Deep time-frequency features and semi-supervised dimension reduction for subject-independent emotion recognition from multi-channel EEG signals

Zali-Vargahan, Behrooz; Charmin, Asghar; Kalbkhani, Hashem; Barghandan, Saeed

doi:10.1016/j.bspc.2023.104806

Cited by 19 publications

(11 citation statements)

References 51 publications

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“…They achieved 85.45% and 85.65% for valence and arousal. However, Xing et al 24 employed SAE (Stack AutoEncoder) with LSTM-RNN to fix the linear EEG signals problem. The accuracies obtained were 81.10% and 74.38% in valence and arousal, respectively.…”

Section: Discussionmentioning

confidence: 99%

“…Furthermore, scientists applied three distinct methods 24 to combine data from various channels and the Fusion after deep feature reduction (FaDFR) method, which combines reduced deep time–frequency features from EEG channels with Inception-V3 25 CNN for deep feature extraction and SVM for classification, produced superior results. The results demonstrated 88.6% accuracy on the DEAP dataset and 94.58% accuracy on the SEED dataset 26 .…”

Section: Related Workmentioning

confidence: 99%

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Detecting emotions through EEG signals based on modified convolutional fuzzy neural network

Ahmadzadeh Nobari Azar,

Cavus,

Esmaili

et al. 2024

Sci Rep

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Emotion is a human sense that can influence an individual’s life quality in both positive and negative ways. The ability to distinguish different types of emotion can lead researchers to estimate the current situation of patients or the probability of future disease. Recognizing emotions from images have problems concealing their feeling by modifying their facial expressions. This led researchers to consider Electroencephalography (EEG) signals for more accurate emotion detection. However, the complexity of EEG recordings and data analysis using conventional machine learning algorithms caused inconsistent emotion recognition. Therefore, utilizing hybrid deep learning models and other techniques has become common due to their ability to analyze complicated data and achieve higher performance by integrating diverse features of the models. However, researchers prioritize models with fewer parameters to achieve the highest average accuracy. This study improves the Convolutional Fuzzy Neural Network (CFNN) for emotion recognition using EEG signals to achieve a reliable detection system. Initially, the pre-processing and feature extraction phases are implemented to obtain noiseless and informative data. Then, the CFNN with modified architecture is trained to classify emotions. Several parametric and comparative experiments are performed. The proposed model achieved reliable performance for emotion recognition with an average accuracy of 98.21% and 98.08% for valence (pleasantness) and arousal (intensity), respectively, and outperformed state-of-the-art methods.

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Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Detecting emotions through EEG signals based on modified convolutional fuzzy neural network

Ahmadzadeh Nobari Azar,

Cavus,

Esmaili

et al. 2024

Sci Rep

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“…Therefore, the CDBA model proposed in this paper is the most suitable for emotion prediction compared with other models. Physiological signals like EEG have the advantages of universality, spontaneity, and difficulty in camouflage ( Zali-Vargahan et al, 2023 ), and human cognitive behavior and mental activity have a strong correlation with EEG signals. Therefore, physiological signals are a good choice to recognize emotions ( Liu et al, 2020 ).…”

Section: Discussionmentioning

confidence: 99%

CDBA: a novel multi-branch feature fusion model for EEG-based emotion recognition

Huang

et al. 2023

Front. Physiol.

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EEG-based emotion recognition through artificial intelligence is one of the major areas of biomedical and machine learning, which plays a key role in understanding brain activity and developing decision-making systems. However, the traditional EEG-based emotion recognition is a single feature input mode, which cannot obtain multiple feature information, and cannot meet the requirements of intelligent and high real-time brain computer interface. And because the EEG signal is nonlinear, the traditional methods of time domain or frequency domain are not suitable. In this paper, a CNN-DSC-Bi-LSTM-Attention (CDBA) model based on EEG signals for automatic emotion recognition is presented, which contains three feature-extracted channels. The normalized EEG signals are used as an input, the feature of which is extracted by multi-branching and then concatenated, and each channel feature weight is assigned through the attention mechanism layer. Finally, Softmax was used to classify EEG signals. To evaluate the performance of the proposed CDBA model, experiments were performed on SEED and DREAMER datasets, separately. The validation experimental results show that the proposed CDBA model is effective in classifying EEG emotions. For triple-category (positive, neutral and negative) and four-category (happiness, sadness, fear and neutrality), the classification accuracies were respectively 99.44% and 99.99% on SEED datasets. For five classification (Valence 1—Valence 5) on DREAMER datasets, the accuracy is 84.49%. To further verify and evaluate the model accuracy and credibility, the multi-classification experiments based on ten-fold cross-validation were conducted, the elevation indexes of which are all higher than other models. The results show that the multi-branch feature fusion deep learning model based on attention mechanism has strong fitting and generalization ability and can solve nonlinear modeling problems, so it is an effective emotion recognition method. Therefore, it is helpful to the diagnosis and treatment of nervous system diseases, and it is expected to be applied to emotion-based brain computer interface systems.

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“…Liu et al ( 2023 ) combined the attention mechanism and pre-trianed convolutional capsule network to extract the spatial information from the original emotional EEG signals. Zali-Vargahan et al ( 2023 ) introduced CNN to extract the deep time-frequency features and employed several machine classifiers such as decision tree to classify different emotional states, where the average accuracy of 94.58% had been achieved in SEED. Ma et al ( 2023 ) developed the transfer learning methods to reduce the distribution differences of emotional EEG signals between different subjects, thereby enabling more robust cross-subject emotion recognition.…”

Section: Introductionmentioning

confidence: 99%

A study on the combination of functional connection features and Riemannian manifold in EEG emotion recognition

Wu,

Ouyang,

Zhou

et al. 2024

Front. Neurosci.

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IntroductionAffective computing is the core for Human-computer interface (HCI) to be more intelligent, where electroencephalogram (EEG) based emotion recognition is one of the primary research orientations. Besides, in the field of brain-computer interface, Riemannian manifold is a highly robust and effective method. However, the symmetric positive definiteness (SPD) of the features limits its application.MethodsIn the present work, we introduced the Laplace matrix to transform the functional connection features, i.e., phase locking value (PLV), Pearson correlation coefficient (PCC), spectral coherent (COH), and mutual information (MI), to into semi-positive, and the max operator to ensure the transformed feature be positive. Then the SPD network is employed to extract the deep spatial information and a fully connected layer is employed to validate the effectiveness of the extracted features. Particularly, the decision layer fusion strategy is utilized to achieve more accurate and stable recognition results, and the differences of classification performance of different feature combinations are studied. What's more, the optimal threshold value applied to the functional connection feature is also studied.ResultsThe public emotional dataset, SEED, is adopted to test the proposed method with subject dependent cross-validation strategy. The result of average accuracies for the four features indicate that PCC outperform others three features. The proposed model achieve best accuracy of 91.05% for the fusion of PLV, PCC, and COH, followed by the fusion of all four features with the accuracy of 90.16%.DiscussionThe experimental results demonstrate that the optimal thresholds for the four functional connection features always kept relatively stable within a fixed interval. In conclusion, the experimental results demonstrated the effectiveness of the proposed method.

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Deep time-frequency features and semi-supervised dimension reduction for subject-independent emotion recognition from multi-channel EEG signals

Cited by 19 publications

References 51 publications

Detecting emotions through EEG signals based on modified convolutional fuzzy neural network

Detecting emotions through EEG signals based on modified convolutional fuzzy neural network

CDBA: a novel multi-branch feature fusion model for EEG-based emotion recognition

A study on the combination of functional connection features and Riemannian manifold in EEG emotion recognition

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