Recognition of human emotions using EEG signals: A review

被引:94
|
作者
Rahman, Md. Mustafizur [1 ]
Sarkar, Ajay Krishno [2 ]
Hossain, Md. Amzad [1 ]
Hossain, Md. Selim [2 ]
Islam, Md. Rabiul [3 ]
Hossain, Md. Biplob [1 ]
Quinn, Julian M. W. [4 ]
Moni, Mohammad Ali [4 ,5 ]
机构
[1] Jashore Univ Sci & Technol, Dept Elect & Elect Engn, Jashore 7408, Bangladesh
[2] Rajshahi Univ Engn & Technol, Dept Elect & Elect Engn, Rajshahi 6204, Bangladesh
[3] Khulna Univ Engn & Technol, Dept Elect & Elect Engn, Khulna 9203, Bangladesh
[4] Garvan Inst Med Res, Hlth Ageing Theme, Darlinghurst, NSW 2010, Australia
[5] Univ Queensland St Lucia, Fac Hlth & Behav Sci, Sch Hlth & Rehabil Sci, St Lucia, Qld 4072, Australia
关键词
Emotion; Electroencephalography; Classification; Recognition; FEATURE-SELECTION; MODE DECOMPOSITION; ARTIFACT REMOVAL; BRAIN; CLASSIFICATION; ECG; DATABASE; MACHINE; SYSTEM; MUSIC;
D O I
10.1016/j.compbiomed.2021.104696
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
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.
引用
收藏
页数:18
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