摘要
脑机接口是一种变革型的人机交互,基于脑电的脑机接口占到研究的大多数,而基于功能性近红外光谱(fNIRS)的脑机接口以其特有的优势开始受到研究者重视。本研究利用fNIRS测量了15名被试在步行想象和空闲状态期间的氧合血红蛋白(HbO)浓度,对HbO信号进行带通滤波和基线漂移矫正。然后提取HbO的均值、峰值和均方根及其组合作为分类特征,最后采用SVM、KNN和LDA进行分类,并测试了任务期间不同时间窗的分类性能。实验结果表明,采用SVM提取3种组合特征的分类准确率明显高于其他特征及分类器,达到了90.37±4.42%;2~8 s时间窗比其他时间窗的分类准确率更高。所提研究有望为步行功能障碍患者提供一种新的可选的主动康复训练方法。
Brain-computer interface is a transformative human-computer interaction. Brain-computer interfaces based on EEG account for most of the research, and functional near-infrared spectroscopy based brain-computer interfaces are beginning to be valued by researchers because of their unique advantages. In the study, fNIRS was used to measure the oxygenated hemoglobin(HbO) concentration of 15 subjects during walking imagery and idle state, and to perform band-pass filtering and baseline drift correction of HbO signals. Then we extracted the mean, peak, root-mean-square and their combinations of HbO as classification features, and finally used SVM, KNN and LDA for classification, and tested the classification performance of different time windows during the task. The experimental results show that the classification accuracy of the three combined features extracted by SVM is significantly higher than other features and classifiers, reaching 90.37±4.42%;the classification accuracy of the 2~8 s time window is higher than that of other time windows. This study is expected to provide a new alternative active rehabilitation training method for patients with walking dysfunction.
作者
李红权
程昭立
王发旺
Li Hongquan;Cheng Zhaoli;Wang Fawang(Institute of Information Engineering and Automation,Kunming University of Science and Technology,Kunming 650500,China)
出处
《电子测量技术》
北大核心
2021年第1期161-164,共4页
Electronic Measurement Technology
关键词
脑机接口
功能性近红外光谱
步行想象
分类准确率
brain-computer interface
functional near-infrared spectroscopy
walking imagery
classification accuracy
