title: Connectivity analysis as a novel approach to motor decoding for prosthesis control.
abstract: The use of neural signals for prosthesis control is an emerging frontier of research to restore lost function to amputees and the paralyzed. Electrocorticography (ECoG) brain-machine interfaces (BMI) are an alternative to EEG and neural spiking and local field potential BMI approaches. Conventional ECoG BMIs rely on spectral analysis at specific electrode sites to extract signals for controlling prostheses. We compare traditional features with information about the connectivity of an ECoG electrode network. We use time-varying dynamic Bayesian networks (TV-DBN) to determine connectivity between ECoG channels in humans during a motor task. We show that, on average, TV-DBN connectivity decreases from baseline preceding movement and then becomes negative, indicating an alteration in the phase relationship between electrode pairs. In some subjects, this change occurs preceding and during movement, before changes in low or high frequency power. We tested TV-DBN output in a hand kinematic decoder and obtained an average correlation coefficient   (r(2)) between actual and predicted joint angle of 0.40, and as high as 0.66 in one subject. This result compares favorably with spectral feature decoders, for which the average correlation coefficient was 0.13. This work introduces a new feature set based on connectivity and demonstrates its potential to improve ECoG BMI accuracy.
authors.affiliation: Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21205, USA. benz@jhu.edu
authors.forename: Heather L
authors.initials: HL
authors.lastname: Benz
authors.forename: Huaijian
authors.initials: H
authors.lastname: Zhang
authors.forename: Anastasios
authors.initials: A
authors.lastname: Bezerianos
authors.forename: Soumyadipta
authors.initials: S
authors.lastname: Acharya
authors.forename: Nathan E
authors.initials: NE
authors.lastname: Crone
authors.forename: Xioaxiang
authors.initials: X
authors.lastname: Zheng
authors.forename: Nitish V
authors.initials: NV
authors.lastname: Thakor
mesh_terms: D000293:Adolescent; D000328:Adult; D000465:Algorithms; D001499:Bayes Theorem; D001921:Brain / Q000502:physiology; D002540:Cerebral Cortex / Q000502:physiology; D004567:Electrodes, Implanted; D004569:Electroencephalography / Q000379:methods*; D004827:Epilepsy / Q000601:surgery; D019054:Evoked Potentials, Motor / Q000502:physiology; D005260:Female; D006225:Hand / Q000502:physiology; D018737:Hand Strength / Q000502:physiology; D006801:Humans; D008297:Male; D008875:Middle Aged; D009044:Motor Cortex / Q000502:physiology; D009068:Movement / Q000502:physiology; D058117:Neural Prostheses; D011474:Prosthesis Design; D014584:User-Computer Interface; D055815:Young Adult
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