Lower limb motion image electroencephalogram recognition based on compound limb motion observation
JIANG Xiaohong
DONG Hongtao
ZHANG Guangju
SUN Guodong
MENG Lin
Abstract：Aiming at the problems that the lower limb motor imagery(MI)brain-computer interface system based on electroen-cephalogram(EEG)signals was limited by weak and implicit lower limb MI signals,and the MI evoked paradigm was not natural or ef-fective,we designed a composite limb MI task in standing posture.Firstly,the limb MI experiments of composite limbs was conducted in the standing posture,and the video assistance was used to reduce the difficulty of MI and enhance the effectiveness of MI.Secondly,the EEG features were extracted by common spatial patterns(CSP),filter bank common spatial patterns(FBCSP),and subject-spe-cific common spatial patterns(SSCSP),and the individual best feature selection algorithm based on mutual information was used for feature selection.Finally,the support vector machine(SVM)was used for classification.On the basis of the EEG data collected from 12 subjects,binary classification of compound limb motor imagery and binary classification of unilateral lower limb MI were conducted.When using SSCSP for feature extraction,the average classification accuracy for compound limb MI was 0.70±0.06,which was 6％higher than that for unilateral lower limb MI.It proves that the EEG induce effect of the composite limb movement imagination paradigm can improve the unilateral limb.
Keywords：Motor imageryBrain-computer interfaceCompound limb motionMotor intention recognition
Publication Date：2024-12-28
Online Publishing Date：2026-09-11(First online date of this platform, not the publication date of the document)
Pages：7( 432-438 )