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SCI Article

Decoding Saccadic Directions Using Epidural ECoG in Non-Human Primates
Author
Lee, Jeyeon (Dept Biomed Engn); Choi, Hoseok (Dept Biomed Engn); Lee, Seho (Dept Biomed Engn); 김인영 (Dept Biomed Engn); Jang, Dong-Pyo (Dept Biomed Engn);
Corresponding Author Info
Jang, DP (reprint author), Hanyang Univ, Dept Biomed Engn, 222-1 Wangsimni Ro, Seoul 04763, South Korea.
E-mail
dongpjang@gmail.com
Document Type
Article
Source
JOURNAL OF KOREAN MEDICAL SCIENCE Volume:32 Issue:8 Pages:1243-1250 Published:2017
Times Cited
0
External Information
http://dx.doi.org/10.3346/jkms.2017.32.8.1243
Abstract
A brain-computer interface (BCI) can be used to restore some communication as an alternative interface for patients suffering from locked-in syndrome. However, most BCI systems are based on SSVEP, P300, or motor imagery, and a diversity of BCI protocols would be needed for various types of patients. In this paper, we trained the choice saccade (CS) task in 2 non-human primate monkeys and recorded the brain signal using an epidural electrocorticogram (eECoG) to predict eye movement direction. We successfully predicted the direction of the upcoming eye movement using a support vector machine (SVM) with the brain signals after the directional cue onset and before the saccade execution. The mean accuracies were 80% for 2 directions and 43% for 4 directions. We also quantified the spatial-spectro-temporal contribution ratio using SVM recursive feature elimination (RFE). The channels over the frontal eye field (FEF), supplementary eye field (SEF), and superior parietal lobule (SPL) area were dominantly used for classification. The alpha-band in the spectral domain and the time bins just after the directional cue onset and just before the saccadic execution were mainly useful for prediction. A saccade based BCI paradigm can be projected in the 2D space, and will hopefully provide an intuitive and convenient communication platform for users.
Web of Science Categories
Medicine, General & Internal
Funding
Brain Research Program through the National Research Foundation of Korea (NRF) - Ministry of Science, ICT & Future Planning [2016M3C7A1904987]; Korea Health Technology R & D Project through the Korea Health Industry Development Institute (KHIDI) - Ministr
Language
English
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