Subject inefficiency phenomenon of motor imagery brain-computer interface: Influence factors and potential solutions  

作　　者：Rui Zhang Fali Li Tao Zhang Dezhong Yao Peng Xu 

机构地区：[1]Henan Key Laboratory of Brain Science and Brain‐Computer Interface Technology,School of Electrical Engineering,Zhengzhou University,Zhengzhou 450001,Henan,China [2]MOE Key Lab for NeuroInformation,University of Electronic Science and Technology of China,Chengdu 611731,Sichuan,China [3]Science of School,Xihua University,Chengdu 610039,Sichuan,China

出　　处：《Brain Science Advances》2020年第3期224-241,共18页神经科学（英文）

基　　金：supported by grants from the National Natural Science Foundation of China(NSFC,Grant No.61603344,No.61961160705,No.#U19A2082);the Key Research Projects of Henan Higher Education Institutions(Project No.16A120008)

摘　　要：Motor imagery brain–computer interfaces(MI-BCIs)have great potential value in prosthetics control,neurorehabilitation,and gaming;however,currently,most such systems only operate in controlled laboratory environments.One of the most important obstacles is the MI-BCI inefficiency phenomenon.The accuracy of MI-BCI control varies significantly(from chance level to 100%accuracy)across subjects due to the not easily induced and unstable MI-related EEG features.An MI-BCI inefficient subject is defined as a subject who cannot achieve greater than 70%accuracy after sufficient training time,and multiple survey results indicate that inefficient subjects account for 10%–50%of the experimental population.The widespread use of MI-BCI has been seriously limited due to these large percentages of inefficient subjects.In this review,we summarize recent findings of the cause of MI-BCI inefficiency from resting-state brain function,task-related brain activity,brain structure,and psychological perspectives.These factors help understand the reasons for inter-subject MI-BCI control performance variability,and it can be concluded that the lower resting-state sensorimotor rhythm(SMR)is the key factor in MI-BCI inefficiency,which has been confirmed by multiple independent laboratories.We then propose to divide MI-BCI inefficient subjects into three categories according to the resting-state SMR and offline/online accuracy to apply more accurate approaches to solve the inefficiency problem.The potential solutions include developing transfer learning algorithms,new experimental paradigms,mindfulness meditation practice,novel training strategies,and identifying new motor imagery-related EEG features.To date,few studies have focused on improving the control accuracy of MI-BCI inefficient subjects;thus,we appeal to the BCI community to focus more on this research area.Only by reducing the percentage of inefficient subjects can we create the opportunity to expand the value and influence of MI-BCI.

关 键 词：motor imagery brain-computer interface(MI-BCI) inefficient BCI user EEG indicator brain structure transfer learning 

分 类 号：R318[医药卫生—生物医学工程] TN911.73[医药卫生—基础医学]