A novel dynamic brain network in arousal for brain states and emotion analysis

Gao, Yunhe; Cao, Zhang; Liu, Jia; Zhang, Jianhai

doi:10.3934/mbe.2021368

Cited by 8 publications

(12 citation statements)

References 40 publications

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“…This indicates frontal, temporal, and parietal areas of the brain provide more emotion-related information. This is consistent with previous literatures [47] [48] [49]. Emotion arousal is mostly reflected in the frontal lobe, temporal lobe, and parietal lobe [47].…”

Section: Interpretabilitysupporting

confidence: 93%

“…This is consistent with previous literatures [47] [48] [49]. Emotion arousal is mostly reflected in the frontal lobe, temporal lobe, and parietal lobe [47]. Pre-frontal and temporal asymmetry have close relations to arousal recognition [48].…”

Section: Interpretabilitysupporting

confidence: 93%

“…However, the saliency maps of the subjects with high F1 scores in Fig. 5(b) and (e) only show strong activation in the frontal, temporal, and parietal areas, which is consistent with [1] [45] [47] [48] [50] [51] [53]. A right hemisphere lateralization pattern is also observed in the averaged saliency map of the top 10% subjects with high F1 scores (Fig.…”

Section: Interpretabilitymentioning

confidence: 51%

“…[47] [48] [50][51], which indicates the network learns from the proper region. And for both arousal and valence, the occipital activities provide certain information.…”

mentioning

confidence: 99%

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TSception: Capturing Temporal Dynamics and Spatial Asymmetry From EEG for Emotion Recognition

Ding

Robinson

Zhang

et al. 2023

IEEE Trans. Affective Comput.

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The high temporal resolution and the asymmetric spatial activations are essential attributes of electroencephalogram (EEG) underlying emotional processes in the brain. To learn the temporal dynamics and spatial asymmetry of EEG towards accurate and generalized emotion recognition, we propose TSception, a multi-scale convolutional neural network that can classify emotions from EEG. TSception consists of dynamic temporal, asymmetric spatial, and high-level fusion layers, which learn discriminative representations in the time and channel dimensions simultaneously. The dynamic temporal layer consists of multi-scale 1D convolutional kernels whose lengths are related to the sampling rate of EEG, which learns the dynamic temporal and frequency representations of EEG. The asymmetric spatial layer takes advantage of the asymmetric EEG patterns for emotion, learning the discriminative global and hemisphere representations. The learned spatial representations will be fused by a high-level fusion layer. Using more generalized cross-validation settings, the proposed method is evaluated on two publicly available datasets DEAP and MAHNOB-HCI. The performance of the proposed network is compared with prior reported methods such as SVM, KNN, FBFgMDM, FBTSC, Unsupervised learning, DeepConvNet, ShallowConvNet, and EEGNet. TSception achieves higher classification accuracies and F1 scores than other methods in most of the experiments. The codes are available at:https://github.com/yi-ding-cs/TSception

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Section: Interpretabilitysupporting

confidence: 93%

Section: Interpretabilitysupporting

confidence: 93%

Section: Interpretabilitymentioning

confidence: 51%

“…[47] [48] [50][51], which indicates the network learns from the proper region. And for both arousal and valence, the occipital activities provide certain information.…”

mentioning

confidence: 99%

See 2 more Smart Citations

TSception: Capturing Temporal Dynamics and Spatial Asymmetry From EEG for Emotion Recognition

Ding

Robinson

Zhang

et al. 2023

IEEE Trans. Affective Comput.

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“…EEG signals are the overall response of the electrophysiological activity of human brain nerve cells in the cerebral cortex or scalp surface [ 10 ]. Clinical studies have shown that EEG signals contain a lot of physiological and pathological information [ 11 ], and different mental states will affect the changes of EEGs, which provides an important basis for the diagnosis of depression and other brain diseases. Campisi, P. et al [ 12 ] pointed out that the brain regions of patients with depression have changed, which reduces the decision-making ability of patients with depression.…”

Section: Introductionmentioning

confidence: 99%

A Depression Diagnosis Method Based on the Hybrid Neural Network and Attention Mechanism

Wang

Liu

et al. 2022

Brain Sciences

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Depression is a common but easily misdiagnosed disease when using a self-assessment scale. Electroencephalograms (EEGs) provide an important reference and objective basis for the identification and diagnosis of depression. In order to improve the accuracy of the diagnosis of depression by using mainstream algorithms, a high-performance hybrid neural network depression detection method is proposed in this paper combined with deep learning technology. Firstly, a concatenating one-dimensional convolutional neural network (1D-CNN) and gated recurrent unit (GRU) are employed to extract the local features and to determine the global features of the EEG signal. Secondly, the attention mechanism is introduced to form the hybrid neural network. The attention mechanism assigns different weights to the multi-dimensional features extracted by the network, so as to screen out more representative features, which can reduce the computational complexity of the network and save the training time of the model while ensuring high precision. Moreover, dropout is applied to accelerate network training and address the over-fitting problem. Experiments reveal that the 1D-CNN-GRU-ATTN model has more effectiveness and a better generalization ability compared with traditional algorithms. The accuracy of the proposed method in this paper reaches 99.33% in a public dataset and 97.98% in a private dataset, respectively.

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CMLP-Net: A convolution-multilayer perceptron network for EEG-based emotion recognition

Lu,

Gu,

et al. 2024

Biomedical Signal Processing and Control

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A novel dynamic brain network in arousal for brain states and emotion analysis

Cited by 8 publications

References 40 publications

TSception: Capturing Temporal Dynamics and Spatial Asymmetry From EEG for Emotion Recognition

TSception: Capturing Temporal Dynamics and Spatial Asymmetry From EEG for Emotion Recognition

A Depression Diagnosis Method Based on the Hybrid Neural Network and Attention Mechanism

CMLP-Net: A convolution-multilayer perceptron network for EEG-based emotion recognition

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