1
|
Wang H, Sun X, Li X, Gu B, Fu Y, Liu W. The bidirectional influence between emotional language and inhibitory control in Chinese: An ERP study. BRAIN AND LANGUAGE 2024; 256:105457. [PMID: 39154455 DOI: 10.1016/j.bandl.2024.105457] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/14/2024] [Revised: 07/25/2024] [Accepted: 08/02/2024] [Indexed: 08/20/2024]
Abstract
The bidirectional influence between emotional language and inhibitory processes has been studied in alphabetic languages, highlighting the need for additional investigation in nonalphabetic languages to explore potential cross-linguistic differences. The present ERP study investigated the bidirectional influence in the context of Mandarin, a language with unique linguistic features and neural substrates. In Experiment 1, emotional adjectives preceded the Go/NoGo cue. The ERPs revealed that negative emotional language facilitated inhibitory control. In Experiment 2, with a Go/NoGo cue preceding the emotional language, the study confirmed that inhibitory control facilitated the semantic integration of negative language in Chinese, whereas the inhibited state may not affect deeper refinement of the emotional content. However, no interaction was observed in positive emotional language processing. These results suggest an interaction between inhibitory control and negative emotional language processing in Chinese, supporting the integrative emotion-cognition view.
Collapse
Affiliation(s)
- Huili Wang
- School of Foreign Languages, Hangzhou City University, Hangzhou 310015, China; Hangzhou Collaborative Innovation Institute of Language Services, Hangzhou 310015, China
| | - Xiaobing Sun
- Research Institute of Foreign Languages, Beijing Foreign Studies University, Beijing 100089, China; School of Foreign Languages, Dalian University of Technology, Dalian 116024, China
| | - Xueyan Li
- School of Foreign Languages, Dalian University of Technology, Dalian 116024, China
| | - Beixian Gu
- School of Foreign Languages, Dalian University of Technology, Dalian 116024, China.
| | - Yang Fu
- School of Foreign Languages, Hangzhou City University, Hangzhou 310015, China; Hangzhou Collaborative Innovation Institute of Language Services, Hangzhou 310015, China; School of International Studies, Zhejiang University, Hangzhou 310058, China.
| | - Wenyu Liu
- School of Foreign Languages, Dalian University of Technology, Dalian 116024, China
| |
Collapse
|
2
|
Fernandes JVMR, de Alexandria AR, Marques JAL, de Assis DF, Motta PC, Silva BRDS. Emotion Detection from EEG Signals Using Machine Deep Learning Models. Bioengineering (Basel) 2024; 11:782. [PMID: 39199740 PMCID: PMC11351761 DOI: 10.3390/bioengineering11080782] [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: 07/15/2024] [Revised: 07/19/2024] [Accepted: 07/23/2024] [Indexed: 09/01/2024] Open
Abstract
Detecting emotions is a growing field aiming to comprehend and interpret human emotions from various data sources, including text, voice, and physiological signals. Electroencephalogram (EEG) is a unique and promising approach among these sources. EEG is a non-invasive monitoring technique that records the brain's electrical activity through electrodes placed on the scalp's surface. It is used in clinical and research contexts to explore how the human brain responds to emotions and cognitive stimuli. Recently, its use has gained interest in real-time emotion detection, offering a direct approach independent of facial expressions or voice. This is particularly useful in resource-limited scenarios, such as brain-computer interfaces supporting mental health. The objective of this work is to evaluate the classification of emotions (positive, negative, and neutral) in EEG signals using machine learning and deep learning, focusing on Graph Convolutional Neural Networks (GCNN), based on the analysis of critical attributes of the EEG signal (Differential Entropy (DE), Power Spectral Density (PSD), Differential Asymmetry (DASM), Rational Asymmetry (RASM), Asymmetry (ASM), Differential Causality (DCAU)). The electroencephalography dataset used in the research was the public SEED dataset (SJTU Emotion EEG Dataset), obtained through auditory and visual stimuli in segments from Chinese emotional movies. The experiment employed to evaluate the model results was "subject-dependent". In this method, the Deep Neural Network (DNN) achieved an accuracy of 86.08%, surpassing SVM, albeit with significant processing time due to the optimization characteristics inherent to the algorithm. The GCNN algorithm achieved an average accuracy of 89.97% in the subject-dependent experiment. This work contributes to emotion detection in EEG, emphasizing the effectiveness of different models and underscoring the importance of selecting appropriate features and the ethical use of these technologies in practical applications. The GCNN emerges as the most promising methodology for future research.
Collapse
Affiliation(s)
| | - Auzuir Ripardo de Alexandria
- Programa de Pós-Graduação em Engenharia de Telecomunicações, Instituto Federal do Ceará (IFCE), Fortaleza 60040-215, Brazil;
| | | | - Débora Ferreira de Assis
- Programa de Pós-Graduação em Engenharia de Teleinformática, Universidade Federal do Ceará, Fortaleza 60455-760, Brazil; (D.F.d.A.); (B.R.d.S.S.)
| | - Pedro Crosara Motta
- Programa de Pós-Graduação em Engenharia Biomédica, Universidade Federal do Rio de Janeiro, Rio de Janeiro 21941-598, Brazil;
| | - Bruno Riccelli dos Santos Silva
- Programa de Pós-Graduação em Engenharia de Teleinformática, Universidade Federal do Ceará, Fortaleza 60455-760, Brazil; (D.F.d.A.); (B.R.d.S.S.)
| |
Collapse
|
3
|
Hamzah HA, Abdalla KK. EEG-based emotion recognition systems; comprehensive study. Heliyon 2024; 10:e31485. [PMID: 38818173 PMCID: PMC11137547 DOI: 10.1016/j.heliyon.2024.e31485] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2024] [Accepted: 05/16/2024] [Indexed: 06/01/2024] Open
Abstract
Emotion recognition technology through EEG signal analysis is currently a fundamental concept in artificial intelligence. This recognition has major practical implications in emotional health care, human-computer interaction, and so on. This paper provides a comprehensive study of different methods for extracting electroencephalography (EEG) features for emotion recognition from four different perspectives, including time domain features, frequency domain features, time-frequency features, and nonlinear features. We summarize the current pattern recognition methods adopted in most related works, and with the rapid development of deep learning (DL) attracting the attention of researchers in this field, we pay more attention to deep learning-based studies and analyse the characteristics, advantages, disadvantages, and applicable scenarios. Finally, the current challenges and future development directions in this field were summarized. This paper can help novice researchers in this field gain a systematic understanding of the current status of emotion recognition research based on EEG signals and provide ideas for subsequent related research.
Collapse
Affiliation(s)
- Hussein Ali Hamzah
- Electrical Engineering Department, College of Engineering, University of Babylon, Iraq
| | - Kasim K. Abdalla
- Electrical Engineering Department, College of Engineering, University of Babylon, Iraq
| |
Collapse
|
4
|
Lazarou E, Exarchos TP. Predicting stress levels using physiological data: Real-time stress prediction models utilizing wearable devices. AIMS Neurosci 2024; 11:76-102. [PMID: 38988886 PMCID: PMC11230864 DOI: 10.3934/neuroscience.2024006] [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: 12/27/2023] [Revised: 03/22/2024] [Accepted: 04/08/2024] [Indexed: 07/12/2024] Open
Abstract
Stress has emerged as a prominent and multifaceted health concern in contemporary society, manifesting detrimental effects on individuals' physical and mental health and well-being. The ability to accurately predict stress levels in real time holds significant promise for facilitating timely interventions and personalized stress management strategies. The increasing incidence of stress-related physical and mental health issues highlights the importance of thoroughly understanding stress prediction mechanisms. Given that stress is a contributing factor to a wide array of mental and physical health problems, objectively assessing stress is crucial for behavioral and physiological studies. While numerous studies have assessed stress levels in controlled environments, the objective evaluation of stress in everyday settings still needs to be explored, primarily due to contextual factors and limitations in self-report adherence. This short review explored the emerging field of real-time stress prediction, focusing on utilizing physiological data collected by wearable devices. Stress was examined from a comprehensive standpoint, acknowledging its effects on both physical and mental well-being. The review synthesized existing research on the development and application of stress prediction models, underscoring advancements, challenges, and future directions in this rapidly evolving domain. Emphasis was placed on examining and critically evaluating the existing research and literature on stress prediction, physiological data analysis, and wearable devices for stress monitoring. The synthesis of findings aimed to contribute to a better understanding of the potential of wearable technology in objectively assessing and predicting stress levels in real time, thereby informing the design of effective interventions and personalized stress management approaches.
Collapse
Affiliation(s)
| | - Themis P. Exarchos
- Bioinformatics and Human Electrophysiology Laboratory, Dept of Informatics, Ionian University, GR49132, Corfu, Greece
| |
Collapse
|
5
|
Wang K, Qiu S, Wei W, Yi W, He H, Xu M, Jung TP, Ming D. Investigating EEG-based cross-session and cross-task vigilance estimation in BCI systems. J Neural Eng 2023; 20:056001. [PMID: 37611567 DOI: 10.1088/1741-2552/acf345] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Accepted: 08/23/2023] [Indexed: 08/25/2023]
Abstract
Objective. The state of vigilance is crucial for effective performance in brain-computer interface (BCI) tasks, and therefore, it is essential to investigate vigilance levels in BCI tasks. Despite this, most studies have focused on vigilance levels in driving tasks rather than on BCI tasks, and the electroencephalogram (EEG) patterns of vigilance states in different BCI tasks remain unclear. This study aimed to identify similarities and differences in EEG patterns and performances of vigilance estimation in different BCI tasks and sessions.Approach.To achieve this, we built a steady-state visual evoked potential-based BCI system and a rapid serial visual presentation-based BCI system and recruited 18 participants to carry out four BCI experimental sessions over four days.Main results. Our findings demonstrate that specific neural patterns for high and low vigilance levels are relatively stable across sessions. Differential entropy features significantly differ between different vigilance levels in all frequency bands and between BCI tasks in the delta and theta frequency bands, with the theta frequency band features playing a critical role in vigilance estimation. Additionally, prefrontal, temporal, and occipital regions are more relevant to the vigilance state in BCI tasks. Our results suggest that cross-session vigilance estimation is more accurate than cross-task estimation.Significance.Our study clarifies the underlying mechanisms of vigilance state in two BCI tasks and provides a foundation for further research in vigilance estimation in BCI applications.
Collapse
Affiliation(s)
- Kangning Wang
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, People's Republic of China
- Laboratory of Brain Atlas and Brain-Inspired Intelligence, State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, People's Republic of China
| | - Shuang Qiu
- Laboratory of Brain Atlas and Brain-Inspired Intelligence, State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, People's Republic of China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, People's Republic of China
| | - Wei Wei
- Laboratory of Brain Atlas and Brain-Inspired Intelligence, State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, People's Republic of China
| | - Weibo Yi
- Beijing Machine and Equipment Institute, Beijing, People's Republic of China
| | - Huiguang He
- Laboratory of Brain Atlas and Brain-Inspired Intelligence, State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, People's Republic of China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, People's Republic of China
| | - Minpeng Xu
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, People's Republic of China
- College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, People's Republic of China
| | - Tzyy-Ping Jung
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, People's Republic of China
- College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, People's Republic of China
- Swartz Center for Computational Neuroscience, University of California, San Diego, CA, United States of America
| | - Dong Ming
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, People's Republic of China
- College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, People's Republic of China
| |
Collapse
|
6
|
Zuo X, Zhang C, Hämäläinen T, Gao H, Fu Y, Cong F. Cross-Subject Emotion Recognition Using Fused Entropy Features of EEG. ENTROPY (BASEL, SWITZERLAND) 2022; 24:1281. [PMID: 36141167 PMCID: PMC9497745 DOI: 10.3390/e24091281] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Revised: 09/04/2022] [Accepted: 09/05/2022] [Indexed: 06/16/2023]
Abstract
Emotion recognition based on electroencephalography (EEG) has attracted high interest in fields such as health care, user experience evaluation, and human-computer interaction (HCI), as it plays an important role in human daily life. Although various approaches have been proposed to detect emotion states in previous studies, there is still a need to further study the dynamic changes of EEG in different emotions to detect emotion states accurately. Entropy-based features have been proved to be effective in mining the complexity information in EEG in many areas. However, different entropy features vary in revealing the implicit information of EEG. To improve system reliability, in this paper, we propose a framework for EEG-based cross-subject emotion recognition using fused entropy features and a Bidirectional Long Short-term Memory (BiLSTM) network. Features including approximate entropy (AE), fuzzy entropy (FE), Rényi entropy (RE), differential entropy (DE), and multi-scale entropy (MSE) are first calculated to study dynamic emotional information. Then, we train a BiLSTM classifier with the inputs of entropy features to identify different emotions. Our results show that MSE of EEG is more efficient than other single-entropy features in recognizing emotions. The performance of BiLSTM is further improved with an accuracy of 70.05% using fused entropy features compared with that of single-type feature.
Collapse
Affiliation(s)
- Xin Zuo
- School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian 116024, China
- Faculty of Information Technology, University of Jyväskylä, 40014 Jyväskylä, Finland
| | - Chi Zhang
- School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian 116024, China
- Liaoning Key Laboratory of Integrated Circuit and Biomedical Electronic System, Dalian 116024, China
| | - Timo Hämäläinen
- Faculty of Information Technology, University of Jyväskylä, 40014 Jyväskylä, Finland
| | - Hanbing Gao
- School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian 116024, China
| | - Yu Fu
- School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian 116024, China
| | - Fengyu Cong
- School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian 116024, China
- Faculty of Information Technology, University of Jyväskylä, 40014 Jyväskylä, Finland
| |
Collapse
|