Directed causality of the human electrocorticogram during dexterous movement.
While significant strides have been made in designing brain-machine interfaces for use in humans, efforts to decode truly dexterous movements in real time have been hindered by difficulty extracting detailed movement-related information from the most practical human neural interface, the electrocorticogram (ECoG). We explore a potentially rich, largely untapped source of movement-related information in the form of cortical connectivity computed with time-varying dynamic Bayesian networks (TV-DBN). We discover that measures of connectivity between ECoG electrodes derived from the local motor potential vary with dexterous movement in 65% of movement-related electrode pairs tested, and measures of connectivity derived from spectral features vary with dexterous movement in 76%. Due to the large number of features generated with connectivity methods, the TV-DBN a promising tool for dexterous decoding.
Johns Hopkins University, Baltimore, MD 21205, USA. benz@jhu.edu
Heather L
HL
Benz
Maxwell
M
Collard
Charalampos
C
Tsimpouris
Soumyadipta
S
Acharya
Nathan E
NE
Crone
Nitish V
NV
Thakor
Anastasios
A
Bezerianos
D001499:Bayes Theorem; D002648:Child; D004558:Electric Stimulation; D004566:Electrodes; D004569:Electroencephalography / Q000295:instrumentation*; D019054:Evoked Potentials, Motor / Q000502:physiology; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008297:Male; D008875:Middle Aged; D009068:Movement / Q000502:physiology*; D009415:Nerve Net / Q000503:physiopathology; D013997:Time Factors; D055815:Young Adult