A method of EEG signal feature extraction based on hybrid DWT and EMD
The processing and recognition of electroencephalogram (EEG) signal is the most important part of brain-computer interface (BCI) system, and the quality of signal processing and recognition is directly related to the effectiveness of BCI system. Aiming at the problems of incomplete removal of artifa...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-02-01
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| Series: | Alexandria Engineering Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110016824015278 |
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| author | Xiaozhong Geng Linen Wang Ping Yu Weixin Hu Qipeng Liang Xintong Zhang Cheng Chen Xi Zhang |
| author_facet | Xiaozhong Geng Linen Wang Ping Yu Weixin Hu Qipeng Liang Xintong Zhang Cheng Chen Xi Zhang |
| author_sort | Xiaozhong Geng |
| collection | DOAJ |
| description | The processing and recognition of electroencephalogram (EEG) signal is the most important part of brain-computer interface (BCI) system, and the quality of signal processing and recognition is directly related to the effectiveness of BCI system. Aiming at the problems of incomplete removal of artifacts and inadequate retention of active components in EEG signal, a fusion method of wavelet transform (WT) and Fast Independent Component Analysis (FastICA) is utilized to preprocess the raw EEG signals. The fusion method can remove noise artifacts while preserving effective information. Aiming at the problems of poor time-frequency resolution and low classification accuracy of the traditional feature extraction method, a feature extraction algorithm on the basis of hybrid Discrete Wavelet Transform (DWT) and Empirical Mode Decomposition (EMD) is proposed. Firstly, DWT is used to analyze the pre-processed EEG signal to obtain a series of sub-band signals. Then, EMD decomposition is applied to subband signal and eigenmode function is extracted to complete feature integration. Finally, the feature extraction results are input into the Support Vector Machine (SVM) for classification. Comparative experiments show that the classification accuracy of the proposed method reaches 91.32 %, which is significantly higher than other algorithms. |
| format | Article |
| id | doaj-art-c6bb370ce06e4974ac1266a2399fc9b9 |
| institution | Kabale University |
| issn | 1110-0168 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Alexandria Engineering Journal |
| spelling | doaj-art-c6bb370ce06e4974ac1266a2399fc9b92025-02-07T04:47:11ZengElsevierAlexandria Engineering Journal1110-01682025-02-01113195204A method of EEG signal feature extraction based on hybrid DWT and EMDXiaozhong Geng0Linen Wang1Ping Yu2Weixin Hu3Qipeng Liang4Xintong Zhang5Cheng Chen6Xi Zhang7School of Computer Technology and Engineering, Changchun Institute of Technology, Changchun 130012, ChinaSchool of Information and Control Engineering, Jilin Institute of Chemical Technology, Jilin 132022, ChinaSchool of Computer Technology and Engineering, Changchun Institute of Technology, Changchun 130012, China; Corresponding author.School of Computer Technology and Engineering, Changchun Institute of Technology, Changchun 130012, ChinaSchool of Computer Technology and Engineering, Changchun Institute of Technology, Changchun 130012, ChinaSchool of accounting, Jilin University of Finance and Economics, Changchun 130117, ChinaSchool of Information and Control Engineering, Jilin Institute of Chemical Technology, Jilin 132022, ChinaSchool of Information and Control Engineering, Jilin Institute of Chemical Technology, Jilin 132022, ChinaThe processing and recognition of electroencephalogram (EEG) signal is the most important part of brain-computer interface (BCI) system, and the quality of signal processing and recognition is directly related to the effectiveness of BCI system. Aiming at the problems of incomplete removal of artifacts and inadequate retention of active components in EEG signal, a fusion method of wavelet transform (WT) and Fast Independent Component Analysis (FastICA) is utilized to preprocess the raw EEG signals. The fusion method can remove noise artifacts while preserving effective information. Aiming at the problems of poor time-frequency resolution and low classification accuracy of the traditional feature extraction method, a feature extraction algorithm on the basis of hybrid Discrete Wavelet Transform (DWT) and Empirical Mode Decomposition (EMD) is proposed. Firstly, DWT is used to analyze the pre-processed EEG signal to obtain a series of sub-band signals. Then, EMD decomposition is applied to subband signal and eigenmode function is extracted to complete feature integration. Finally, the feature extraction results are input into the Support Vector Machine (SVM) for classification. Comparative experiments show that the classification accuracy of the proposed method reaches 91.32 %, which is significantly higher than other algorithms.http://www.sciencedirect.com/science/article/pii/S1110016824015278EEGFastICADiscrete wavelet transformEmpirical mode decompositionSupport vector machine |
| spellingShingle | Xiaozhong Geng Linen Wang Ping Yu Weixin Hu Qipeng Liang Xintong Zhang Cheng Chen Xi Zhang A method of EEG signal feature extraction based on hybrid DWT and EMD Alexandria Engineering Journal EEG FastICA Discrete wavelet transform Empirical mode decomposition Support vector machine |
| title | A method of EEG signal feature extraction based on hybrid DWT and EMD |
| title_full | A method of EEG signal feature extraction based on hybrid DWT and EMD |
| title_fullStr | A method of EEG signal feature extraction based on hybrid DWT and EMD |
| title_full_unstemmed | A method of EEG signal feature extraction based on hybrid DWT and EMD |
| title_short | A method of EEG signal feature extraction based on hybrid DWT and EMD |
| title_sort | method of eeg signal feature extraction based on hybrid dwt and emd |
| topic | EEG FastICA Discrete wavelet transform Empirical mode decomposition Support vector machine |
| url | http://www.sciencedirect.com/science/article/pii/S1110016824015278 |
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