Fig.4 Classification accuracy of different classifiers in the
three conditions
Conclusion : The active rehabilitation system based on BCI is an
important tool in the future process. In this study, we proposed an
effective method to detect the motion intention of low limbs using EEG
signals. Firstly, a movement experiment was designed with the EEG signal
synchronously recorded and the brain was divided into eight regions.
Then, a series of entropy features and complexity parameters were
discussed respectively for the grand averaged EEGs from each brain
region. Finally, through the statistical analysis, relevant features
with significant differences among the three motion conditions are
screened out, combined with SVM, LDA and LR to realize the
classification of standing, sitting and quiet. The highest
classification accuracy is up to 87%, which proves the feasibility of
detecting and recognizing the lower limb motion intention based on the
complexity parameters of EEGs. This
study also provides a new insight for EEG based motion intention
detection, and has a certain reference value for the development of BCI
based lower limb walking aid and rehabilitation robot.
Acknowledgments: This work was supported by the Science and
Technology Project of Lanzhou City in China (No. 2021-1-150), and the
Science and Technology Project of Gansu Province in China (No.
20JR10RA215), and the open project of Neuracle Company (No.
BRKOT-LZJTU-20220329C). We also would like to thanks the support from
Tianyou Youth Talent Lift Program of Lanzhou Jiaotong University,
Lanzhou, China.
Received: 22 Augest 2022 Accepted: xx March 2022
doi: 10.****
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