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Zhang X, Cheng X, Liu H. TPRO-NET: an EEG-based emotion recognition method reflecting subtle changes in emotion. Sci Rep 2024; 14:13491. [PMID: 38866813 PMCID: PMC11169376 DOI: 10.1038/s41598-024-62990-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2024] [Accepted: 05/23/2024] [Indexed: 06/14/2024] Open
Abstract
Emotion recognition based on Electroencephalogram (EEG) has been applied in various fields, including human-computer interaction and healthcare. However, for the popular Valence-Arousal-Dominance emotion model, researchers often classify the dimensions into high and low categories, which cannot reflect subtle changes in emotion. Furthermore, there are issues with the design of EEG features and the efficiency of transformer. To address these issues, we have designed TPRO-NET, a neural network that takes differential entropy and enhanced differential entropy features as input and outputs emotion categories through convolutional layers and improved transformer encoders. For our experiments, we categorized the emotions in the DEAP dataset into 8 classes and those in the DREAMER dataset into 5 classes. On the DEAP and the DREAMER datasets, TPRO-NET achieved average accuracy rates of 97.63%/97.47%/97.88% and 98.18%/98.37%/98.40%, respectively, on the Valence/Arousal/Dominance dimension for the subject-dependent experiments. Compared to other advanced methods, TPRO-NET demonstrates superior performance.
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Affiliation(s)
- Xinyi Zhang
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230026, China
- Suzhou Institute of Biomedical Engineering and Technology, China Academy of Science, Suzhou, 215163, China
| | - Xiankai Cheng
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230026, China.
- Suzhou Institute of Biomedical Engineering and Technology, China Academy of Science, Suzhou, 215163, China.
| | - Hui Liu
- Cognitive Systems Lab, University of Bremen, Bremen, Germany.
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2
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Ma W, Zheng Y, Li T, Li Z, Li Y, Wang L. A comprehensive review of deep learning in EEG-based emotion recognition: classifications, trends, and practical implications. PeerJ Comput Sci 2024; 10:e2065. [PMID: 38855206 PMCID: PMC11157589 DOI: 10.7717/peerj-cs.2065] [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: 11/01/2023] [Accepted: 04/25/2024] [Indexed: 06/11/2024]
Abstract
Emotion recognition utilizing EEG signals has emerged as a pivotal component of human-computer interaction. In recent years, with the relentless advancement of deep learning techniques, using deep learning for analyzing EEG signals has assumed a prominent role in emotion recognition. Applying deep learning in the context of EEG-based emotion recognition carries profound practical implications. Although many model approaches and some review articles have scrutinized this domain, they have yet to undergo a comprehensive and precise classification and summarization process. The existing classifications are somewhat coarse, with insufficient attention given to the potential applications within this domain. Therefore, this article systematically classifies recent developments in EEG-based emotion recognition, providing researchers with a lucid understanding of this field's various trajectories and methodologies. Additionally, it elucidates why distinct directions necessitate distinct modeling approaches. In conclusion, this article synthesizes and dissects the practical significance of EEG signals in emotion recognition, emphasizing its promising avenues for future application.
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Affiliation(s)
- Weizhi Ma
- School of Information Science and Technology, North China University of Technology, Beijing, China
| | - Yujia Zheng
- School of Information Science and Technology, North China University of Technology, Beijing, China
| | - Tianhao Li
- School of Information Science and Technology, North China University of Technology, Beijing, China
| | - Zhengping Li
- School of Information Science and Technology, North China University of Technology, Beijing, China
| | - Ying Li
- School of Information Science and Technology, North China University of Technology, Beijing, China
| | - Lijun Wang
- School of Information Science and Technology, North China University of Technology, Beijing, China
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3
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Holguin-Garcia SA, Guevara-Navarro E, Daza-Chica AE, Patiño-Claro MA, Arteaga-Arteaga HB, Ruz GA, Tabares-Soto R, Bravo-Ortiz MA. A comparative study of CNN-capsule-net, CNN-transformer encoder, and Traditional machine learning algorithms to classify epileptic seizure. BMC Med Inform Decis Mak 2024; 24:60. [PMID: 38429718 PMCID: PMC10908140 DOI: 10.1186/s12911-024-02460-z] [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: 08/09/2023] [Accepted: 02/13/2024] [Indexed: 03/03/2024] Open
Abstract
INTRODUCTION Epilepsy is a disease characterized by an excessive discharge in neurons generally provoked without any external stimulus, known as convulsions. About 2 million people are diagnosed each year in the world. This process is carried out by a neurological doctor using an electroencephalogram (EEG), which is lengthy. METHOD To optimize these processes and make them more efficient, we have resorted to innovative artificial intelligence methods essential in classifying EEG signals. For this, comparing traditional models, such as machine learning or deep learning, with cutting-edge models, in this case, using Capsule-Net architectures and Transformer Encoder, has a crucial role in finding the most accurate model and helping the doctor to have a faster diagnosis. RESULT In this paper, a comparison was made between different models for binary and multiclass classification of the epileptic seizure detection database, achieving a binary accuracy of 99.92% with the Capsule-Net model and a multiclass accuracy with the Transformer Encoder model of 87.30%. CONCLUSION Artificial intelligence is essential in diagnosing pathology. The comparison between models is helpful as it helps to discard those that are not efficient. State-of-the-art models overshadow conventional models, but data processing also plays an essential role in evaluating the higher accuracy of the models.
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Affiliation(s)
| | - Ernesto Guevara-Navarro
- Departamento de Electrónica y Automatización, Universidad Autónoma de Manizales, Manizales, 170001, Caldas, Colombia
| | - Alvaro Eduardo Daza-Chica
- Departamento de Electrónica y Automatización, Universidad Autónoma de Manizales, Manizales, 170001, Caldas, Colombia
| | - Maria Alejandra Patiño-Claro
- Departamento de Electrónica y Automatización, Universidad Autónoma de Manizales, Manizales, 170001, Caldas, Colombia
| | - Harold Brayan Arteaga-Arteaga
- Departamento de Electrónica y Automatización, Universidad Autónoma de Manizales, Manizales, 170001, Caldas, Colombia
| | - Gonzalo A Ruz
- Facultad de Ingeniería y Ciencias, Universidad Adolfo Ibáñez, Santiago, 7941169, Chile
- Center of Applied Ecology and Sustainability (CAPES), Santiago, 8331150, Chile
- Data Observatory Foundation, Santiago, 7510277, Chile
| | - Reinel Tabares-Soto
- Departamento de Electrónica y Automatización, Universidad Autónoma de Manizales, Manizales, 170001, Caldas, Colombia
- Departamento de Sistemas e Informática, Universidad de Caldas, Manizales, 170004, Caldas, Colombia
- Facultad de Ingeniería y Ciencias, Universidad Adolfo Ibáñez, Santiago, 7941169, Chile
| | - Mario Alejandro Bravo-Ortiz
- Departamento de Electrónica y Automatización, Universidad Autónoma de Manizales, Manizales, 170001, Caldas, Colombia.
- Centro de Bioinformática y Biología Computacional (BIOS), Manizales, 170001, Colombia.
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Chen Z, Yang R, Huang M, Li F, Lu G, Wang Z. EEGProgress: A fast and lightweight progressive convolution architecture for EEG classification. Comput Biol Med 2024; 169:107901. [PMID: 38159400 DOI: 10.1016/j.compbiomed.2023.107901] [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: 09/08/2023] [Revised: 12/11/2023] [Accepted: 12/23/2023] [Indexed: 01/03/2024]
Abstract
Because of the intricate topological structure and connection of the human brain, extracting deep spatial features from electroencephalograph (EEG) signals is a challenging and time-consuming task. The extraction of topological spatial information plays a crucial role in EEG classification, and the architecture of the spatial convolution greatly affects the performance and complexity of convolutional neural network (CNN) based EEG classification models. In this study, a progressive convolution CNN architecture named EEGProgress is proposed, aiming to efficiently extract the topological spatial information of EEG signals from multi-scale levels (electrode, brain region, hemisphere, global) with superior speed. To achieve this, the raw EEG data is permuted using the empirical topological permutation rule, integrating the EEG data with numerous topological properties. Subsequently, the spatial features are extracted by a progressive feature extractor including prior, electrode, region, and hemisphere convolution blocks, progressively extracting the deep spatial features with reduced parameters and speed. Finally, the comparison and ablation experiments under both cross-subject and within-subject scenarios are conducted on a public dataset to verify the performance of the proposed EEGProgress and the effectiveness of the topological permutation. The results demonstrate the superior feature extraction ability of the proposed EEGProgress, with an average increase of 4.02% compared to other CNN-based EEG classification models under both cross-subject and within-subject scenarios. Furthermore, with the obtained average testing time, FLOPs, and parameters, the proposed EEGProgress outperforms other comparison models in terms of model complexity.
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Affiliation(s)
- Zhige Chen
- School of Advanced Technology, Xi'an Jiaotong-Liverpool University, Suzhou 215123, China; School of Electrical Engineering, Electronics and Computer Science, University of Liverpool, Liverpool L69 3BX, United Kingdom
| | - Rui Yang
- School of Advanced Technology, Xi'an Jiaotong-Liverpool University, Suzhou 215123, China
| | - Mengjie Huang
- Design School, Xi'an Jiaotong-Liverpool University, Suzhou 215123, China.
| | - Fumin Li
- School of Advanced Technology, Xi'an Jiaotong-Liverpool University, Suzhou 215123, China; School of Electrical Engineering, Electronics and Computer Science, University of Liverpool, Liverpool L69 3BX, United Kingdom
| | - Guoping Lu
- School of Electrical Engineering, Nantong University, Nantong 226019, China
| | - Zidong Wang
- Department of Computer Science, Brunel University London, Uxbridge, Middlesex UB8 3PH, United Kingdom
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Liu R, Chao Y, Ma X, Sha X, Sun L, Li S, Chang S. ERTNet: an interpretable transformer-based framework for EEG emotion recognition. Front Neurosci 2024; 18:1320645. [PMID: 38298914 PMCID: PMC10827927 DOI: 10.3389/fnins.2024.1320645] [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: 10/12/2023] [Accepted: 01/02/2024] [Indexed: 02/02/2024] Open
Abstract
Background Emotion recognition using EEG signals enables clinicians to assess patients' emotional states with precision and immediacy. However, the complexity of EEG signal data poses challenges for traditional recognition methods. Deep learning techniques effectively capture the nuanced emotional cues within these signals by leveraging extensive data. Nonetheless, most deep learning techniques lack interpretability while maintaining accuracy. Methods We developed an interpretable end-to-end EEG emotion recognition framework rooted in the hybrid CNN and transformer architecture. Specifically, temporal convolution isolates salient information from EEG signals while filtering out potential high-frequency noise. Spatial convolution discerns the topological connections between channels. Subsequently, the transformer module processes the feature maps to integrate high-level spatiotemporal features, enabling the identification of the prevailing emotional state. Results Experiments' results demonstrated that our model excels in diverse emotion classification, achieving an accuracy of 74.23% ± 2.59% on the dimensional model (DEAP) and 67.17% ± 1.70% on the discrete model (SEED-V). These results surpass the performances of both CNN and LSTM-based counterparts. Through interpretive analysis, we ascertained that the beta and gamma bands in the EEG signals exert the most significant impact on emotion recognition performance. Notably, our model can independently tailor a Gaussian-like convolution kernel, effectively filtering high-frequency noise from the input EEG data. Discussion Given its robust performance and interpretative capabilities, our proposed framework is a promising tool for EEG-driven emotion brain-computer interface.
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Affiliation(s)
- Ruixiang Liu
- School of Intelligent Medicine, China Medical University, Shenyang, Liaoning, China
| | - Yihu Chao
- School of Intelligent Medicine, China Medical University, Shenyang, Liaoning, China
| | - Xuerui Ma
- School of Intelligent Medicine, China Medical University, Shenyang, Liaoning, China
| | - Xianzheng Sha
- School of Intelligent Medicine, China Medical University, Shenyang, Liaoning, China
| | - Limin Sun
- Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai, China
| | - Shuo Li
- School of Life Sciences, China Medical University, Shenyang, Liaoning, China
| | - Shijie Chang
- School of Intelligent Medicine, China Medical University, Shenyang, Liaoning, China
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Lin K, Zhang L, Cai J, Sun J, Cui W, Liu G. DSE-Mixer: A pure multilayer perceptron network for emotion recognition from EEG feature maps. J Neurosci Methods 2024; 401:110008. [PMID: 37967671 DOI: 10.1016/j.jneumeth.2023.110008] [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: 05/21/2023] [Revised: 09/20/2023] [Accepted: 11/09/2023] [Indexed: 11/17/2023]
Abstract
BACKGROUND Decoding emotions from brain maps is a challenging task. Convolutional Neural Network (CNN) is commonly used for EEG feature map. However, due to its local bias, CNN is unable to efficiently utilize the global spatial information of EEG signals which limits the accuracy of emotion recognition. NEW METHODS We design the Dual-scal EEG-Mixer(DSE-Mixer) model for EEG feature map processing. Its brain region mixer layer and electrode mixer layer are designed to fuse EEG information at different spatial scales. For each mixer layer, the structure of alternating mixing of rows and columns of the input table enables cross-regional and cross-Mchannel communication of EEG information. In addition, a channel attention mechanism is introduced to adaptively learn the importance of each channel. RESULTS On the DEAP dataset, the DSE-Mixer model achieved a binary classification accuracy of 95.19% for arousal and 95.22% for valence. For the four-class classification across valence and arousal, the accuracies were HVHA: 92.12%, HVLA: 89.77%, LVLA: 93.35%, and LVHA: 92.63%. On the SEED dataset, the average recognition accuracy for the three emotions (positive, negative, and neutral) is 93.69%. COMPARISON WITH EXISTING METHODS In the emotion recognition research based on the DEAP and SEED datasets, DSE-Mixer achieved a high ranking performance. Compared to the two commonly used model in computer vision field, CNN and Vision Transformer(VIT), DSE-Mixer achieved significantly higher classification accuracy while requiring much less computational complexity. CONCLUSIONS DSE-Mixer provides a novel brain map processing model with a small size, demonstrating outstanding performance in emotion recognition.
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Affiliation(s)
- Kai Lin
- Colleage of Instrumentation and Electrical Engineering, Jilin University, Changchun, 130000, Jilin, China.
| | - Linhang Zhang
- Colleage of Instrumentation and Electrical Engineering, Jilin University, Changchun, 130000, Jilin, China.
| | - Jing Cai
- Colleage of Instrumentation and Electrical Engineering, Jilin University, Changchun, 130000, Jilin, China.
| | - Jiaqi Sun
- Colleage of Instrumentation and Electrical Engineering, Jilin University, Changchun, 130000, Jilin, China.
| | - Wenjie Cui
- Colleage of Instrumentation and Electrical Engineering, Jilin University, Changchun, 130000, Jilin, China.
| | - Guangda Liu
- Colleage of Instrumentation and Electrical Engineering, Jilin University, Changchun, 130000, Jilin, China.
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Jafari M, Shoeibi A, Khodatars M, Bagherzadeh S, Shalbaf A, García DL, Gorriz JM, Acharya UR. Emotion recognition in EEG signals using deep learning methods: A review. Comput Biol Med 2023; 165:107450. [PMID: 37708717 DOI: 10.1016/j.compbiomed.2023.107450] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Revised: 08/03/2023] [Accepted: 09/01/2023] [Indexed: 09/16/2023]
Abstract
Emotions are a critical aspect of daily life and serve a crucial role in human decision-making, planning, reasoning, and other mental states. As a result, they are considered a significant factor in human interactions. Human emotions can be identified through various sources, such as facial expressions, speech, behavior (gesture/position), or physiological signals. The use of physiological signals can enhance the objectivity and reliability of emotion detection. Compared with peripheral physiological signals, electroencephalogram (EEG) recordings are directly generated by the central nervous system and are closely related to human emotions. EEG signals have the great spatial resolution that facilitates the evaluation of brain functions, making them a popular modality in emotion recognition studies. Emotion recognition using EEG signals presents several challenges, including signal variability due to electrode positioning, individual differences in signal morphology, and lack of a universal standard for EEG signal processing. Moreover, identifying the appropriate features for emotion recognition from EEG data requires further research. Finally, there is a need to develop more robust artificial intelligence (AI) including conventional machine learning (ML) and deep learning (DL) methods to handle the complex and diverse EEG signals associated with emotional states. This paper examines the application of DL techniques in emotion recognition from EEG signals and provides a detailed discussion of relevant articles. The paper explores the significant challenges in emotion recognition using EEG signals, highlights the potential of DL techniques in addressing these challenges, and suggests the scope for future research in emotion recognition using DL techniques. The paper concludes with a summary of its findings.
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Affiliation(s)
- Mahboobeh Jafari
- Data Science and Computational Intelligence Institute, University of Granada, Spain
| | - Afshin Shoeibi
- Data Science and Computational Intelligence Institute, University of Granada, Spain.
| | - Marjane Khodatars
- Data Science and Computational Intelligence Institute, University of Granada, Spain
| | - Sara Bagherzadeh
- Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | - Ahmad Shalbaf
- Department of Biomedical Engineering and Medical Physics, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - David López García
- Data Science and Computational Intelligence Institute, University of Granada, Spain
| | - Juan M Gorriz
- Data Science and Computational Intelligence Institute, University of Granada, Spain; Department of Psychiatry, University of Cambridge, UK
| | - U Rajendra Acharya
- School of Mathematics, Physics and Computing, University of Southern Queensland, Springfield, Australia
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8
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Gao C, Uchitomi H, Miyake Y. Cross-Sensory EEG Emotion Recognition with Filter Bank Riemannian Feature and Adversarial Domain Adaptation. Brain Sci 2023; 13:1326. [PMID: 37759927 PMCID: PMC10526196 DOI: 10.3390/brainsci13091326] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Revised: 09/04/2023] [Accepted: 09/13/2023] [Indexed: 09/29/2023] Open
Abstract
Emotion recognition is crucial in understanding human affective states with various applications. Electroencephalography (EEG)-a non-invasive neuroimaging technique that captures brain activity-has gained attention in emotion recognition. However, existing EEG-based emotion recognition systems are limited to specific sensory modalities, hindering their applicability. Our study innovates EEG emotion recognition, offering a comprehensive framework for overcoming sensory-focused limits and cross-sensory challenges. We collected cross-sensory emotion EEG data using multimodal emotion simulations (three sensory modalities: audio/visual/audio-visual with two emotion states: pleasure or unpleasure). The proposed framework-filter bank adversarial domain adaptation Riemann method (FBADR)-leverages filter bank techniques and Riemannian tangent space methods for feature extraction from cross-sensory EEG data. Compared with Riemannian methods, filter bank and adversarial domain adaptation could improve average accuracy by 13.68% and 8.36%, respectively. Comparative analysis of classification results proved that the proposed FBADR framework achieved a state-of-the-art cross-sensory emotion recognition performance and reached an average accuracy of 89.01% ± 5.06%. Moreover, the robustness of the proposed methods could ensure high cross-sensory recognition performance under a signal-to-noise ratio (SNR) ≥ 1 dB. Overall, our study contributes to the EEG-based emotion recognition field by providing a comprehensive framework that overcomes limitations of sensory-oriented approaches and successfully tackles the difficulties of cross-sensory situations.
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Affiliation(s)
- Chenguang Gao
- Department of Computer Science, Tokyo Institute of Technology, Yokohama 226-8502, Japan; (H.U.); (Y.M.)
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Qiu X, Wang S, Wang R, Zhang Y, Huang L. A multi-head residual connection GCN for EEG emotion recognition. Comput Biol Med 2023; 163:107126. [PMID: 37327757 DOI: 10.1016/j.compbiomed.2023.107126] [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: 12/07/2022] [Revised: 03/22/2023] [Accepted: 06/01/2023] [Indexed: 06/18/2023]
Abstract
Electroencephalography (EEG) emotion recognition is a crucial aspect of human-computer interaction. However, conventional neural networks have limitations in extracting profound EEG emotional features. This paper introduces a novel multi-head residual graph convolutional neural network (MRGCN) model that incorporates complex brain networks and graph convolution networks. The decomposition of multi-band differential entropy (DE) features exposes the temporal intricacy of emotion-linked brain activity, and the combination of short and long-distance brain networks can explore complex topological characteristics. Moreover, the residual-based architecture not only enhances performance but also augments classification stability across subjects. The visualization of brain network connectivity offers a practical technique for investigating emotional regulation mechanisms. The MRGCN model exhibits average classification accuracies of 95.8% and 98.9% for the DEAP and SEED datasets, respectively, highlighting its excellent performance and robustness.
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Affiliation(s)
- Xiangkai Qiu
- College of Electronic and Optical Engineering & College of Flexible Electronics, Nanjing University of Posts and Telecommunications, Nanjing, China
| | - Shenglin Wang
- College of Electronic and Optical Engineering & College of Flexible Electronics, Nanjing University of Posts and Telecommunications, Nanjing, China
| | - Ruqing Wang
- College of Electronic and Optical Engineering & College of Flexible Electronics, Nanjing University of Posts and Telecommunications, Nanjing, China
| | - Yiling Zhang
- College of Electronic and Optical Engineering & College of Flexible Electronics, Nanjing University of Posts and Telecommunications, Nanjing, China
| | - Liya Huang
- College of Electronic and Optical Engineering & College of Flexible Electronics, Nanjing University of Posts and Telecommunications, Nanjing, China; National and Local Joint Engineering Laboratory of RF Integration and Micro-Assembly Technology, Nanjing, China.
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Zong J, Xiong X, Zhou J, Ji Y, Zhou D, Zhang Q. FCAN-XGBoost: A Novel Hybrid Model for EEG Emotion Recognition. SENSORS (BASEL, SWITZERLAND) 2023; 23:5680. [PMID: 37420845 DOI: 10.3390/s23125680] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Revised: 06/03/2023] [Accepted: 06/15/2023] [Indexed: 07/09/2023]
Abstract
In recent years, artificial intelligence (AI) technology has promoted the development of electroencephalogram (EEG) emotion recognition. However, existing methods often overlook the computational cost of EEG emotion recognition, and there is still room for improvement in the accuracy of EEG emotion recognition. In this study, we propose a novel EEG emotion recognition algorithm called FCAN-XGBoost, which is a fusion of two algorithms, FCAN and XGBoost. The FCAN module is a feature attention network (FANet) that we have proposed for the first time, which processes the differential entropy (DE) and power spectral density (PSD) features extracted from the four frequency bands of the EEG signal and performs feature fusion and deep feature extraction. Finally, the deep features are fed into the eXtreme Gradient Boosting (XGBoost) algorithm to classify the four emotions. We evaluated the proposed method on the DEAP and DREAMER datasets and achieved a four-category emotion recognition accuracy of 95.26% and 94.05%, respectively. Additionally, our proposed method reduces the computational cost of EEG emotion recognition by at least 75.45% for computation time and 67.51% for memory occupation. The performance of FCAN-XGBoost outperforms the state-of-the-art four-category model and reduces computational costs without losing classification performance compared with other models.
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Affiliation(s)
- Jing Zong
- Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China
| | - Xin Xiong
- Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China
| | - Jianhua Zhou
- Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China
| | - Ying Ji
- Graduate School, Kunming Medical University, Kunming 650500, China
| | - Diao Zhou
- Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China
| | - Qi Zhang
- Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China
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