A Customized ECA-CRNN Model for Emotion Recognition Based on EEG Signals

Song, Yan; Yin, Yue; Xu, Pengfei

doi:10.3390/electronics12132900

Cited by 5 publications

(5 citation statements)

References 41 publications

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“…Understanding and achieving desirable results in the field of emotion recognition remains a challenging task, presenting opportunities for further innovation and investigation. Although previous studies [7,28,30,32,34,38] have demonstrated satisfactory results, there are still critical issues yet to be addressed, such as robustness, generalizability, and handling of intra-subject variability and observation variations. To address these gaps, this work aims to develop a robust and flexible pipeline for emotion recognition that can be applied to real-world data, while also mitigating the impact of these challenges.…”

Section: Discussion and Future Workmentioning

confidence: 99%

“…Prior studies have demonstrated a strong correlation between EEG emotions and EEG frequencies [24,25]. Typically, EEG frequency features are extracted by mapping the EEG signals to Theta (4-7 Hz), Alpha (8-13 Hz), Beta (14-29 Hz), Gamma (30)(31)(32)(33)(34)(35)(36)(37)(38)(39)(40)(41)(42)(43)(44)(45)(46)(47), and other frequency bands [26]. Dimensionality reduction and classification are also essential stages in the emotion classification pipeline [27].…”

Section: Related Workmentioning

confidence: 99%

“…The method was validated on MAHNOB HCI and DEAP datasets. Song et al [34] developed a model that integrates efficient channel attention into a combination of CNN and gated circulation units for emotion recognition on the DEAP dataset. Wang et al [35] proposed a CNN model for cross-subject emotion recognition and selecting the channels with the highest accuracy as the feature vector, the model is validated on the SEED dataset.…”

Section: Related Workmentioning

confidence: 99%

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The Efficacy and Utility of Lower-Dimensional Riemannian Geometry for EEG-Based Emotion Classification

et al. 2023

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Electroencephalography (EEG) signals have diverse applications in brain-computer interfaces (BCIs), neurological condition diagnoses, and emotion recognition across healthcare, education, and entertainment domains. This paper presents a robust method that leverages Riemannian geometry to enhance the accuracy of EEG-based emotion classification. The proposed approach involves adaptive feature extraction using principal component analysis (PCA) in the Euclidean space to capture relevant signal characteristics and improve classification performance. Covariance matrices are derived from the extracted features and projected onto the Riemannian manifold. Emotion classification is performed using the minimum distance to Riemannian mean (MDRM) classifier. The effectiveness of the method was evaluated through experiments on four datasets, DEAP, DREAMER, MAHNOB, and SEED, demonstrating its generalizability and consistent accuracy improvement across different scenarios. The classification accuracy and robustness were compared with several state-of-the-art classification methods, which supports the validity and efficacy of using Riemannian geometry for enhancing the accuracy of EEG-based emotion classification.

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Section: Discussion and Future Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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The Efficacy and Utility of Lower-Dimensional Riemannian Geometry for EEG-Based Emotion Classification

et al. 2023

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“…ECA (Efficient Channel Attention) [29] is a channel attention module widely used in visual perception modeling. It can perform channel feature enhancement on multichannel input features [46], and the size of the feature map will not be altered after enhancement using ECA. Hence, the ECA module can enhance the model without increasing the complexity of modeling benefits.…”

Section: Eca Blockmentioning

confidence: 99%

MES-CTNet: A Novel Capsule Transformer Network Base on a Multi-Domain Feature Map for Electroencephalogram-Based Emotion Recognition

Du,

Ding,

et al. 2024

Brain Sciences

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Emotion recognition using the electroencephalogram (EEG) has garnered significant attention within the realm of human–computer interaction due to the wealth of genuine emotional data stored in EEG signals. However, traditional emotion recognition methods are deficient in mining the connection between multi-domain features and fitting their advantages. In this paper, we propose a novel capsule Transformer network based on a multi-domain feature for EEG-based emotion recognition, referred to as MES-CTNet. The model’s core consists of a multichannel capsule neural network(CapsNet) embedded with ECA (Efficient Channel Attention) and SE (Squeeze and Excitation) blocks and a Transformer-based temporal coding layer. Firstly, a multi-domain feature map is constructed by combining the space–frequency–time characteristics of the multi-domain features as inputs to the model. Then, the local emotion features are extracted from the multi-domain feature maps by the improved CapsNet. Finally, the Transformer-based temporal coding layer is utilized to globally perceive the emotion feature information of the continuous time slices to obtain a final emotion state. The paper fully experimented on two standard datasets with different emotion labels, the DEAP and SEED datasets. On the DEAP dataset, MES-CTNet achieved an average accuracy of 98.31% in the valence dimension and 98.28% in the arousal dimension; it achieved 94.91% for the cross-session task on the SEED dataset, demonstrating superior performance compared to traditional EEG emotion recognition methods. The MES-CTNet method, utilizing a multi-domain feature map as proposed herein, offers a broader observation perspective for EEG-based emotion recognition. It significantly enhances the classification recognition rate, thereby holding considerable theoretical and practical value in the EEG emotion recognition domain.

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“…Accordingly, researchers use EEG in various domains that involve neural engineering, neurosciences, and biomedical sciences (e.g., brain–computer interfaces, BCIs) [ 5 , 6 ]. EEG signal plays a crucial role in several EEG-based research and application areas such as clinical applications for epilepsy [ 7 ], depression [ 8 , 9 ], the effective monitoring of emotion [ 10 ], mental stress [ 11 , 12 , 13 ], and sinogram [ 14 ].…”

Section: Introductionmentioning

confidence: 99%

Mental Stress Classification Based on Selected Electroencephalography Channels Using Correlation Coefficient of Hjorth Parameters

Hag,

Al-Shargie,

Handayani

et al. 2023

Brain Sciences

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Electroencephalography (EEG) signals offer invaluable insights into diverse activities of the human brain, including the intricate physiological and psychological responses associated with mental stress. A major challenge, however, is accurately identifying mental stress while mitigating the limitations associated with a large number of EEG channels. Such limitations encompass computational complexity, potential overfitting, and the prolonged setup time for electrode placement, all of which can hinder practical applications. To address these challenges, this study presents the novel CCHP method, aimed at identifying and ranking commonly optimal EEG channels based on their sensitivity to the mental stress state. This method’s uniqueness lies in its ability not only to find common channels, but also to prioritize them according to their responsiveness to stress, ensuring consistency across subjects and making it potentially transformative for real-world applications. From our rigorous examinations, eight channels emerged as universally optimal in detecting stress variances across participants. Leveraging features from the time, frequency, and time–frequency domains of these channels, and employing machine learning algorithms, notably RLDA, SVM, and KNN, our approach achieved a remarkable accuracy of 81.56% with the SVM algorithm outperforming existing methodologies. The implications of this research are profound, offering a stepping stone toward the development of real-time stress detection devices, and consequently, enabling clinicians to make more informed therapeutic decisions based on comprehensive brain activity monitoring.

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A Customized ECA-CRNN Model for Emotion Recognition Based on EEG Signals

Cited by 5 publications

References 41 publications

The Efficacy and Utility of Lower-Dimensional Riemannian Geometry for EEG-Based Emotion Classification

The Efficacy and Utility of Lower-Dimensional Riemannian Geometry for EEG-Based Emotion Classification

MES-CTNet: A Novel Capsule Transformer Network Base on a Multi-Domain Feature Map for Electroencephalogram-Based Emotion Recognition

Mental Stress Classification Based on Selected Electroencephalography Channels Using Correlation Coefficient of Hjorth Parameters

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