Deep feature pyramid network for EEG emotion recognition

Hou, Fazheng; Gao, Qiang; Song, Yu; Wang, Zhe; Bai, Zhongli; Yang, Yi; Tian, Zekun

doi:10.1016/j.measurement.2022.111724

Cited by 11 publications

(7 citation statements)

References 41 publications

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“…Sheykhivand et al [22] Music CNN-LSTM 3 96 Hou et al [28] Video Table 6 compares previous studies, as well as the methods employed in each study, with the proposed improved deep model. As shown in Table 6, the proposed method achieved the highest accuracy when compared with previous works.…”

Section: Resultsmentioning

confidence: 99%

“…They used two databases, DEAP and SEED, in order to evaluate their proposed method and achieved a high accuracy of 90%. Hu et al [28] used Feature Pyramid Network (FPN) to improve emotion recognition performance based on EEG signals. In their proposed model, the Differential Entropy (DE) of each recorded EEG channel was extracted as the main feature.…”

Section: Introductionmentioning

confidence: 99%

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Customized 2D CNN Model for the Automatic Emotion Recognition Based on EEG Signals

et al. 2023

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Automatic emotion recognition from electroencephalogram (EEG) signals can be considered as the main component of brain–computer interface (BCI) systems. In the previous years, many researchers in this direction have presented various algorithms for the automatic classification of emotions from EEG signals, and they have achieved promising results; however, lack of stability, high error, and low accuracy are still considered as the central gaps in this research. For this purpose, obtaining a model with the precondition of stability, high accuracy, and low error is considered essential for the automatic classification of emotions. In this research, a model based on Deep Convolutional Neural Networks (DCNNs) is presented, which can classify three positive, negative, and neutral emotions from EEG signals based on musical stimuli with high reliability. For this purpose, a comprehensive database of EEG signals has been collected while volunteers were listening to positive and negative music in order to stimulate the emotional state. The architecture of the proposed model consists of a combination of six convolutional layers and two fully connected layers. In this research, different feature learning and hand-crafted feature selection/extraction algorithms were investigated and compared with each other in order to classify emotions. The proposed model for the classification of two classes (positive and negative) and three classes (positive, neutral, and negative) of emotions had 98% and 96% accuracy, respectively, which is very promising compared with the results of previous research. In order to evaluate more fully, the proposed model was also investigated in noisy environments; with a wide range of different SNRs, the classification accuracy was still greater than 90%. Due to the high performance of the proposed model, it can be used in brain–computer user environments.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Customized 2D CNN Model for the Automatic Emotion Recognition Based on EEG Signals

et al. 2023

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“…If the signal obeys or approximately obeys a Gaussian distribution

, its DE can be approximated to obtain a more easily calculated formula [ 41 ]. For the pre-processed EEG signals in each frequency band, they all approximately obey the Gaussian distribution.…”

Section: Methodsmentioning

confidence: 99%

Optimal channel dynamic selection for Constructing lightweight Data EEG-based emotion recognition

Zhang,

Xu,

Zhang

et al. 2024

Heliyon

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“…At this stage, there are two main steps for emotion recognition of EEG signals, feature extraction, and feature classification [15]. The feature extraction methods are mainly divided into the time domain, frequency domain, time-frequency domain, and spatial domain [16].…”

Section: Introductionmentioning

confidence: 99%

Domain-Adaptive Emotion Recognition Based on Horizontal Vertical Flow Representation of EEG Signals

Bai

et al. 2023

IEEE Access

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With the development of cognitive science and brain science, brain-computer interface technology can use Electroencephalogram (EEG) signals to better represent the inner changes of emotions. In this paper, A video-induced emotional stimulation experimental paradigm was designed, and the EEG signals of 15 hearing-impaired subjects under three emotions (positive, neutral, and negative) were collected. Considering the flow diffusion properties of EEG signals, we used the diffusion effect based on horizontal representation and vertical representation forms to obtain the spatial domain features. After EEG preprocessing, the differential entropy feature (DE) in the frequency domain is extracted. The frequency domain features of 62 channels are delivered to two Bi-directional Long Short-Term Memory (BiLSTM) to obtain spatial domain features of horizontal and vertical representations respectively, and then two kinds of domain features are fused by the residual network. The attention mechanism is applied to effectively extract emotional representational information from the fused features. To solve the cross-subject problem of emotion recognition, the domain adaptation method is utilized, and a center alignment loss function is applied to increase the distance of inter-class and reduce the distance of intra-class. According to the experimental results, the average accuracies of 75.89% (subject-dependent) and 69.59% (cross-subject) are obtained. Moreover, the validation was also performed on the public dataset SEED, achieving average accuracies of 93.99% (subject-dependent) and 84.22% (cross-subject), respectively.

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Deep feature pyramid network for EEG emotion recognition

Cited by 11 publications

References 41 publications

Customized 2D CNN Model for the Automatic Emotion Recognition Based on EEG Signals

Customized 2D CNN Model for the Automatic Emotion Recognition Based on EEG Signals

Optimal channel dynamic selection for Constructing lightweight Data EEG-based emotion recognition

Domain-Adaptive Emotion Recognition Based on Horizontal Vertical Flow Representation of EEG Signals

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