期刊
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
卷 19, 期 2, 页码 193-203出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNSRE.2011.2107750
关键词
Amyotrophic lateral sclerosis; human motor cortex; intracortical neural interface system; multi-state decoding; point-and-click control; quadriplegia; stroke
资金
- NIH-NINDS, NSF/NIH [R01 NS 50867-01]
- NIH [R01DC009899, N01HD53403, RC1HD063931]
- Office of Naval Research [N0014-04-1-082]
- European Neurobotics Program [FP6-IST-001917]
- Office of Research and Development, Rehabilitation R&D Service, Department of Veterans Affairs
- Doris Duke Charitable Foundation
- MGH-Deane Institute
- Ministry of Education, Science and Technology [R31-10008]
We present a point-and-click intracortical neural interface system (NIS) that enables humans with tetraplegia to volitionally move a 2-D computer cursor in any desired direction on a computer screen, hold it still, and click on the area of interest. This direct brain-computer interface extracts both discrete (click) and continuous (cursor velocity) signals from a single small population of neurons in human motor cortex. A key component of this system is a multi-state probabilistic decoding algorithm that simultaneously decodes neural spiking activity of a small population of neurons and outputs either a click signal or the velocity of the cursor. The algorithm combines a linear classifier, which determines whether the user is intending to click or move the cursor, with a Kalman filter that translates the neural population activity into cursor velocity. We present a paradigm for training the multi-state decoding algorithm using neural activity observed during imagined actions. Two human participants with tetraplegia (paralysis of the four limbs) performed a closed-loop radial target acquisition task using the point-and-click NIS over multiple sessions. We quantified point-and-click performance using various human-computer interaction measurements for pointing devices. We found that participants could control the cursor motion and click on specified targets with a small error rate (<3% in one participant). This study suggests that signals from a small ensemble of motor cortical neurons (similar to 40) can be used for natural point-and-click 2-D cursor control of a personal computer.
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