NDLI: Electrocorticogram encoding of upper extremity movement duration

Content Provider	IEEE Xplore Digital Library
Author	Wang, P.T. King, C.E. McCrimmon, C.M. Shaw, S.J. Millett, D.E. Liu, C.Y. Chui, L.A. Nenadic, Z. Do, A.H.
Copyright Year	2014
Description	Author affiliation: Dept. of Biomed. Eng., Univ. of California, Irvine, Irvine, CA, USA (Wang, P.T.; King, C.E.; McCrimmon, C.M.; Nenadic, Z.) \|\| Dept. of Neurology, Rancho Los Amigos Nat. Rehabilitation Center (RLANRC), Downey, CA, USA (Shaw, S.J.; Millett, D.E.) \|\| Dept. of Neurosurg., RLANRC, Downey, CA, USA (Liu, C.Y.) \|\| Dept. of Neurology, UCI, Irvine, CA, USA (Chui, L.A.; Do, A.H.)
Abstract	Electrocorticogram (ECoG) is a promising long-term signal acquisition platform for brain-computer interface (BCI) systems such as upper extremity prostheses. Several studies have demonstrated decoding of arm and finger trajectories from ECoG high-gamma band (80-160 Hz) signals. In this study, we systematically vary the velocity of three elementary movement types (pincer grasp, elbow and shoulder flexion/extension) to test whether the high-gamma band encodes for the entirety of the movements, or merely the movement onset. To this end, linear regression models were created for the durations and amplitudes of high-gamma power bursts and velocity deflections. One subject with 8×8 high-density ECoG grid (4 mm center-to-center electrode spacing) participated in the experiment. The results of the regression models indicated that the power burst durations varied directly with the movement durations (e.g. $R^{2}=0.71$ and slope=1.0 s/s for elbow). The persistence of power bursts for the duration of the movement suggests that the primary motor cortex (M1) is likely active for the entire duration of a movement, instead of providing a marker for the movement onset. On the other hand, the amplitudes were less co-varied. Furthermore, the electrodes of maximum $R^{2}$ conformed to somatotopic arrangement of the brain. Also, electrodes responsible for flexion and extension movements could be resolved on the high-density grid. In summary, these findings suggest that M1 may be directly responsible for activating the individual muscle motor units, and future BCI may be able to utilize them for better control of prostheses.
Sponsorship	IEEE Eng. Med. Biol. Soc.
Starting Page	1243
Ending Page	1246
File Size	2242645
Page Count	4
File Format	PDF
ISBN	9781424479290
ISSN	1557170X
DOI	10.1109/EMBC.2014.6943822
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-08-26
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Electrodes Trajectory Biological system modeling Joints Decoding Elbow
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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