面向运动决策识别的fNIRS-BCI应用研究  

Applied Research of fNIRS⁃BCI for Motion Decision Recognition

作　　者：秦转萍[1,2] 刘欣霖 路光达 张伟[3] 刘东远 高峰[4] Qin Zhuanping;Liu Xinlin;Lu Guangda;Zhang Wei;Liu Dongyuan;Gao Feng(School of Automation and Electrical Engineering,Tianjin University of Technology and Education,Tianjin 300222,China;Tianjin Key Laboratory of Information Sensing&Intelligent Control,Tianjin 300222,China;School of Transportation Science and Engineering,Civil Aviation University of China,Tianjin University,Tianjin 300300,China;College of Precision Instruments and Optoelectronics Engineering,Tianjin University,Tianjin 300072,China)

机构地区：[1]天津职业技术师范大学自动化与电气工程学院,天津300222 [2]天津市信息传感与智能控制重点实验室,天津300222 [3]中国民航大学交通科学与工程学院,天津300300 [4]天津大学精密仪器与光电子工程学院,天津300072

出　　处：《中国激光》2024年第15期184-193,共10页Chinese Journal of Lasers

基　　金：国家自然科学基金(81971656,62075156,62205239);中国博士后自然科学基金(2023M732600);天津市教委科研计划重点项目(2022ZD009,2022ZD035);天津市“揭榜挂帅”科技计划项目(2023JB02)。

摘　　要：便捷可靠地获得日常情境下的脑激活模式是实现脑机接口实用化的重要前提。额叶作为参与人认知、决策和执行过程的重要脑区,其结构平整且无毛发遮挡,特别适合使用基于功能性近红外光谱技术(fNIRS)的便携式设备进行日常情境脑激活信息的可靠测量。以实用化fNIRS脑机接口应用为目标,发展一种基于统计参数特征的优选策略,实现单周期下不同运动任务的实时分类。首先,使用三次样条插值和Savitzky-Golay等融合滤波算法对测量信息进行平滑滤波,以检测和校正运动伪影。然后将统计优选后的脑激活特征输入到分类模型中,实现左、右手抓握运动和左、右手指敲击,以及静息状态三种动作模式的准确分类。进一步地,根据分类模型开发在线运动执行交互式分类界面,利用训练好的分类模型可实时显示受试者的动作执行意图,为面向运动决策识别的实用化脑机接口应用提供重要工具和方法参考。Objective With the vast expansion and increasing demand for the application domain of brain-computer interface(BCI)technology,stringent requirements have been imposed for the precision,stability,and convenience of the instruments and algorithms employed in implementing BCI technology.The adoption of BCI based on functional near-infrared spectroscopy(fNIRS)has successfully attained equilibrium among assorted factors such as acquisition modality,signal efficacy,deployment complexity,and resilience to interference,thereby making it a pivotal component of BCI research.In this investigation,portable fNIRS devices are used to achieve highly precise motion decision recognition within a single cycle,as indicated in the display of classification outcomes.The results of this study serve as a pivotal resource,offering invaluable tools and methodological references for the pragmatic application of BCI in motion decision recognition.Methods Neural activation data are acquired from a cohort of three individuals during motion execution,and an optimization strategy based on statistical parameter attributes is subsequently devised.This study aims to achieve instantaneous classification of distinct motion tasks encompassed within a single cycle.Primarily,the acquired measurement data are carefully refined using sophisticated algorithms such as cubic spline interpolation and fusion filtering techniques such as Savitzky Golay.These methodologies effectively identify and rectify any undesired effects caused by movement-related artifacts.Subsequently,the optimized statistical attributes pertaining to brain activation are input into a classification model,to categorize precisely,three distinct motion decisions:the grasping movement of the left and right hands,finger tapping of the left and right hands,and the resting state.Results and Discussions The accuracy(A),precision(P),recall(R),and F1-score(F1)are employed as performance evaluation metrics for model training.In contrast to the direct T-test optimization,the model trained using t

关 键 词：功能性近红外光谱技术 脑机接口 额叶 运动决策识别 

分 类 号：O434[机械工程—光学工程]