An Adaptive EEG Feature Extraction Method Based on Stacked Denoising Autoencoder for Mental Fatigue Connectivity
Mental fatigue is a common psychobiological state elected by prolonged cognitive activities. Although, the performance and the disadvantage of the mental fatigue have been well known, its connectivity among the multiareas of the brain has not been thoroughly studied yet. This is important for the cl...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Neural Plasticity |
| Online Access: | http://dx.doi.org/10.1155/2021/3965385 |
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| author | Zhongliang Yu Lili Li Wenwei Zhang Hangyuan Lv Yun Liu Umair Khalique |
| author_facet | Zhongliang Yu Lili Li Wenwei Zhang Hangyuan Lv Yun Liu Umair Khalique |
| author_sort | Zhongliang Yu |
| collection | DOAJ |
| description | Mental fatigue is a common psychobiological state elected by prolonged cognitive activities. Although, the performance and the disadvantage of the mental fatigue have been well known, its connectivity among the multiareas of the brain has not been thoroughly studied yet. This is important for the clarification of the mental fatigue mechanism. However, the common method of connectivity analysis based on EEG cannot get rid of the interference from strong noise. In this paper, an adaptive feature extraction model based on stacked denoising autoencoder has been proposed. The signal to noise ratio of the extracted feature has been analyzed. Compared with principal component analysis, the proposed method can significantly improve the signal to noise ratio and suppress the noise interference. The proposed method has been applied on the analysis of mental fatigue connectivity. The causal connectivity among the frontal, motor, parietal, and visual areas under the awake, fatigue, and sleep deprivation conditions has been analyzed, and different patterns of connectivity between conditions have been revealed. The connectivity direction under awake condition and sleep deprivation condition is opposite. Moreover, there is a complex and bidirectional connectivity relationship, from the anterior areas to the posterior areas and from the posterior areas to the anterior areas, under fatigue condition. These results imply that there are different brain patterns on the three conditions. This study provides an effective method for EEG analysis. It may be favorable to disclose the underlying mechanism of mental fatigue by connectivity analysis. |
| format | Article |
| id | doaj-art-f8255179039548278a41cb300329a8be |
| institution | Kabale University |
| issn | 2090-5904 1687-5443 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Neural Plasticity |
| spelling | doaj-art-f8255179039548278a41cb300329a8be2025-02-03T01:31:09ZengWileyNeural Plasticity2090-59041687-54432021-01-01202110.1155/2021/39653853965385An Adaptive EEG Feature Extraction Method Based on Stacked Denoising Autoencoder for Mental Fatigue ConnectivityZhongliang Yu0Lili Li1Wenwei Zhang2Hangyuan Lv3Yun Liu4Umair Khalique5College of New Materials and New Energies, Shenzhen Technology University, Shenzhen, Guangdong 518118, ChinaCollege of Heath Science and Environment Engineering, Shenzhen Technology University, Shenzhen, Guangdong 518118, ChinaCollege of New Materials and New Energies, Shenzhen Technology University, Shenzhen, Guangdong 518118, ChinaSchool of Mechanical Engineering and Automation, Northeastern University, Shenyang 110819, ChinaCollege of Information, Liaoning University, Shenyang 110136, ChinaSchool of Mechanical Engineering, Xi’an Jiao Tong University, Xi’an 710049, ChinaMental fatigue is a common psychobiological state elected by prolonged cognitive activities. Although, the performance and the disadvantage of the mental fatigue have been well known, its connectivity among the multiareas of the brain has not been thoroughly studied yet. This is important for the clarification of the mental fatigue mechanism. However, the common method of connectivity analysis based on EEG cannot get rid of the interference from strong noise. In this paper, an adaptive feature extraction model based on stacked denoising autoencoder has been proposed. The signal to noise ratio of the extracted feature has been analyzed. Compared with principal component analysis, the proposed method can significantly improve the signal to noise ratio and suppress the noise interference. The proposed method has been applied on the analysis of mental fatigue connectivity. The causal connectivity among the frontal, motor, parietal, and visual areas under the awake, fatigue, and sleep deprivation conditions has been analyzed, and different patterns of connectivity between conditions have been revealed. The connectivity direction under awake condition and sleep deprivation condition is opposite. Moreover, there is a complex and bidirectional connectivity relationship, from the anterior areas to the posterior areas and from the posterior areas to the anterior areas, under fatigue condition. These results imply that there are different brain patterns on the three conditions. This study provides an effective method for EEG analysis. It may be favorable to disclose the underlying mechanism of mental fatigue by connectivity analysis.http://dx.doi.org/10.1155/2021/3965385 |
| spellingShingle | Zhongliang Yu Lili Li Wenwei Zhang Hangyuan Lv Yun Liu Umair Khalique An Adaptive EEG Feature Extraction Method Based on Stacked Denoising Autoencoder for Mental Fatigue Connectivity Neural Plasticity |
| title | An Adaptive EEG Feature Extraction Method Based on Stacked Denoising Autoencoder for Mental Fatigue Connectivity |
| title_full | An Adaptive EEG Feature Extraction Method Based on Stacked Denoising Autoencoder for Mental Fatigue Connectivity |
| title_fullStr | An Adaptive EEG Feature Extraction Method Based on Stacked Denoising Autoencoder for Mental Fatigue Connectivity |
| title_full_unstemmed | An Adaptive EEG Feature Extraction Method Based on Stacked Denoising Autoencoder for Mental Fatigue Connectivity |
| title_short | An Adaptive EEG Feature Extraction Method Based on Stacked Denoising Autoencoder for Mental Fatigue Connectivity |
| title_sort | adaptive eeg feature extraction method based on stacked denoising autoencoder for mental fatigue connectivity |
| url | http://dx.doi.org/10.1155/2021/3965385 |
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