Subject-independent Emotion recognition based on Entropy of EEG Signals

Yang, Haihui; Rong, Panxiang; Sun, Guobing

doi:10.1109/ccdc52312.2021.9602439

Cited by 4 publications

(5 citation statements)

References 15 publications

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“…Comparing results, the proposed STILN possesses higher accuracy of arousal and valence than the performance of networks in [33]- [40] (except [38]). Our method is more accurate in classification of arousal, but the performance of classification for valence is not as better as [38]. Through analysis, [38] adopts K-fold cross-validation, and the EEG of the same subjects is distributed in the training set and the testing set, and resulting in higher accuracy for test.…”

Section: Comparison With Related Researchmentioning

confidence: 92%

“…Our method is more accurate in classification of arousal, but the performance of classification for valence is not as better as [38]. Through analysis, [38] adopts K-fold cross-validation, and the EEG of the same subjects is distributed in the training set and the testing set, and resulting in higher accuracy for test. Furthermore, we find that the methods using PSD features of EEG superior to the methods applying other EEG features (differential entropy, wavelet entropy, etc.).…”

Section: Comparison With Related Researchmentioning

confidence: 99%

“…Table 8 lists the performance of classification for comparison schemes. Except for references [33] and [38] using the K-fold cross-validation method, the remaining research uses LOOCV. Comparing results, the proposed STILN possesses higher accuracy of arousal and valence than the performance of networks in [33]- [40] (except [38]).…”

Section: Comparison With Related Researchmentioning

confidence: 99%

“…Except for references [33] and [38] using the K-fold cross-validation method, the remaining research uses LOOCV. Comparing results, the proposed STILN possesses higher accuracy of arousal and valence than the performance of networks in [33]- [40] (except [38]). Our method is more accurate in classification of arousal, but the performance of classification for valence is not as better as [38].…”

Section: Comparison With Related Researchmentioning

confidence: 99%

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Spatial-Temporal Feature Fusion Neural Network for EEG-Based Emotion Recognition

Wang

Zhang

et al. 2022

IEEE Trans. Instrum. Meas.

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The spatial correlations and the temporal contexts are indispensable in Electroencephalogram (EEG)-based emotion recognition. However, the learning of complex spatial correlations among several channels is a challenging problem. Besides, the temporal contexts learning is beneficial to emphasize the critical EEG frames because the subjects only reach the prospective emotion during part of stimuli. Hence, we propose a novel Spatial-Temporal Information Learning Network (STILN) to extract the discriminative features by capturing the spatial correlations and temporal contexts. Specifically, the generated 2D power topographic maps capture the dependencies among electrodes, and they are fed to the CNN-based spatial feature extraction network. Furthermore, Convolutional Block Attention Module (CBAM) recalibrates the weights of power topographic maps to emphasize the crucial brain regions and frequency bands. Meanwhile, Batch Normalizations (BNs) and Instance Normalizations (INs) are appropriately combined to relieve the individual differences. In the temporal contexts learning, we adopt the Bidirectional Long Short-Term Memory Network (Bi-LSTM) network to capture the dependencies among the EEG frames. To validate the effectiveness of the proposed method, subject-independent experiments are conducted on the public DEAP dataset. The proposed method has achieved the outstanding performance, and the accuracies of arousal and valence classification have reached 0.6831 and 0.6752 respectively.

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Section: Comparison With Related Researchmentioning

confidence: 92%

Section: Comparison With Related Researchmentioning

confidence: 99%

Section: Comparison With Related Researchmentioning

confidence: 99%

Section: Comparison With Related Researchmentioning

confidence: 99%

See 2 more Smart Citations

Spatial-Temporal Feature Fusion Neural Network for EEG-Based Emotion Recognition

Wang

Zhang

et al. 2022

IEEE Trans. Instrum. Meas.

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“…Numerous studies have been conducted on emotion recognition based on various modes, such as facial expressions (Ko, 2018 ), speech (Schuller, 2018 ) and electrophysiological signals. Electroencephalography (EEG) stands out among these signals due to its objective properties and high temporal resolution benefits (Yang et al, 2021 ). Specifically, EEG-based affective brain-computer-interfaces (aBCIs) (Mühl et al, 2014 ) aim to detect affective states from EEG signals and use them in various applications, such as estimating driver drowsiness to improve driving safety (Wu et al, 2016 ; Cui et al, 2019 ; Jiang et al, 2020 ) and establishing an objective detection system for depression (Cai et al, 2020 ) or post-traumatic stress disorder (Rozgic et al, 2014 ) to enable self-diagnosis.…”

Section: Introductionmentioning

confidence: 99%

MISNet: multi-source information-shared EEG emotion recognition network with two-stream structure

Gong,

Zhong,

et al. 2024

Front. Neurosci.

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IntroductionWhen constructing machine learning and deep neural networks, the domain shift problem on different subjects complicates the subject independent electroencephalography (EEG) emotion recognition. Most of the existing domain adaptation methods either treat all source domains as equivalent or train source-specific learners directly, misleading the network to acquire unreasonable transfer knowledge and thus resulting in negative transfer.MethodsThis paper incorporates the individual difference and group commonality of distinct domains and proposes a multi-source information-shared network (MISNet) to enhance the performance of subject independent EEG emotion recognition models. The network stability is enhanced by employing a two-stream training structure with loop iteration strategy to alleviate outlier sources confusing the model. Additionally, we design two auxiliary loss functions for aligning the marginal distributions of domain-specific and domain shared features, and then optimize the convergence process by constraining gradient penalty on these auxiliary loss functions. Furthermore, the pre-training strategy is also proposed to ensure that the initial mapping of shared encoder contains sufficient emotional information.ResultsWe evaluate the proposed MISNet to ascertain the impact of several hyper-parameters on the domain adaptation capability of network. The ablation experiments are conducted on two publically accessible datasets SEED and SEED-IV to assess the effectiveness of each loss function.DiscussionThe experimental results demonstrate that by disentangling private and shared emotional characteristics from differential entropy features of EEG signals, the proposed MISNet can gain robust subject independent performance and strong domain adaptability.

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Multi-source Information-Shared Domain Adaptation for EEG Emotion Recognition

Gong

Wang

et al. 2022

Lecture Notes in Computer Science

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Subject-independent Emotion recognition based on Entropy of EEG Signals

Cited by 4 publications

References 15 publications

Spatial-Temporal Feature Fusion Neural Network for EEG-Based Emotion Recognition

Spatial-Temporal Feature Fusion Neural Network for EEG-Based Emotion Recognition

MISNet: multi-source information-shared EEG emotion recognition network with two-stream structure

Multi-source Information-Shared Domain Adaptation for EEG Emotion Recognition

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