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Wang Z, Huang R, Yan Y, Luo Z, Zhao S, Wang B, Jin J, Xie L, Yin E. An Improved Canonical Correlation Analysis for EEG Inter-Band Correlation Extraction. Bioengineering (Basel) 2023; 10:1200. [PMID: 37892930 PMCID: PMC10604862 DOI: 10.3390/bioengineering10101200] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Revised: 09/29/2023] [Accepted: 10/05/2023] [Indexed: 10/29/2023] Open
Abstract
(1) Background: Emotion recognition based on EEG signals is a rapidly growing and promising research field in affective computing. However, traditional methods have focused on single-channel features that reflect time-domain or frequency-domain information of the EEG, as well as bi-channel features that reveal channel-wise relationships across brain regions. Despite these efforts, the mechanism of mutual interactions between EEG rhythms under different emotional expressions remains largely unexplored. Currently, the primary form of information interaction between EEG rhythms is phase-amplitude coupling (PAC), which results in computational complexity and high computational cost. (2) Methods: To address this issue, we proposed a method of extracting inter-bands correlation (IBC) features via canonical correlation analysis (CCA) based on differential entropy (DE) features. This approach eliminates the need for surrogate testing and reduces computational complexity. (3) Results: Our experiments verified the effectiveness of IBC features through several tests, demonstrating that the more correlated features between EEG frequency bands contribute more to emotion classification accuracy. We then fused IBC features and traditional DE features at the decision level, which significantly improved the accuracy of emotion recognition on the SEED dataset and the local CUMULATE dataset compared to using a single feature alone. (4) Conclusions: These findings suggest that IBC features are a promising approach to promoting emotion recognition accuracy. By exploring the mutual interactions between EEG rhythms under different emotional expressions, our method can provide valuable insights into the underlying mechanisms of emotion processing and improve the performance of emotion recognition systems.
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Affiliation(s)
- Zishan Wang
- School of Information Science and Engineering, East China University of Science and Technology, Shanghai 200030, China; (Z.W.); (B.W.); (J.J.)
- Defense Innovation Institute, Academy of Military Sciences (AMS), Beijing 100000, China; (Z.L.); (S.Z.); (L.X.); (E.Y.)
- Tianjin Artificial Intelligence Innovation Center (TAIIC), Tianjin 300450, China;
| | - Ruqiang Huang
- Tianjin Artificial Intelligence Innovation Center (TAIIC), Tianjin 300450, China;
| | - Ye Yan
- Defense Innovation Institute, Academy of Military Sciences (AMS), Beijing 100000, China; (Z.L.); (S.Z.); (L.X.); (E.Y.)
- Tianjin Artificial Intelligence Innovation Center (TAIIC), Tianjin 300450, China;
| | - Zhiguo Luo
- Defense Innovation Institute, Academy of Military Sciences (AMS), Beijing 100000, China; (Z.L.); (S.Z.); (L.X.); (E.Y.)
- Tianjin Artificial Intelligence Innovation Center (TAIIC), Tianjin 300450, China;
| | - Shaokai Zhao
- Defense Innovation Institute, Academy of Military Sciences (AMS), Beijing 100000, China; (Z.L.); (S.Z.); (L.X.); (E.Y.)
- Tianjin Artificial Intelligence Innovation Center (TAIIC), Tianjin 300450, China;
| | - Bei Wang
- School of Information Science and Engineering, East China University of Science and Technology, Shanghai 200030, China; (Z.W.); (B.W.); (J.J.)
| | - Jing Jin
- School of Information Science and Engineering, East China University of Science and Technology, Shanghai 200030, China; (Z.W.); (B.W.); (J.J.)
| | - Liang Xie
- Defense Innovation Institute, Academy of Military Sciences (AMS), Beijing 100000, China; (Z.L.); (S.Z.); (L.X.); (E.Y.)
- Tianjin Artificial Intelligence Innovation Center (TAIIC), Tianjin 300450, China;
| | - Erwei Yin
- Defense Innovation Institute, Academy of Military Sciences (AMS), Beijing 100000, China; (Z.L.); (S.Z.); (L.X.); (E.Y.)
- Tianjin Artificial Intelligence Innovation Center (TAIIC), Tianjin 300450, China;
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Modality encoded latent dataset for emotion recognition. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104140] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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Gao Z, Wang X, Yang Y, Li Y, Ma K, Chen G. A Channel-Fused Dense Convolutional Network for EEG-Based Emotion Recognition. IEEE Trans Cogn Dev Syst 2021. [DOI: 10.1109/tcds.2020.2976112] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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Ouchani M, Gharibzadeh S, Jamshidi M, Amini M. A Review of Methods of Diagnosis and Complexity Analysis of Alzheimer's Disease Using EEG Signals. BIOMED RESEARCH INTERNATIONAL 2021; 2021:5425569. [PMID: 34746303 PMCID: PMC8566072 DOI: 10.1155/2021/5425569] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Revised: 06/20/2021] [Accepted: 10/18/2021] [Indexed: 01/27/2023]
Abstract
This study will concentrate on recent research on EEG signals for Alzheimer's diagnosis, identifying and comparing key steps of EEG-based Alzheimer's disease (AD) detection, such as EEG signal acquisition, preprocessing function extraction, and classification methods. Furthermore, highlighting general approaches, variations, and agreement in the use of EEG identified shortcomings and guidelines for multiple experimental stages ranging from demographic characteristics to outcomes monitoring for future research. Two main targets have been defined based on the article's purpose: (1) discriminative (or detection), i.e., look for differences in EEG-based features across groups, such as MCI, moderate Alzheimer's disease, extreme Alzheimer's disease, other forms of dementia, and stable normal elderly controls; and (2) progression determination, i.e., look for correlations between EEG-based features and clinical markers linked to MCI-to-AD conversion and Alzheimer's disease intensity progression. Limitations mentioned in the reviewed papers were also gathered and explored in this study, with the goal of gaining a better understanding of the problems that need to be addressed in order to advance the use of EEG in Alzheimer's disease science.
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Affiliation(s)
- Mahshad Ouchani
- Institute for Cognitive and Brain Sciences, Shahid Beheshti University, Tehran, Iran
| | - Shahriar Gharibzadeh
- Institute for Cognitive and Brain Sciences, Shahid Beheshti University, Tehran, Iran
| | - Mahdieh Jamshidi
- Institute for Cognitive and Brain Sciences, Shahid Beheshti University, Tehran, Iran
| | - Morteza Amini
- Shahid Beheshti University, Tehran, Iran
- Institute for Cognitive Science Studies (ICSS), Tehran, Iran
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Rahman MM, Sarkar AK, Hossain MA, Hossain MS, Islam MR, Hossain MB, Quinn JMW, Moni MA. Recognition of human emotions using EEG signals: A review. Comput Biol Med 2021; 136:104696. [PMID: 34388471 DOI: 10.1016/j.compbiomed.2021.104696] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Revised: 07/23/2021] [Accepted: 07/23/2021] [Indexed: 10/20/2022]
Abstract
Assessment of the cognitive functions and state of clinical subjects is an important aspect of e-health care delivery, and in the development of novel human-machine interfaces. A subject can display a range of emotions that significantly influence cognition, and emotion classification through the analysis of physiological signals is a key means of detecting emotion. Electroencephalography (EEG) signals have become a common focus of such development compared to other physiological signals because EEG employs simple and subject-acceptable methods for obtaining data that can be used for emotion analysis. We have therefore reviewed published studies that have used EEG signal data to identify possible interconnections between emotion and brain activity. We then describe theoretical conceptualization of basic emotions, and interpret the prevailing techniques that have been adopted for feature extraction, selection, and classification. Finally, we have compared the outcomes of these recent studies and discussed the likely future directions and main challenges for researchers developing EEG-based emotion analysis methods.
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Affiliation(s)
- Md Mustafizur Rahman
- Department of Electrical and Electronic Engineering, Jashore University of Science & Technology, Jashore, 7408, Bangladesh.
| | - Ajay Krishno Sarkar
- Department of Electrical and Electronic Engineering, Rajshahi University of Engineering & Technology, Rajshahi, 6204, Bangladesh.
| | - Md Amzad Hossain
- Department of Electrical and Electronic Engineering, Jashore University of Science & Technology, Jashore, 7408, Bangladesh.
| | - Md Selim Hossain
- Department of Electrical and Electronic Engineering, Rajshahi University of Engineering & Technology, Rajshahi, 6204, Bangladesh.
| | - Md Rabiul Islam
- Department of Electrical and Electronic Engineering, Khulna University of Engineering & Technology, Khulna, 9203, Bangladesh.
| | - Md Biplob Hossain
- Department of Electrical and Electronic Engineering, Jashore University of Science & Technology, Jashore, 7408, Bangladesh.
| | - Julian M W Quinn
- Healthy Ageing Theme, Garvan Institute of Medical Research, Darlinghurst, NSW, 2010, Australia.
| | - Mohammad Ali Moni
- Healthy Ageing Theme, Garvan Institute of Medical Research, Darlinghurst, NSW, 2010, Australia; School of Health and Rehabilitation Sciences, Faculty of Health and Behavioural Sciences, The University of Queensland St Lucia, QLD 4072, Australia.
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