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Kim, Sung-Phil
Brain-Computer Interface (BCI) Lab
Research Interests
  • Brain-computer interface, Statistical Signal Processing, Neural Code, Neuromarketing

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Point-and-Click Cursor Control With an Intracortical Neural Interface System by Humans With Tetraplegia

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Title
Point-and-Click Cursor Control With an Intracortical Neural Interface System by Humans With Tetraplegia
Author
Kim, Sung-PhilSimeral, John D.Hochberg, Leigh R.Donoghue, John P.Friehs, Gerhard M.Black, Michael J.
Issue Date
2011-04
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, v.19, no.2, pp.193 - 203
Abstract
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.
URI
https://scholarworks.unist.ac.kr/handle/201301/9553
DOI
10.1109/TNSRE.2011.2107750
ISSN
1534-4320
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