J Korean Med Sci.  2017 Aug;32(8):1243-1250. 10.3346/jkms.2017.32.8.1243.

Decoding Saccadic Directions Using Epidural ECoG in Non-Human Primates

• Lee J
• Choi H
• Lee S
• Cho BH
• Ahn KH
• Kim IY
• Lee KM
• Jang DP

• KMID: 2439456
• DOI: http://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 α-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.

Keyword

Brain-Computer Interfaces; Saccade; Non-Human Primate; Epidural ECoG

MeSH Terms

Brain
Brain-Computer Interfaces
Classification
Cues
Eye Movements
Frontal Lobe
Haplorhini
Humans
Parietal Lobe
Primates*
Quadriplegia
Saccades
Support Vector Machine