Brain computer interface and its application in rehabilitation

【作者】 金晶；

【Author】 Jing Jin;Key Laboratory of Advanced Control and Optimization for Chemical Processes,Ministry of Education,East China University of Science and Technology;

【机构】 华东理工大学信息科学与工程学院自动化系；

【摘要】 The aim of a brain-computer interface(BCI) is to provide a communication channel for patients who have lost normal communication abilities due to severe motor impairments. Many studies of ECUST BCI lab were focused on P300-based BCI system and motor imagery-based rehabilitation system. For examples, 1) Since the row/column flash pattern could not be optimized flexibly in the stimulus configuration, a new non-row/column flash pattern based on binomial coefficients code was presented to avoid the negative effects of ‘repetition blindness’ and ‘attentional blink’[1]. 2) Conspicuous changes of the stimuli could evoke large event-related potentials, however it would lead to strong interference and visual fatigue. To solve this problem, a new stimulus was presented by using minor changes that would lead to strong differences in image information, which could decrease the adjacent interference and mental fatigue. 3) Online training and adaptive strategy significantly decreased the calibration time and increased the information transfer rate. 4) We also developed a new stroke rehabilitation system based on BCI for Chinese patients.[2]更多还原

【Abstract】 The aim of a brain-computer interface(BCI) is to provide a communication channel for patients who have lost normal communication abilities due to severe motor impairments. Many studies of ECUST BCI lab were focused on P300-based BCI system and motor imagery-based rehabilitation system. For examples, 1) Since the row/column flash pattern could not be optimized flexibly in the stimulus configuration, a new non-row/column flash pattern based on binomial coefficients code was presented to avoid the negative effects of ‘repetition blindness’ and ‘attentional blink’[1]. 2) Conspicuous changes of the stimuli could evoke large event-related potentials, however it would lead to strong interference and visual fatigue. To solve this problem, a new stimulus was presented by using minor changes that would lead to strong differences in image information, which could decrease the adjacent interference and mental fatigue. 3) Online training and adaptive strategy significantly decreased the calibration time and increased the information transfer rate. 4) We also developed a new stroke rehabilitation system based on BCI for Chinese patients.[2]更多还原