Mapping and decoding cortical engagement during motor imagery, mental arithmetic, and silent word generation using MEG. Hum Brain Mapp 2023 Jun 01;44(8):3324-3342
Date
03/30/2023Pubmed ID
36987698Pubmed Central ID
PMC10171552DOI
10.1002/hbm.26284Scopus ID
2-s2.0-85151932139 (requires institutional sign-in at Scopus site) 4 CitationsAbstract
Accurate quantification of cortical engagement during mental imagery tasks remains a challenging brain-imaging problem with immediate relevance to developing brain-computer interfaces. We analyzed magnetoencephalography (MEG) data from 18 individuals completing cued motor imagery, mental arithmetic, and silent word generation tasks. Participants imagined movements of both hands (HANDS) and both feet (FEET), subtracted two numbers (SUB), and silently generated words (WORD). The task-related cortical engagement was inferred from beta band (17-25 Hz) power decrements estimated using a frequency-resolved beamforming method. In the hands and feet motor imagery tasks, beta power consistently decreased in premotor and motor areas. In the word and subtraction tasks, beta-power decrements showed engagements in language and arithmetic processing within the temporal, parietal, and inferior frontal regions. A support vector machine classification of beta power decrements yielded high accuracy rates of 74 and 68% for classifying motor-imagery (HANDS vs. FEET) and cognitive (WORD vs. SUB) tasks, respectively. From the motor-versus-nonmotor contrasts, excellent accuracy rates of 85 and 80% were observed for hands-versus-word and hands-versus-sub, respectively. A multivariate Gaussian-process classifier provided an accuracy rate of 60% for the four-way (HANDS-FEET-WORD-SUB) classification problem. Individual task performance was revealed by within-subject correlations of beta-decrements. Beta-power decrements are helpful metrics for mapping and decoding cortical engagement during mental processes in the absence of sensory stimuli or overt behavioral outputs. Markers derived based on beta decrements may be suitable for rehabilitation purposes, to characterize motor or cognitive impairments, or to treat patients recovering from a cerebral stroke.
Author List
Youssofzadeh V, Roy S, Chowdhury A, Izadysadr A, Parkkonen L, Raghavan M, Prasad GAuthors
Manoj Raghavan MD, PhD Professor in the Neurology department at Medical College of WisconsinVahab Youssofzadeh PhD Assistant Professor in the Neurology department at Medical College of Wisconsin
MESH terms used to index this publication - Major topics in bold
Brain-Computer InterfacesElectroencephalography
Humans
Imagination
Magnetoencephalography
Motor Cortex