Attention-based Spatio-Temporal Graphic LSTM for EEG Emotion Recognition

Li, Xiaoxu; Zheng, Wenming; Zong, Yuan; Chang, Hongli; Lu, Cheng

doi:10.1109/ijcnn52387.2021.9534443

Cited by 12 publications

(8 citation statements)

References 26 publications

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“…These methods can be generally categorised as learnable or pre-defined. Multiple/Combined graph definitions -- [47], [49], [53], [54], [57]- [59], [61]- [64], [67], [69], [72], [79], [79], [81], [82], [87], [92], [102] [51], [53], [55], [57], [71], [72], [75], [78], [81], [82], [87], [89], [90], [92], [93], [93], [95]- [99], [101], [102] Raw signal ✓ ✗ ✗…”

Section: Definition Of Brain Graph Structurementioning

confidence: 99%

Graph Neural Network-Based EEG Classification: A Survey

Klepl,

Wu,

2024

IEEE Trans. Neural Syst. Rehabil. Eng.

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Graph neural networks (GNN) are increasingly used to classify EEG for tasks such as emotion recognition, motor imagery and neurological diseases and disorders. A wide range of methods have been proposed to design GNN-based classifiers. Therefore, there is a need for a systematic review and categorisation of these approaches. We exhaustively search the published literature on this topic and derive several categories for comparison. These categories highlight the similarities and differences among the methods. The results suggest a prevalence of spectral graph convolutional layers over spatial. Additionally, we identify standard forms of node features, with the most popular being the raw EEG signal and differential entropy. Our results summarise the emerging trends in GNN-based approaches for EEG classification. Finally, we discuss several promising research directions, such as exploring the potential of transfer learning methods and appropriate modelling of cross-frequency interactions.

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Section: Definition Of Brain Graph Structurementioning

confidence: 99%

Graph Neural Network-Based EEG Classification: A Survey

Klepl,

Wu,

2024

IEEE Trans. Neural Syst. Rehabil. Eng.

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“…It consists of two kinds of attention: spatial attention, which focuses on the relevant regions or nodes in space; and temporal attention, which focuses on the time steps in time dimension. Sartipi et al proposed the novel spatial-temporal attention neural network (STANN) to extract discriminative spatial and temporal features of EEG signals by a parallel structure of the multi-column convolutional neural network and attention-based bidirectional long-short term memory (Sartipi et al, 2021). Li X. et al (2021 proposed a model called attention-based spatial=temporal graphic long shortterm memory (ASTG-LSTM), in which a specific spatial-temporal attention embedded into the model to improve the invariance ability against the emotional intensity fluctuation.…”

Section: Introductionmentioning

confidence: 99%

“…Sartipi et al proposed the novel spatial-temporal attention neural network (STANN) to extract discriminative spatial and temporal features of EEG signals by a parallel structure of the multi-column convolutional neural network and attention-based bidirectional long-short term memory (Sartipi et al, 2021 ). Li X. et al ( 2021 ) proposed a model called attention-based spatial=temporal graphic long short-term memory (ASTG-LSTM), in which a specific spatial-temporal attention embedded into the model to improve the invariance ability against the emotional intensity fluctuation. Liu et al ( 2022 ) proposed a spatial-temporal attention to explore the relationship between emotion and spatial-temporal EEG features.…”

Section: Introductionmentioning

confidence: 99%

STGATE: Spatial-temporal graph attention network with a transformer encoder for EEG-based emotion recognition

Pan

Huang

et al. 2023

Front. Hum. Neurosci.

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Electroencephalogram (EEG) is a crucial and widely utilized technique in neuroscience research. In this paper, we introduce a novel graph neural network called the spatial-temporal graph attention network with a transformer encoder (STGATE) to learn graph representations of emotion EEG signals and improve emotion recognition performance. In STGATE, a transformer-encoder is applied for capturing time-frequency features which are fed into a spatial-temporal graph attention for emotion classification. Using a dynamic adjacency matrix, the proposed STGATE adaptively learns intrinsic connections between different EEG channels. To evaluate the cross-subject emotion recognition performance, leave-one-subject-out experiments are carried out on three public emotion recognition datasets, i.e., SEED, SEED-IV, and DREAMER. The proposed STGATE model achieved a state-of-the-art EEG-based emotion recognition performance accuracy of 90.37% in SEED, 76.43% in SEED-IV, and 76.35% in DREAMER dataset, respectively. The experiments demonstrated the effectiveness of the proposed STGATE model for cross-subject EEG emotion recognition and its potential for graph-based neuroscience research.

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“…This capability has the potential to advance the diagnosis and treatment of emotional disorders such as depression [2], anxiety [3], and post-traumatic stress disorder (PTSD) [4]. Moreover, the EEG holds promise for diverse applications, including human-computer interaction [5], affective computing [6,7], marketing research, and entertainment. Consequently, the development of reliable and accurate EEG emotion recognition systems bears great significance for the scientific community and society at large.…”

Section: Introductionmentioning

confidence: 99%

“…(6) Multimodal emotion recognition combines EEG data with other modalities, such as facial expressions, speech, or physiological signals, to enhance the accuracy of EEG emotion recognition [15,16]. (7) Artificial Neural Networks (ANNs) are trained on the EEG signal to recognize different emotional states based on the extracted features [17]. (8) Deep learning techniques exhibit promising results in EEG-based emotion recognition, with ongoing exploration to develop models capable of capturing the intricate and dynamic patterns of brain activity associated with distinct emotional states [18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Subject-Independent EEG Emotion Recognition Based on Genetically Optimized Projection Dictionary Pair Learning

et al. 2023

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One of the primary challenges in Electroencephalogram (EEG) emotion recognition lies in developing models that can effectively generalize to new unseen subjects, considering the significant variability in EEG signals across individuals. To address the issue of subject-specific features, a suitable approach is to employ projection dictionary learning, which enables the identification of emotion-relevant features across different subjects. To accomplish the objective of pattern representation and discrimination for subject-independent EEG emotion recognition, we utilized the fast and efficient projection dictionary pair learning (PDPL) technique. PDPL involves the joint use of a synthesis dictionary and an analysis dictionary to enhance the representation of features. Additionally, to optimize the parameters of PDPL, which depend on experience, we applied the genetic algorithm (GA) to obtain the optimal solution for the model. We validated the effectiveness of our algorithm using leave-one-subject-out cross validation on three EEG emotion databases: SEED, MPED, and GAMEEMO. Our approach outperformed traditional machine learning methods, achieving an average accuracy of 69.89% on the SEED database, 24.11% on the MPED database, 64.34% for the two-class GAMEEMO, and 49.01% for the four-class GAMEEMO. These results highlight the potential of subject-independent EEG emotion recognition algorithms in the development of intelligent systems capable of recognizing and responding to human emotions in real-world scenarios.

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Attention-based Spatio-Temporal Graphic LSTM for EEG Emotion Recognition

Cited by 12 publications

References 26 publications

Graph Neural Network-Based EEG Classification: A Survey

Graph Neural Network-Based EEG Classification: A Survey

STGATE: Spatial-temporal graph attention network with a transformer encoder for EEG-based emotion recognition

Subject-Independent EEG Emotion Recognition Based on Genetically Optimized Projection Dictionary Pair Learning

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