• KSBNS 2024


Original Article

Exp Neurobiol 2009; 18(2): 97-111

Published online December 31, 2009

© The Korean Society for Brain and Neural Sciences

Real-time One-dimensional Brain-Computer Interface (BCI) Using Prefrontal Cortex Neuronal Activities of Rats

Yi-Ran Lang, Hyunjoo Lee and Hyung-Cheul Shin*

Department of Physiology, College of Medicine, Hallym University, Chuncheon 200-702, Korea

Correspondence to: *To whom correspondence should be addressed.
TEL: 82-33-248-2585, FAX: 82-33-256-3426


The aim of this study is to verify the feasibility of control of one-dimensional (1-D) rotating machine using neural activities of Prefrontal cortex (PFC) in a BCI system. In this study, adult male Sprague-Dawley rats received bilateral implantation of recording micro-electrodes in PFC area. The spontaneous activities of a pair of PFC neurons of water-deprived rats were encoded and converted through a triple-step threshold comparator algorithm to three commands for one-dimensional movement control of a robotic wheel for accessing water. Averaged activities of two PFC neurons were quantized in every 200 ms to four ranges of activities around the mean firing rates (±0.5 SD) and were converted to four values. After comparison of the values of two chosen neuron units, direction and speed of rotation were decided. Rats were trained to complete one-dimensional control task to obtain water reward. The results indicated the percentage of stop event increased alone with more training. Different brain activity significantly influenced total water-drinking duration and non-water-drinking duration. Events generated from neuronal activity differed according to variant experimental sessions. Correlation between two signal units impacted controlling performance. Overall, the results of this study suggest that rats were able to manipulate the 1-D BCI system by differentially modulating PFC single neuron activities according to different circumstances.

Keywords: brain-computer interface, prefrontal cortex, rat, one-dimensional, single neuron recording