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  Neural correlates of unstructured motor behaviors

Gabriel, P. G., Chen, K. J., Alasfour, A., Pailla, T., Doyle, W. K., Devinsky, O., et al. (2019). Neural correlates of unstructured motor behaviors. Journal of Neural Engineering, 16(6): 066026. doi:10.1088/1741-2552/ab355c.

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 Creators:
Gabriel, Paolo G1, Author
Chen, K J1, Author
Alasfour, A1, Author
Pailla, T1, Author
Doyle, W K2, Author
Devinsky, O2, Author
Friedman, D2, Author
Dugan, P2, Author
Melloni, Lucia2, 3, Author           
Thesen, T2, 4, Author
Gonda, D5, Author
Sattar, S5, Author
Wang, S G5, 6, Author
Gilja, V1, Author
Affiliations:
1Department of Electrical and Computer Engineering, University of California , San Diego, La Jolla, CA, United States of America, ou_persistent22              
2Comprehensive Epilepsy Center, New York University Langone Medical Center, New York, NY, United States of America, ou_persistent22              
3Department of Neuroscience, Max Planck Institute for Empirical Aesthetics, Max Planck Society, ou_2421697              
4Department of Physiology, Neuroscience & Behavioral Science, St. George's University , West Indies, Grenada, ou_persistent22              
5Rady Children's Hospital, San Diego, CA, United States of America, ou_persistent22              
6University of Minnesota School of Medicine , MN, United States of America, ou_persistent22              

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 Abstract: Objective. We studied the relationship between uninstructed, unstructured movements and neural activity in three epilepsy patients with intracranial electroencephalographic (iEEG) recordings. Approach. We used a custom system to continuously record high definition video precisely time-aligned to clinical iEEG data. From these video recordings, movement periods were annotated via semi-automatic tracking based on dense optical flow. Main results. We found that neural signal features (8–32 Hz and 76–100 Hz power) previously identified from task-based experiments are also modulated before and during a variety of movement behaviors. These movement behaviors are coarsely labeled by time period and movement side (e.g. 'Idle' and 'Move', 'Right' and 'Left'); movements within a label can include a wide variety of uninstructed behaviors. A rigorous nested cross-validation framework was used to classify both movement onset and lateralization with statistical significance for all subjects. Significance. We demonstrate an evaluation framework to study neural activity related to natural movements not evoked by a task, annotated over hours of video. This work further establishes the feasibility to study neural correlates of unstructured behavior through continuous recording in the epilepsy monitoring unit. The insights gained from such studies may advance our understanding of how the brain naturally controls movement, which may inform the development of more robust and generalizable brain–computer interfaces.

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Language(s): eng - English
 Dates: 2019-01-222019-07-222019-10-302019-12
 Publication Status: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1088/1741-2552/ab355c
 Degree: -

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Title: Journal of Neural Engineering
Source Genre: Journal
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Publ. Info: Bristol : Institute of Physics Publishing
Pages: - Volume / Issue: 16 (6) Sequence Number: 066026 Start / End Page: - Identifier: ISSN: 1741-2552
CoNE: https://pure.mpg.de/cone/journals/resource/17412552