Spatial–temporal features-based EEG emotion recognition using graph convolution network and long short-term memory

Zheng, Fa; Hu, Bin; Zheng, Xiangwei; Zhang, Yuang

doi:10.1088/1361-6579/acd675

Cited by 6 publications

(3 citation statements)

References 46 publications

(36 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Finally, the undirected graph was fed into this model. Zheng et al [ 21 ] constructed an EEG electrode location matrix corresponding to brain region distribution, thereby reconstructing EEG data. They used a combined model of a graph convolutional neural network and LSTM (GCN + LSTM) to extract the spatial and temporal features of the EEG signals.…”

Section: Resultsmentioning

confidence: 99%

Emotion Classification Based on Transformer and CNN for EEG Spatial–Temporal Feature Learning

Yao,

Li,

Ding

et al. 2024

Brain Sciences

View full text Add to dashboard Cite

Objectives: The temporal and spatial information of electroencephalogram (EEG) signals is crucial for recognizing features in emotion classification models, but it excessively relies on manual feature extraction. The transformer model has the capability of performing automatic feature extraction; however, its potential has not been fully explored in the classification of emotion-related EEG signals. To address these challenges, the present study proposes a novel model based on transformer and convolutional neural networks (TCNN) for EEG spatial–temporal (EEG ST) feature learning to automatic emotion classification. Methods: The proposed EEG ST-TCNN model utilizes position encoding (PE) and multi-head attention to perceive channel positions and timing information in EEG signals. Two parallel transformer encoders in the model are used to extract spatial and temporal features from emotion-related EEG signals, and a CNN is used to aggregate the EEG’s spatial and temporal features, which are subsequently classified using Softmax. Results: The proposed EEG ST-TCNN model achieved an accuracy of 96.67% on the SEED dataset and accuracies of 95.73%, 96.95%, and 96.34% for the arousal–valence, arousal, and valence dimensions, respectively, for the DEAP dataset. Conclusions: The results demonstrate the effectiveness of the proposed ST-TCNN model, with superior performance in emotion classification compared to recent relevant studies. Significance: The proposed EEG ST-TCNN model has the potential to be used for EEG-based automatic emotion recognition.

show abstract

Section: Resultsmentioning

confidence: 99%

Emotion Classification Based on Transformer and CNN for EEG Spatial–Temporal Feature Learning

Yao,

Li,

Ding

et al. 2024

Brain Sciences

View full text Add to dashboard Cite

show abstract

“…For example, convolutional neural networks (CNNs) and RNNs can be combined for spatial and temporal feature extraction, respectively [20]. More recently, graph convolutional networks (GCNs) and long short-term memory network (LSTM) are adopted and shown superior performance [21].…”

Section: Manual Deep Learning For Eeg-based Emotion Recognitionmentioning

confidence: 99%

AutoEER: automatic EEG-based emotion recognition with neural architecture search

Liu

Zhang

et al. 2023

J. Neural Eng.

View full text Add to dashboard Cite

Objective. Emotion recognition based on electroencephalography (EEG) is garnering increasing attention among researchers due to its wideranging applications and the rise of portable devices. Deep learning-based models have demonstrated impressive progress in EEG-based emotion recognition, thanks to their exceptional feature extraction capabilities. However, the manual design of deep networks is time-consuming and labour-intensive. Moreover, the inherent variability of EEG signals necessitates extensive customization of models, exacerbating these challenges. Neural architecture search (NAS) methods can alleviate the need for excessive manual involvement by automatically discovering the optimal network structure for EEG-based emotion recognition.Approach. In this regard, we propose AutoEER (Automatic EEG-based Emotion Recognition), a framework that leverages tailored NAS to automatically discover the optimal network structure for EEG-based emotion recognition. We carefully design a customized search space specifically for EEG signals, incorporating operators that effectively capture both temporal and spatial properties of EEG.Additionally, we employ a novel parameterization strategy to derive the optimal network structure from the proposed search space. Main results. Extensive experimentation on emotion classification tasks using two benchmark datasets, DEAP and SEED, has demonstrated that AutoEER outperforms state-of-the-art (SOTA) manual deep and NAS models. Specifically, compared to the optimal model WangNAS on the accuracy (ACC) metric, AutoEER improves its average accuracy on all datasets by 0.93%. Similarly, compared to the optimal model LiNAS on the F1 Ssore (F1) metric, AutoEER improves its average F1 score on all datasets by 4.51%. Furthermore, the architectures generated by AutoEER exhibit superior transferability compared to alternative methods. Significance.AutoEER represents a novel approach to EEG analysis, utilizing a specialized search space to design models tailored to individual subjects. This approach significantly reduces the labour and time costs associated with manual model construction in EEG research, holding great promise for advancing the field and streamlining research practices.

show abstract

“…Electroencephalography (EEG), one of the most important modalities in emotion recognition, can directly measure physiological electrical activities of the central nervous system (Gunes and Schuller 2013). Its advantages include noninvasive sampling and high time-resolution (Zheng et al 2023). However, the complex spatial correlations (Wang et al 2022a) and significant individual differences (Li et al 2022) also present challenges for EEG feature learning.…”

Section: Introductionmentioning

confidence: 99%

Cerebral asymmetry representation learning-based deep subdomain adaptation network for electroencephalogram-based emotion recognition

Wang,

Wan

et al. 2024

Physiol. Meas.

View full text Add to dashboard Cite

Objective. Extracting discriminative spatial information from multiple electrodes is a crucial and challenging problem for Electroencephalogram (EEG)-based emotion recognition. Additionally, the domain shift caused by the individual differences would degrade the performance of cross-subject EEG classification. Approach. To deal with the problems, we propose the Cerebral Asymmetry Representation Learning based Deep Subdomain Adaptation Network (CARL-DSAN) to enhance the cross-subject EEG-based emotion recognition. Specifically, the CARL module is inspired by the neuroscience findings that asymmetrical activations of left and right brain hemispheres occur during cognitive and affective processes. In the CARL module, we introduce a novel two-step strategy for extracting discriminative features through intra-hemisphere spatial learning and asymmetry representation learning. Moreover, the transformer encoders within the CARL module could emphasize the contributive electrodes and electrode pairs. Subsequently, the DSAN module, known for its superior performance over global domain adaption, is adopted to mitigate domain shift and further improve the cross-subject performance by aligning relevant subdomains which share the same class samples. Main Results. To validate the effectiveness of the CARL-DSAN, we conduct the subject-independent experiments on the DEAP database, achieving the accuracies of 68.67% and 67.11% for arousal and valence classification respectively, and corresponding accuracies of 67.70% and 67.18% on the MAHNOB-HCI database. Significance. The results demonstrate that CARL-DSAN could achieve outstanding cross-subject performance in both arousal and valence classification.

show abstract

Spatial–temporal features-based EEG emotion recognition using graph convolution network and long short-term memory

Cited by 6 publications

References 46 publications

Emotion Classification Based on Transformer and CNN for EEG Spatial–Temporal Feature Learning

Emotion Classification Based on Transformer and CNN for EEG Spatial–Temporal Feature Learning

AutoEER: automatic EEG-based emotion recognition with neural architecture search

Cerebral asymmetry representation learning-based deep subdomain adaptation network for electroencephalogram-based emotion recognition

Contact Info

Product

Resources

About