nand to consider alternatives that are appropriate for the subject.\n