Motor Imagery EEG Classification Based on Multi-Domain Feature Rotation and Stacking Ensemble
Background: Decoding motor intentions from electroencephalogram (EEG) signals is a critical component of motor imagery-based brain–computer interface (MI–BCIs). In traditional EEG signal classification, effectively utilizing the valuable information contained within the electroencephalogram is cruci...
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2025-01-01
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| Series: | Brain Sciences |
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| author | Xianglong Zhu Ming Meng Zewen Yan Zhizeng Luo |
| author_facet | Xianglong Zhu Ming Meng Zewen Yan Zhizeng Luo |
| author_sort | Xianglong Zhu |
| collection | DOAJ |
| description | Background: Decoding motor intentions from electroencephalogram (EEG) signals is a critical component of motor imagery-based brain–computer interface (MI–BCIs). In traditional EEG signal classification, effectively utilizing the valuable information contained within the electroencephalogram is crucial. Objectives: To further optimize the use of information from various domains, we propose a novel framework based on multi-domain feature rotation transformation and stacking ensemble for classifying MI tasks. Methods: Initially, we extract the features of Time Domain, Frequency domain, Time-Frequency domain, and Spatial Domain from the EEG signals, and perform feature selection for each domain to identify significant features that possess strong discriminative capacity. Subsequently, local rotation transformations are applied to the significant feature set to generate a rotated feature set, enhancing the representational capacity of the features. Next, the rotated features were fused with the original significant features from each domain to obtain composite features for each domain. Finally, we employ a stacking ensemble approach, where the prediction results of base classifiers corresponding to different domain features and the set of significant features undergo linear discriminant analysis for dimensionality reduction, yielding discriminative feature integration as input for the meta-classifier for classification. Results: The proposed method achieves average classification accuracies of 92.92%, 89.13%, and 86.26% on the BCI Competition III Dataset IVa, BCI Competition IV Dataset I, and BCI Competition IV Dataset 2a, respectively. Conclusions: Experimental results show that the method proposed in this paper outperforms several existing MI classification methods, such as the Common Time-Frequency-Spatial Patterns and the Selective Extract of the Multi-View Time-Frequency Decomposed Spatial, in terms of classification accuracy and robustness. |
| format | Article |
| id | doaj-art-a5053f5a244b40bf882e21aa185acedc |
| institution | Kabale University |
| issn | 2076-3425 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Brain Sciences |
| spelling | doaj-art-a5053f5a244b40bf882e21aa185acedc2025-01-24T13:25:48ZengMDPI AGBrain Sciences2076-34252025-01-011515010.3390/brainsci15010050Motor Imagery EEG Classification Based on Multi-Domain Feature Rotation and Stacking EnsembleXianglong Zhu0Ming Meng1Zewen Yan2Zhizeng Luo3School of Automation, Hangzhou Dianzi University, Hangzhou 310018, ChinaSchool of Automation, Hangzhou Dianzi University, Hangzhou 310018, ChinaSchool of Automation, Hangzhou Dianzi University, Hangzhou 310018, ChinaSchool of Automation, Hangzhou Dianzi University, Hangzhou 310018, ChinaBackground: Decoding motor intentions from electroencephalogram (EEG) signals is a critical component of motor imagery-based brain–computer interface (MI–BCIs). In traditional EEG signal classification, effectively utilizing the valuable information contained within the electroencephalogram is crucial. Objectives: To further optimize the use of information from various domains, we propose a novel framework based on multi-domain feature rotation transformation and stacking ensemble for classifying MI tasks. Methods: Initially, we extract the features of Time Domain, Frequency domain, Time-Frequency domain, and Spatial Domain from the EEG signals, and perform feature selection for each domain to identify significant features that possess strong discriminative capacity. Subsequently, local rotation transformations are applied to the significant feature set to generate a rotated feature set, enhancing the representational capacity of the features. Next, the rotated features were fused with the original significant features from each domain to obtain composite features for each domain. Finally, we employ a stacking ensemble approach, where the prediction results of base classifiers corresponding to different domain features and the set of significant features undergo linear discriminant analysis for dimensionality reduction, yielding discriminative feature integration as input for the meta-classifier for classification. Results: The proposed method achieves average classification accuracies of 92.92%, 89.13%, and 86.26% on the BCI Competition III Dataset IVa, BCI Competition IV Dataset I, and BCI Competition IV Dataset 2a, respectively. Conclusions: Experimental results show that the method proposed in this paper outperforms several existing MI classification methods, such as the Common Time-Frequency-Spatial Patterns and the Selective Extract of the Multi-View Time-Frequency Decomposed Spatial, in terms of classification accuracy and robustness.https://www.mdpi.com/2076-3425/15/1/50electroencephalogrammotor imagerystacking ensemblemulti-domain featuresrotation transform |
| spellingShingle | Xianglong Zhu Ming Meng Zewen Yan Zhizeng Luo Motor Imagery EEG Classification Based on Multi-Domain Feature Rotation and Stacking Ensemble Brain Sciences electroencephalogram motor imagery stacking ensemble multi-domain features rotation transform |
| title | Motor Imagery EEG Classification Based on Multi-Domain Feature Rotation and Stacking Ensemble |
| title_full | Motor Imagery EEG Classification Based on Multi-Domain Feature Rotation and Stacking Ensemble |
| title_fullStr | Motor Imagery EEG Classification Based on Multi-Domain Feature Rotation and Stacking Ensemble |
| title_full_unstemmed | Motor Imagery EEG Classification Based on Multi-Domain Feature Rotation and Stacking Ensemble |
| title_short | Motor Imagery EEG Classification Based on Multi-Domain Feature Rotation and Stacking Ensemble |
| title_sort | motor imagery eeg classification based on multi domain feature rotation and stacking ensemble |
| topic | electroencephalogram motor imagery stacking ensemble multi-domain features rotation transform |
| url | https://www.mdpi.com/2076-3425/15/1/50 |
| work_keys_str_mv | AT xianglongzhu motorimageryeegclassificationbasedonmultidomainfeaturerotationandstackingensemble AT mingmeng motorimageryeegclassificationbasedonmultidomainfeaturerotationandstackingensemble AT zewenyan motorimageryeegclassificationbasedonmultidomainfeaturerotationandstackingensemble AT zhizengluo motorimageryeegclassificationbasedonmultidomainfeaturerotationandstackingensemble |