Research on motor imagery recognition based on feature fusion and transfer adaptive boosting.
10.7507/1001-5515.202304067
- Author:
Yuxin ZHANG
1
;
Chenrui ZHANG
1
;
Shihao SUN
2
;
Guizhi XU
1
Author Information
1. Department of Biomedical Engineering, School of Health Sciences and Biomedical Engineering, Hebei University of Technology, TianJin 300130, P. R. China.
2. State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, P. R. China.
- Publication Type:Journal Article
- Keywords:
Brain-computer interface;
Feature fusion;
Motor imagery;
Transfer adaptive boosting;
Transfer learning
- MeSH:
Brain-Computer Interfaces;
Humans;
Support Vector Machine;
Algorithms;
Neural Networks, Computer;
Imagination/physiology*;
Pattern Recognition, Automated/methods*;
Electroencephalography;
Wavelet Analysis
- From:
Journal of Biomedical Engineering
2025;42(1):9-16
- CountryChina
- Language:Chinese
-
Abstract:
This paper proposes a motor imagery recognition algorithm based on feature fusion and transfer adaptive boosting (TrAdaboost) to address the issue of low accuracy in motor imagery (MI) recognition across subjects, thereby increasing the reliability of MI-based brain-computer interfaces (BCI) for cross-individual use. Using the autoregressive model, power spectral density and discrete wavelet transform, time-frequency domain features of MI can be obtained, while the filter bank common spatial pattern is used to extract spatial domain features, and multi-scale dispersion entropy is employed to extract nonlinear features. The IV-2a dataset from the 4 th International BCI Competition was used for the binary classification task, with the pattern recognition model constructed by combining the improved TrAdaboost integrated learning algorithm with support vector machine (SVM), k nearest neighbor (KNN), and mind evolutionary algorithm-based back propagation (MEA-BP) neural network. The results show that the SVM-based TrAdaboost integrated learning algorithm has the best performance when 30% of the target domain instance data is migrated, with an average classification accuracy of 86.17%, a Kappa value of 0.723 3, and an AUC value of 0.849 8. These results suggest that the algorithm can be used to recognize MI signals across individuals, providing a new way to improve the generalization capability of BCI recognition models.
