计算机科学
窗口(计算)
脑-机接口
传输(计算)
信息传递
实时计算
模拟
人工智能
模式识别(心理学)
脑电图
电信
心理学
精神科
操作系统
并行计算
作者
Lan Niu,Jianxiong Bin,Jun kong shuai Wang,Gege Zhan,Lihua Zhang,Zhongxue Gan,Xiaoyang Kang
标识
DOI:10.1016/j.bspc.2023.104826
摘要
Identification accuracy and information transfer rate are two critical performance indicators of the brain-computer interfaces and virtual reality (BCI_VR) hybrid system. A parameter closely related to these two indicators is the time-window length of the data. When the time-window value is too large, it is not conducive to the information transmission rate. Similarly, it affects recognition accuracy when the value is too small. How to set the time-window value reasonably to achieve a balance between recognition accuracy and information transfer rate to ensure the high-performance of the hybrid system is very important. In this paper, a dynamic time-window length optimized mechanism is proposed, which decides whether to change the time-window value of different subjects or trials according to the online performance of the hybrid system. Ten participants took part in the online experiment with an average accuracy of 92.07% and an average information transfer rate of 34.367 bits/min. Compared with the time-window adjustment method based on fixed-step in the existing study, the proposed method has achieved a 10.51% increase in information transfer rate and a 5.66% reduction in average detection time under the same experimental flow.
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