The Application of Entropy in Motor Imagery Paradigms of Brain–Computer Interfaces
<b>Background:</b> In motor imagery brain–computer interface (MI-BCI) research, electroencephalogram (EEG) signals are complex and nonlinear. This complexity and nonlinearity render signal processing and classification challenging when employing traditional linear methods. Information en...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-02-01
|
| Series: | Brain Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3425/15/2/168 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849718794994319360 |
|---|---|
| author | Chengzhen Wu Bo Yao Xin Zhang Ting Li Jinhai Wang Jiangbo Pu |
| author_facet | Chengzhen Wu Bo Yao Xin Zhang Ting Li Jinhai Wang Jiangbo Pu |
| author_sort | Chengzhen Wu |
| collection | DOAJ |
| description | <b>Background:</b> In motor imagery brain–computer interface (MI-BCI) research, electroencephalogram (EEG) signals are complex and nonlinear. This complexity and nonlinearity render signal processing and classification challenging when employing traditional linear methods. Information entropy, with its intrinsic nonlinear characteristics, effectively captures the dynamic behavior of EEG signals, thereby addressing the limitations of traditional methods in capturing linear features. However, the multitude of entropy types leads to unclear application scenarios, with a lack of systematic descriptions. <b>Methods:</b> This study conducted a review of 63 high-quality research articles focused on the application of entropy in MI-BCI, published between 2019 and 2023. It summarizes the names, functions, and application scopes of 13 commonly used entropy measures. <b>Results:</b> The findings indicate that sample entropy (16.3%), Shannon entropy (13%), fuzzy entropy (12%), permutation entropy (9.8%), and approximate entropy (7.6%) are the most frequently utilized entropy features in MI-BCI. The majority of studies employ a single entropy feature (79.7%), with dual entropy (9.4%) and triple entropy (4.7%) being the most prevalent combinations in multiple entropy applications. The incorporation of entropy features can significantly enhance pattern classification accuracy (by 8–10%). Most studies (67%) utilize public datasets for classification verification, while a minority design and conduct experiments (28%), and only 5% combine both methods. <b>Conclusions:</b> Future research should delve into the effects of various entropy features on specific problems to clarify their application scenarios. As research methodologies continue to evolve and advance, entropy features are poised to play a significant role in a wide array of fields and contexts. |
| format | Article |
| id | doaj-art-79a5df1cf7a64bc8971dc95d0ea98d14 |
| institution | DOAJ |
| issn | 2076-3425 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Brain Sciences |
| spelling | doaj-art-79a5df1cf7a64bc8971dc95d0ea98d142025-08-20T03:12:16ZengMDPI AGBrain Sciences2076-34252025-02-0115216810.3390/brainsci15020168The Application of Entropy in Motor Imagery Paradigms of Brain–Computer InterfacesChengzhen Wu0Bo Yao1Xin Zhang2Ting Li3Jinhai Wang4Jiangbo Pu5School of Life Sciences, Tiangong University, Tianjin 300387, ChinaInstitute of Biomedical Engineering, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin 300192, ChinaInstitute of Biomedical Engineering, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin 300192, ChinaInstitute of Biomedical Engineering, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin 300192, ChinaSchool of Life Sciences, Tiangong University, Tianjin 300387, ChinaInstitute of Biomedical Engineering, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin 300192, China<b>Background:</b> In motor imagery brain–computer interface (MI-BCI) research, electroencephalogram (EEG) signals are complex and nonlinear. This complexity and nonlinearity render signal processing and classification challenging when employing traditional linear methods. Information entropy, with its intrinsic nonlinear characteristics, effectively captures the dynamic behavior of EEG signals, thereby addressing the limitations of traditional methods in capturing linear features. However, the multitude of entropy types leads to unclear application scenarios, with a lack of systematic descriptions. <b>Methods:</b> This study conducted a review of 63 high-quality research articles focused on the application of entropy in MI-BCI, published between 2019 and 2023. It summarizes the names, functions, and application scopes of 13 commonly used entropy measures. <b>Results:</b> The findings indicate that sample entropy (16.3%), Shannon entropy (13%), fuzzy entropy (12%), permutation entropy (9.8%), and approximate entropy (7.6%) are the most frequently utilized entropy features in MI-BCI. The majority of studies employ a single entropy feature (79.7%), with dual entropy (9.4%) and triple entropy (4.7%) being the most prevalent combinations in multiple entropy applications. The incorporation of entropy features can significantly enhance pattern classification accuracy (by 8–10%). Most studies (67%) utilize public datasets for classification verification, while a minority design and conduct experiments (28%), and only 5% combine both methods. <b>Conclusions:</b> Future research should delve into the effects of various entropy features on specific problems to clarify their application scenarios. As research methodologies continue to evolve and advance, entropy features are poised to play a significant role in a wide array of fields and contexts.https://www.mdpi.com/2076-3425/15/2/168information entropymotor imageryelectroencephalogrampattern classification |
| spellingShingle | Chengzhen Wu Bo Yao Xin Zhang Ting Li Jinhai Wang Jiangbo Pu The Application of Entropy in Motor Imagery Paradigms of Brain–Computer Interfaces Brain Sciences information entropy motor imagery electroencephalogram pattern classification |
| title | The Application of Entropy in Motor Imagery Paradigms of Brain–Computer Interfaces |
| title_full | The Application of Entropy in Motor Imagery Paradigms of Brain–Computer Interfaces |
| title_fullStr | The Application of Entropy in Motor Imagery Paradigms of Brain–Computer Interfaces |
| title_full_unstemmed | The Application of Entropy in Motor Imagery Paradigms of Brain–Computer Interfaces |
| title_short | The Application of Entropy in Motor Imagery Paradigms of Brain–Computer Interfaces |
| title_sort | application of entropy in motor imagery paradigms of brain computer interfaces |
| topic | information entropy motor imagery electroencephalogram pattern classification |
| url | https://www.mdpi.com/2076-3425/15/2/168 |
| work_keys_str_mv | AT chengzhenwu theapplicationofentropyinmotorimageryparadigmsofbraincomputerinterfaces AT boyao theapplicationofentropyinmotorimageryparadigmsofbraincomputerinterfaces AT xinzhang theapplicationofentropyinmotorimageryparadigmsofbraincomputerinterfaces AT tingli theapplicationofentropyinmotorimageryparadigmsofbraincomputerinterfaces AT jinhaiwang theapplicationofentropyinmotorimageryparadigmsofbraincomputerinterfaces AT jiangbopu theapplicationofentropyinmotorimageryparadigmsofbraincomputerinterfaces AT chengzhenwu applicationofentropyinmotorimageryparadigmsofbraincomputerinterfaces AT boyao applicationofentropyinmotorimageryparadigmsofbraincomputerinterfaces AT xinzhang applicationofentropyinmotorimageryparadigmsofbraincomputerinterfaces AT tingli applicationofentropyinmotorimageryparadigmsofbraincomputerinterfaces AT jinhaiwang applicationofentropyinmotorimageryparadigmsofbraincomputerinterfaces AT jiangbopu applicationofentropyinmotorimageryparadigmsofbraincomputerinterfaces |