Feasibility Study for Fully Prediction of the Movement Imagery Intention of Lower Limb Ambulation

doi:10.12068/j.issn.1005-3026.2024.05.001

Abstract

Abstract:

Acquiring intentions before the start of lower limb movement imagery is the key issue in providing precise control strategies for the lower limb neurorehabilitation system. To this end， the feasibility of using electroencephalogram （EEG） signals prior to movement imagery to fully predict the movement imagery start‐stop intention and intention types of lower limb ambulation is investigated. The EEG signal is pre?processed and the movement?related cortical potential （MRCP） is extracted. 15 channels with significant discriminability are selected based on MRCP. The temporal convolutional network models are used to decode the movement imagery intention and intention types of lower limb ambulation from the selected MRCP channel features. The results show that the EEG channel signals selected by MRCP morphology are with significant separable differences in both start‐stop intention and intention types， verifying that using only pre?movement imagery EEG signals is capable of fully prediction the movement imagery start‐stop intention and intention types of lower limb ambulation movement in humans.

Key words: electroencephalogram （EEG）, movement‐related cortical potential （MRCP）, temporal convolutional network, movement imagery intention of lower limb ambulation, fully prediction

CLC Number:

R 318

Bin ZHOU, Hong WANG, Tan LI, Qin LAN. Feasibility Study for Fully Prediction of the Movement Imagery Intention of Lower Limb Ambulation[J]. Journal of Northeastern University(Natural Science), 2024, 45(5): 609-618.

Figures/Tables 6

Fig.1 Experimental system and schematic diagram of experiment

Fig.2 Structure diagram of TCN model

Fig.3 MRCP morphology analysis results

Fig.4 Results of intention fully predictions

Fig.5 Results of intention type fully predictions

Fig.6 Structure diagram of online fully prediction algorithm

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