Data augmentation for enhancing EEG-based emotion recognition with deep generative models.
Open accessAbstract
Objective. The data scarcity problem in emotion recognition from electroencephalography (EEG) leads to difficulty in building an affective model with high accuracy using machine learning algorithms or deep neural networks. Inspired by emerging deep generative models, we propose three methods for augmenting EEG training data to enhance the performance of emotion recognition models. Approach. Our proposed methods are based on two deep generative models, variational autoencoder (VAE) and generative adversarial network (GAN), and two data augmentation ways, full and partial usage strategies. For the full usage strategy, all of the generated data are augmented to the training dataset without judging the quality of the generated data, while for the partial usage, only high-quality data are selected and appended to the training dataset. These three methods are called conditional Wasserstein GAN (cWGAN), selective VAE (sVAE), and selective WGAN (sWGAN). Main results. To evaluate the effectiveness of these proposed methods, we perform a systematic experimental study on two public EEG datasets for emotion recognition, namely, SEED and DEAP. We first generate realistic-like EEG training data in two forms: power spectral density and differential entropy. Then, we augment the original training datasets with a different number of generated realistic-like EEG data. Finally, we train support vector machines and deep neural networks with shortcut layers to build affective models using the original and augmented training datasets. The experimental results demonstrate that our proposed data augmentation methods based on generative models outperform the existing data augmentation approaches such as conditional VAE, Gaussian noise, and rotational data augmentation. We also observe that the number of generated data should be less than 10 times of the original training dataset to achieve the best performance. Significance. The augmented training datasets produced by our proposed sWGAN method significantly enhance the performance of EEG-based emotion recognition models.
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cWGAN and sVAE DA with power spectral density or differential entropy representations enhanced classifier performance in emotion recognition.
“Experiments show that two representations of EEG signals were suitable for the requirement of the artificial datasets that enhances the performance of the classifier.”
From Data Augmentation for Deep Neural Networks Model in EEG Classification Task: A Review (2021) · cites this paper as Luo et al., 2020
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · see citing works
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155 · see citing works
(2008) · Journal of Machine Learning Research · Cited by 33,051 · see citing works
Labaca-Castro, R. (2023) · Springer eBooks · doi:10.1007/978-3-658-40442-0_9 · Cited by 21,100 · see citing works
Reducing the dimensionality of data with neural networks
Hinton, G. E., & Salakhutdinov, R. R. (2006) · Science · doi:10.1126/science.1127647 · Cited by 20,928 · see citing works
Auto-Encoding Variational Bayes
Kingma, D. P., & Welling, M. (2013) · arXiv · doi:10.48550/arxiv.1312.6114 · Cited by 20,067 · see citing works
GAN（Generative Adversarial Nets）
淳司, 柴. (2017) · Journal of Japan Society for Fuzzy Theory and Intelligent Informatics · doi:10.3156/jsoft.29.5_177_2 · Cited by 19,268 · see citing works
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
Ledig, C., Theis, L., Huszar, F., Caballero, J., Cunningham, A., Acosta, A., Aitken, A., Tejani, A., Totz, J., Wang, Z., et al. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.19 · Cited by 12,696 · see citing works
Unsupervised representation learning with deep convolutional generative adversarial networks
Radford, A., Metz, L., & Chintala, S. (2015) · arXiv · doi:10.48550/arxiv.1511.06434 · Cited by 12,235 · see citing works
Improved training of Wasserstein GANs
Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., & Courville, A. (2017) · arXiv · doi:10.48550/arxiv.1704.00028 · Cited by 6,800 · see citing works
DEAP: A Database for Emotion Analysis ;Using Physiological Signals
Koelstra, S., Muhl, C., Soleymani, M., Lee, J.-S., Yazdani, A., Ebrahimi, T., Pun, T., Nijholt, A., & Patras, I. (2012) · IEEE Transactions on Affective Computing · doi:10.1109/t-affc.2011.15 · Cited by 5,093 · see citing works
Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition
Dahl, G. E., Yu, D., Deng, L., & Acero, A. (2012) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/tasl.2011.2134090 · Cited by 3,011 · see citing works
Best practices for convolutional neural networks applied to visual document analysis
Simard, P., Steinkraus, D., & Platt, J. C. (2005) · Proceedings of the International Conference on Document Analysis and Recognition · doi:10.1109/icdar.2003.1227801 · Cited by 2,805 · see citing works
Zheng, W.-L., & Lu, B.-L. (2015) · IEEE Transactions on Autonomous Mental Development · doi:10.1109/tamd.2015.2431497 · Cited by 2,477 · see citing works
Self-citationUnlabeled Samples Generated by GAN Improve the Person Re-identification Baseline in Vitro
Zheng, Z., Zheng, L., & Yang, Y. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.405 · Cited by 2,142 · see citing works
The Unreasonable Effectiveness of Data
Halevy, A. Y., Norvig, P., & Pereira, F. M. B. (2009) · IEEE Intelligent Systems · doi:10.1109/mis.2009.36 · Cited by 1,776 · see citing works
Deep learning for electroencephalogram (EEG) classification tasks: a review
Craik, A., He, Y., & Contreras-Vidal, J. L. (2019) · Journal of Neural Engineering · doi:10.1088/1741-2552/ab0ab5 · Cited by 1,758 · see citing works
A Multimodal Database for Affect Recognition and Implicit Tagging
Soleymani, M., Lichtenauer, J., Pun, T., & Pantic, M. (2012) · IEEE Transactions on Affective Computing · doi:10.1109/t-affc.2011.25 · Cited by 1,664 · see citing works
Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling
Wu, J., Zhang, C., Xue, T., Freeman, W. T., & Tenenbaum, J. B. (2016) · arXiv · doi:10.48550/arxiv.1610.07584 · Cited by 1,393 · see citing works
EmotionMeter: A Multimodal Framework for Recognizing Human Emotions
Zheng, W.-L., Liu, W., Lu, Y., Lu, B.-L., & Cichocki, A. (2019) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2018.2797176 · Cited by 1,276 · see citing works
Self-citationA survey on deep learning for big data
Zhang, Q., Yang, L. T., Chen, Z., & Li, P. (2018) · Information Fusion · doi:10.1016/j.inffus.2017.10.006 · Cited by 1,226 · see citing works
Emotions Recognition Using EEG Signals: A Survey
Alarcao, S. M., & Fonseca, M. J. (2019) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2017.2714671 · Cited by 1,201 · see citing works
Katsigiannis, S., & Ramzan, N. (2018) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2017.2688239 · Cited by 1,135 · see citing works
Differential entropy feature for EEG-based emotion classification
Duan, R.-N., Zhu, J., & Lu, B. (2013) · International IEEE/EMBS Conference on Neural Engineering · doi:10.1109/ner.2013.6695876 · Cited by 1,108 · see citing works
Self-citationFeature Extraction and Selection for Emotion Recognition from EEG
Jenke, R., Peer, A., & BUSS, M. J. (2014) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2014.2339834 · Cited by 1,087 · see citing works
van Dyk, D. A., & Meng, X.-L. (2001) · Journal of Computational and Graphical Statistics · doi:10.1198/10618600152418584 · Cited by 1,052 · see citing works
Identifying Stable Patterns over Time for Emotion Recognition from EEG
Zheng, W.-L., Zhu, J.-Y., & Lu, B.-L. (2016) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2017.2712143 · Cited by 925 · see citing works
Self-citationEmotional state classification from EEG data using machine learning approach
Wang, X.-W., Nie, D., & Lu, B.-L. (2014) · Neurocomputing · doi:10.1016/j.neucom.2013.06.046 · Cited by 755 · see citing works
Self-citationDeep Convolutional Inverse Graphics NetworkPreprint
Kulkarni, T. D., Whitney, W., Kohli, P., & Tenenbaum, J. B. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1503.03167 · Cited by 708 · see citing works
Emotion Recognition From EEG Using Higher Order Crossings
Petrantonakis, P. C., & Hadjileontiadis, L. J. (2009) · IEEE Transactions on Information Technology in Biomedicine · doi:10.1109/titb.2009.2034649 · Cited by 687 · see citing works
Differential entropy feature for EEG-based vigilance estimation
Shi, L.-C., Jiao, Y.-Y., & Lu, B.-L. (2013) · 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2013.6611075 · Cited by 396 · see citing works
Self-citationBänziger, T., Grandjean, D., & Scherer, K. R. (2009) · Emotion · doi:10.1037/a0017088 · Cited by 380 · see citing works
Markov Chain Monte Carlo and Variational Inference: Bridging the Gap
Salimans, T., Kingma, D. P., & Welling, M. (2014) · UvA-DARE (University of Amsterdam) · doi:10.48550/arxiv.1410.6460 · Cited by 362 · see citing works
MPED: A Multi-Modal Physiological Emotion Database for Discrete Emotion Recognition
Song, T., Zheng, W., Cheng, L., Zong, Y., Zhang, X., & Cui, Z. (2019) · IEEE Access · doi:10.1109/access.2019.2891579 · Cited by 339 · see citing works
Lotte, F. (2015) · Proceedings of the IEEE · doi:10.1109/jproc.2015.2404941 · Cited by 327 · see citing works
A survey of affective brain computer interfaces: principles, state-of-the-art, and challenges
Mühl, C., Allison, B. Z., Nijholt, A., & Chanel, G. (2014) · Brain-Computer Interfaces · doi:10.1080/2326263x.2014.912881 · Cited by 284 · see citing works
Data augmentation for EEG-based emotion recognition with deep convolutional neural networks
Wang, F., Zhong, S.-H., Peng, J., Jiang, J., & Liu, Y. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-73600-6_8 · Cited by 256 · see citing works
Advances in Neural Networks - ISNN 2006
Wang, J., Żurada, J. M., Yi, Z., Lu, B., & Yin, H. (2006) · Lecture notes in computer science · doi:10.1007/11759966 · Cited by 253 · see citing works
Self-citationEEG Data Augmentation for Emotion Recognition Using a Conditional Wasserstein GAN
Luo, Y., & Lu, B. (2018) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2018.8512865 · Cited by 228 · see citing works
Self-citationEmotion Classification with Data Augmentation Using Generative Adversarial Networks
Zhu, X., Liu, Y., Li, J., Wan, T., & Qin, Z. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-93040-4_28 · Cited by 197 · see citing works
A facial expression emotion recognition based human-robot interaction system
Liu, Z., Wu, M. H., Cao, W., Chen, L., Xu, J., Zhang, R., Zhou, M., & Mao, J.-W. (2017) · IEEE/CAA Journal of Automatica Sinica · doi:10.1109/jas.2017.7510622 · Cited by 185 · see citing works
EEG-Based Emotion Recognition Using Hierarchical Network With Subnetwork Nodes
Yang, Y., Wu, Q. M. J., Zheng, W.-L., & Lu, B.-L. (2018) · IEEE Transactions on Cognitive and Developmental Systems · doi:10.1109/tcds.2017.2685338 · Cited by 141 · see citing works
Self-citationRotational data augmentation for electroencephalographic data
Krell, M. M., & Kim, S. K. (2017) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2017.8036864 · Cited by 73 · see citing works
Unsupervised Learning in Reservoir Computing for EEG-Based Emotion Recognition
Fourati, R., Ammar, B., Medina, J., & Alimi, A. M. (2020) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2020.2982143 · Cited by 68 · see citing works
Depression and implicit emotion processing: An EEG study
Bocharov, A. V., Knyazev, G. G., & Savostyanov, A. N. (2017) · Neurophysiologie Clinique · doi:10.1016/j.neucli.2017.01.009 · Cited by 55 · see citing works
Zhao, L.-M., Li, R., Zheng, W.-L., & Lu, B.-L. (2019) · International IEEE/EMBS Conference on Neural Engineering · doi:10.1109/ner.2019.8717055 · Cited by 53 · see citing works
Self-citationPrediction of subjective ratings of emotional pictures by EEG features
McFarland, D. J., Parvaz, M. A., Sarnacki, W. A., Goldstein, R. Z., & Wolpaw, J. R. (2016) · Journal of Neural Engineering · doi:10.1088/1741-2552/14/1/016009 · Cited by 37 · see citing works
Feature Extraction for Emotion Recognition and Modelling Using Neurophysiological Data
Samara, A., Menezes, M. L. R., & Galway, L. (2016) · International Conference on Ubiquitous Computing and Communications (IUCC) · doi:10.1109/iucc-css.2016.027 · Cited by 37 · see citing works
Using body movement and posture for emotion detection in non-acted scenarios
Garber-Barron, M., & Si, M. (2012) · IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) · doi:10.1109/fuzz-ieee.2012.6250780 · Cited by 26 · see citing works
Flagged first, then by citations
2 of the 51 references couldn’t be matched to a known paper.
Cited by
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EEG Based Emotion Recognition: A Tutorial and Review
Li, X., Zhang, Y., Tiwari, P., Song, D., Hu, B., Yang, M., Zhao, Z., Kumar, N., & Marttinen, P. (2023) · ACM Computing Surveys · doi:10.1145/3524499 · Cited by 508 · see citing works
Feng, L., Cheng, C., Zhao, M., Deng, H., & Zhang, Y. (2022) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3198688 · Cited by 165 · see citing works
GANSER: A Self-Supervised Data Augmentation Framework for EEG-Based Emotion Recognition
Zhang, Z., Liu, Y., & Zhong, S.-H. (2022) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2022.3170369 · Cited by 154 · see citing works
Samal, P., & Hashmi, M. F. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-023-10690-2 · Cited by 162 · see citing works
Generative adversarial networks in EEG analysis: an overview
Habashi, A. G., Azab, A. M., Eldawlatly, S., & Aly, G. M. (2023) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/s12984-023-01169-w · Cited by 126 · see citing works
Data Augmentation for Deep Neural Networks Model in EEG Classification Task: A Review
He, C., Liu, J., Zhu, Y., & Du, W. (2021) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2021.765525 · Cited by 105 · see citing works
“Luo et al. (2020) applied conditional Wasserstein GAN (cWGAN) and selective VAE (sVAE) to enhance the performance of the classifier in the emotion recognition tasks.”
MethodA Multifaceted benchmarking of synthetic electronic health record generation models
Yan, C., Yan, Y., Wan, Z., Zhang, Z., Omberg, L., Guinney, J., Mooney, S. D., & Malin, B. (2022) · Nature Communications · doi:10.1038/s41467-022-35295-1 · Cited by 100 · see citing works
Self-citationLiu, W., Zheng, W., Li, Z., Wu, S., Gan, L., & Lu, B. (2022) · Journal of Neural Engineering · doi:10.1088/1741-2552/ac5c8d · Cited by 94 · see citing works
Self-citationSample-Based Data Augmentation Based on Electroencephalogram Intrinsic Characteristics
Li, R., Wang, L., Suganthan, P. N., & Sourina, O. (2022) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3185587 · Cited by 82 · see citing works
PARSE: Pairwise Alignment of Representations in Semi-Supervised EEG Learning for Emotion Recognition
Zhang, G., Davoodnia, V., & Etemad, A. (2022) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2022.3210441 · Cited by 78 · see citing works
Dual-Encoder VAE-GAN With Spatiotemporal Features for Emotional EEG Data Augmentation
Tian, C., Ma, Y., Cammon, J., Fang, F. Z., Zhang, Y., & Meng, M. (2023) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2023.3266810 · Cited by 74 · see citing works
EEG-based emotion recognition systems; comprehensive study
Hamzah, H. A., & Abdalla, K. K. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e31485 · Cited by 82 · see citing works
EEG data augmentation for emotion recognition with a multiple generator conditional Wasserstein GAN
Zhang, A., Su, L., Zhang⋆, Y., Fu, Y., Wu, L., & Liang, S. (2021) · Complex & Intelligent Systems · doi:10.1007/s40747-021-00336-7 · Cited by 69 · see citing works
EEG Feature Extraction and Data Augmentation in Emotion Recognition
Kalashami, M. P., Pedram, M. M., & Sadr, H. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/7028517 · Cited by 69 · see citing works
Self-supervised Learning with Attention Mechanism for EEG-based seizure detection
Xiao, T., Wang, Z., Zhang, Y., Lv, H., Wang, S., Feng, H., & Zhao, Y. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.105464 · Cited by 69 · see citing works
Data Augmentation for Audio-Visual Emotion Recognition with an Efficient Multimodal Conditional GAN
Ma, F., Li, Y., Ni, S., Huang, S., & Zhang, L. (2022) · Applied Sciences · doi:10.3390/app12010527 · Cited by 69 · see citing works
Variational Autoencoder for Image-Based Augmentation of Eye-Tracking Data
Elbattah, M., Loughnane, C., Guérin, J.-L., Carette, R., Cilia, F., & Dequen, G. (2021) · Journal of Imaging · doi:10.3390/jimaging7050083 · Cited by 63 · see citing works
Ko, W., Jeon, E., Jeong, S., Phyo, J., & Suk, H. (2021) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2021.643386 · Cited by 58 · see citing works
Diagnosis of autism spectrum disorder based on functional brain networks and machine learning
Alves, C. L., Toutain, T. G. L. D. O., Aguiar, P. D. C., Pineda, A. M., Roster, K. I. O., Thielemann, C., Porto, J. A. M., & Rodrigues, F. A. (2023) · Scientific Reports · doi:10.1038/s41598-023-34650-6 · Cited by 60 · see citing works
A systematic literature review of emotion recognition using EEG signals
Prabowo, D. W., Nugroho, H. A., Setiawan, N. A., & Debayle, J. (2023) · Cognitive Systems Research · doi:10.1016/j.cogsys.2023.101152 · Cited by 54 · see citing works
Lee, D.-Y., Jeong, J.-H., Lee, B.-H., & Lee, S. (2022) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2022.3143836 · Cited by 53 · see citing works
Lü, H., Barzegar, V., Nemani, V. P., Hu, C. K., Laflamme, S., & Zimmerman, A. T. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.117415 · Cited by 47 · see citing works
Adaptive Deep Feature Fusion for Continuous Authentication With Data Augmentation
Li, Y., Liu, L., Qin, H., Deng, S., El‐Yacoubi, M. A., & Zhou, G. (2022) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2022.3186614 · Cited by 45 · see citing works
BWGAN-GP: An EEG Data Generation Method for Class Imbalance Problem in RSVP Tasks
Xu, M., Chen, Y., Wang, Y., Wang, D., Liu, Z., & Zhang, L. (2022) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2022.3145515 · Cited by 44 · see citing works
Arı, B. G., Siddique, K., Alçin, Ö. F., Aslan, M., Şengür, A., & Mehmood, R. M. (2022) · IEEE Access · doi:10.1109/access.2022.3181887 · Cited by 44 · see citing works
Zhou, R., Ye, W., Zhang, Z., Luo, Y., Zhang, L., Li, L., Huang, G., Dong, Y., Zhang, Y., & Liang, Z. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2024.3493425 · Cited by 42 · see citing works
Self-citationGITGAN: Generative inter-subject transfer for EEG motor imagery analysis
Yin, K., Lim, E. Y., & Lee, S. (2023) · Pattern Recognition · doi:10.1016/j.patcog.2023.110015 · Cited by 41 · see citing works
EEG2Vec: Learning Affective EEG Representations via Variational Autoencoders
Bethge, D., Hallgarten, P., Große-Puppendahl, T., Kari, M., Chuang, L. L., Özdenizci, O., & Schmidt, A. (2022) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc53654.2022.9945517 · Cited by 41 · see citing works
Mini review: Challenges in EEG emotion recognition
Zhang, Z., Mir, J. M. F., & Mateu, L. G. (2024) · Frontiers in Psychology · doi:10.3389/fpsyg.2023.1289816 · Cited by 39 · see citing works
Guo, W., & Wang, Y. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.121889 · Cited by 37 · see citing works
Gilakjani, S. S., & Osman, H. A. (2023) · IEEE Access · doi:10.1109/access.2023.3344476 · Cited by 38 · see citing works
Generating realistic neurophysiological time series with denoising diffusion probabilistic models
Vetter, J., Macke, J. H., & Gao, R. (2024) · Patterns · doi:10.1016/j.patter.2024.101047 · Cited by 40 · see citing works
Carrle, F. P., Hollenbenders, Y., & Reichenbach, A. (2023) · Frontiers in Neuroscience · doi:10.3389/fnins.2023.1219133 · Cited by 36 · see citing works
Xie, Y., & Oniga, S. (2023) · Sensors · doi:10.3390/s23041932 · Cited by 40 · see citing works
Ahmed, T., & Longo, L. (2022) · IEEE Access · doi:10.1109/access.2022.3212777 · Cited by 34 · see citing works
On the role of generative artificial intelligence in the development of brain-computer interfaces
Eldawlatly, S. (2024) · BMC Biomedical Engineering · doi:10.1186/s42490-024-00080-2 · Cited by 34 · see citing works
Representation Learning and Pattern Recognition in Cognitive Biometrics: A Survey
Wang, M., Yin, X., Zhu, Y., & Hu, J. (2022) · Sensors · doi:10.3390/s22145111 · Cited by 34 · see citing works
Dawsari, H. A., Al-Ahmadi, S. A., & Muhammad, F. N. (2023) · Diagnostics · doi:10.3390/diagnostics13162624 · Cited by 32 · see citing works
Ahuja, C. K., & Sethia, D. (2024) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2024.1421922 · Cited by 31 · see citing works
Zhou, S., Huang, D., Liu, C., & Jiang, D. (2022) · Applied Soft Computing · doi:10.1016/j.asoc.2022.108889 · Cited by 28 · see citing works
Jurczak, M., Kołodziej, M., & Majkowski, A. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2022.782367 · Cited by 26 · see citing works
Diagnosis of Autism Disorder Based on Deep Network Trained by Augmented EEG Signals
Ardakani, H. A., Taghizadeh, M., & Shayegh, F. (2022) · International Journal of Neural Systems · doi:10.1142/s0129065722500460 · Cited by 26 · see citing works
LibEER: A Comprehensive Benchmark and Algorithm Library for EEG-Based Emotion Recognition
Liu, H., Yang, S., Zhang, Y., Wang, M., Gong, F., Xie, C., Liu, G., Liu, Z., Liu, Y., Lu, B., et al. (2025) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2025.3605833 · Cited by 25 · see citing works
Self-citationGenerative technology for human emotion recognition: A scoping review
Ma, F., Yuan, Y., Xie, Y., Ren, H., Liu, I., He, Y., Ren, F., Yu, F. R., & Ni, S. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102753 · Cited by 39 · see citing works
Diffusion models-based motor imagery EEG sample augmentation via mixup strategy
Luo, T.-J., & Cai, Z. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.125585 · Cited by 22 · see citing works
Yi, Y., Xu, Y., Yang, B., & Tian, Y. W. (2024) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2024.3395359 · Cited by 21 · see citing works
Prakash, A., & Poulose, A. (2025) · Data Science and Management · doi:10.1016/j.dsm.2024.12.004 · Cited by 21 · see citing works
Decoding motor imagery tasks using ESI and hybrid feature CNN
Fang, T., Song, Z., Zhan, G., Zhang, X., Mu, W., Wang, P., Zhang, L., & Kang, X. (2022) · Journal of Neural Engineering · doi:10.1088/1741-2552/ac4ed0 · Cited by 21 · see citing works
Martins, F. M., González, V. M., Villar, J. R., & López, B. G. (2023) · Sensors · doi:10.3390/s23042312 · Cited by 21 · see citing works
Li, Y., Luo, J., Deng, S., & Zhou, G. (2023) · ACM Transactions on Sensor Networks · doi:10.1145/3599727 · Cited by 20 · see citing works
Zhang, J., Zhang, F., & Wei, H. (2024) · Pattern Recognition · doi:10.1016/j.patcog.2024.111005 · Cited by 20 · see citing works
Choo, S., Park, H., Jung, J., Flores, K., & Nam, C. S. (2024) · Neural Networks · doi:10.1016/j.neunet.2024.106665 · Cited by 19 · see citing works
Yang, Y., Zhang, X., Guan, Q., & Lin, Y. (2022) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2022.3144636 · Cited by 19 · see citing works
Song, Z., Deng, B., Wang, J., & Yi, G. (2022) · Journal of Neural Engineering · doi:10.1088/1741-2552/ac697d · Cited by 19 · see citing works
EEG Data Augmentation for Emotion Recognition with a Task-Driven GAN
Qing, L., Hao, J., & Guo, Y. (2023) · Algorithms · doi:10.3390/a16020118 · Cited by 19 · see citing works
Deep Generative Models: The winning key for large and easily accessible ECG datasets?
Monachino, G., Zanchi, B., Fiorillo, L., Conte, G., Auricchio, A., Tzovara, A., & Faraci, F. D. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107655 · Cited by 19 · see citing works
Adaptive Domain Alignment Neural Networks for Cross-Domain EEG Emotion Recognition
Hong, X., Du, C., & He, H. (2024) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2024.3480355 · Cited by 18 · see citing works
Toward the Construction of Affective Brain-Computer Interface: A Systematic Review
Chen, H., Li, J., He, H., Zhu, J., Sun, S., Li, X., & Hu, B. (2025) · ACM Computing Surveys · doi:10.1145/3712259 · Cited by 17 · see citing works
E2SGAN: EEG-to-SEEG translation with generative adversarial networks
Hu, M., Chen, J., Jiang, S., Ji, W., Mei, S., Chen, L., & Wang, X. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2022.971829 · Cited by 16 · see citing works
Cai, Z., Wang, L., Guo, M., Xu, G., Guo, L., & Li, Y. (2022) · International Journal of Neural Systems · doi:10.1142/s0129065722500058 · Cited by 17 · see citing works
Subject-independent EEG classification based on a hybrid neural network
Zhang, H., Ji, H., Yu, J. H., Li, J., Jin, L., Liu, L., Bai, Z., & Ye, C. (2023) · Frontiers in Neuroscience · doi:10.3389/fnins.2023.1124089 · Cited by 16 · see citing works
Short-length SSVEP data extension by a novel generative adversarial networks based framework
Pan, Y., Li, N., Zhang, Y., Xu, P., & Yao, D. (2024) · Cognitive Neurodynamics · doi:10.1007/s11571-024-10134-9 · Cited by 16 · see citing works
Multimodal Emotion Classification Method and Analysis of Brain Functional Connectivity Networks
Sun, X., Zheng, X., Li, T., Li, Y., & Cui, L. (2022) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2022.3192533 · Cited by 16 · see citing works
Song, Y., Yang, L., Jia, X., & Xie, L. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2102.04456 · Cited by 20 · see citing works
Deep learning approaches for EEG-based healthcare applications: a comprehensive review
Lyu, R. (2026) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2025.1689073 · Cited by 15 · see citing works
Novel three-axis accelerometer-based silent speech interface using deep neural network
Kwon, J., Nam, H., Chae, Y., Lee, S., Kim, I. Y., & Im, C. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.105909 · Cited by 15 · see citing works
Wang, R., Li, T., Gao, Z. J., Yan, X., Wang, J., Wang, Z., & Gao, J. (2024) · Applied Soft Computing · doi:10.1016/j.asoc.2024.111301 · Cited by 15 · see citing works
Ko, W., Jeon, E., Yoon, J. S., & Suk, H. (2022) · Scientific Reports · doi:10.1038/s41598-022-08490-9 · Cited by 14 · see citing works
Samyoun, S., Islam, M. M., Iqbal, T., & Stankovic, J. A. (2022) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3534600 · Cited by 14 · see citing works
Hu, Y., Sun, L., Mao, X., & Zhang, S. (2024) · Electronics · doi:10.3390/electronics13214310 · Cited by 14 · see citing works
Research on Fatigue Driving Detection Technology Based on CA-ACGAN
Ye, H., Chen, M., & Feng, G. (2024) · Brain Sciences · doi:10.3390/brainsci14050436 · Cited by 13 · see citing works
A self-supervised data augmentation strategy for EEG-based emotion recognition
Qiao, Y., & Zhao, Q. (2025) · Information Fusion · doi:10.1016/j.inffus.2025.103279 · Cited by 13 · see citing works
Gan, M., & Wang, C. (2022) · Biomedical Optics Express · doi:10.1364/boe.449796 · Cited by 15 · see citing works
An Auxiliary Synthesis Framework for Enhancing EEG-Based Classification With Limited Data
Liang, S., Kuang, S., Wang, D., Yuan, Z., Zhang, H., & Sun, L. (2023) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2023.3268979 · Cited by 13 · see citing works
EEG‐Based Emotion Recognition Datasets for Virtual Environments: A Survey
Hamzah, H. A., & Abdalla, K. K. (2024) · Applied Computational Intelligence and Soft Computing · doi:10.1155/2024/6091523 · Cited by 14 · see citing works
Generative Adversarial Networks for Electroencephalogram Signal Analysis: A Mini Review
Wang, J., Mu, W., Wang, A., Wang, L., Han, J., Wang, P., Niu, L., Bin, J., Zhang, L., & Kang, X. (2023) · International Winter Conference on Brain-Computer Interface (BCI) · doi:10.1109/bci57258.2023.10078666 · Cited by 12 · see citing works
Synthesizing affective neurophysiological signals using generative models: A review paper
Nia, A. F., Tang, V., Talou, G. D. M., & Billinghurst, M. (2024) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2024.110129 · Cited by 12 · see citing works
Zhang, X., Lu, Z., Zhang, T., Li, H., Wang, Y., & Tao, Q. (2021) · Frontiers in Neuroscience · doi:10.3389/fnins.2021.727394 · Cited by 11 · see citing works
On the use of generative deep neural networks to synthesize artificial multichannel EEG signals
Özdenizci, O., & Deniz Erdogmus, B. (2021) · International IEEE/EMBS Conference on Neural Engineering · doi:10.1109/ner49283.2021.9441381 · Cited by 11 · see citing works
ATGAN: attention-based temporal GAN for EEG data augmentation in personal identification
Zhang, S., Sun, L., Mao, X., Zhao, M., & Hu, Y. (2024) · EURASIP Journal on Advances in Signal Processing · doi:10.1186/s13634-024-01188-2 · Cited by 10 · see citing works
Synthetic seismocardiogram generation using a transformer-based neural network
Nikbakht, M. R., Gazi, A. H., Zia, J. S., An, S., Lin, D. J., Inan, O. T., & Kamaleswaran, R. (2023) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocad067 · Cited by 10 · see citing works
Virtual Electroencephalogram Acquisition: A Review on Electroencephalogram Generative Methods
You, Z., Guo, Y., Zhang, X., & Zhao, Y. (2025) · Sensors · doi:10.3390/s25103178 · Cited by 9 · see citing works
Improving EEG-based Alzheimer’s Disease Identification with Generative Adversarial Learning
Song, Z., Wang, J., Yi, G., & Deng, B. (2021) · Chinese Control Conference (CCC) · doi:10.23919/ccc52363.2021.9550108 · Cited by 9 · see citing works
Bhandari, V., Londhe, N. D., & Kshirsagar, G. B. (2023) · IEEE Transactions on Cognitive and Developmental Systems · doi:10.1109/tcds.2023.3288201 · Cited by 9 · see citing works
Decoding auditory-evoked response in affective states using wearable around-ear EEG system
Choi, J.-H., Kaongoen, N., Choi, H., Kim, M., Kim, B. H., & Jo, S. (2023) · Biomedical Physics & Engineering Express · doi:10.1088/2057-1976/acf137 · Cited by 8 · see citing works
EEG-Based Emotion Recognition Using Spatial-Temporal Connectivity
Chu, W., Fu, B., Xia, Y., & Liu, Y. (2023) · IEEE Access · doi:10.1109/access.2023.3308811 · Cited by 8 · see citing works
Li, X., Song, J., Zhao, Z., Wang, C., Song, D., & Hu, B. (2024) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp48485.2024.10447740 · Cited by 8 · see citing works
Ground Truth Dataset for EEG-Based Emotion Recognition With Visual Indication
Yang, G., Jiao, R. L., Jiang, H., & Zhang, T. (2020) · IEEE Access · doi:10.1109/access.2020.3030680 · Cited by 8 · see citing works
Improving Automatic Recognition of Emotional States Using EEG Data Augmentation Techniques
Szczakowska, P., Wosiak, A., & Żykwińska, K. (2023) · Procedia Computer Science · doi:10.1016/j.procs.2023.10.419 · Cited by 7 · see citing works
EEG Topography Amplification Using FastGAN-ASP Method
Zhao, M., Zhang, S., Mao, X., & Sun, L. (2023) · Electronics · doi:10.3390/electronics12244944 · Cited by 7 · see citing works
EEG emotion recognition framework based on invariant wavelet scattering convolution network
Elrefaiy, A., Tawfik, N., Zayed, N. M., & Elhenawy, I. (2024) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-023-04746-y · Cited by 7 · see citing works
Zhang, X., Wang, S., Xu, K., Zhao, R., & She, Y. (2024) · Mathematical Biosciences & Engineering · doi:10.3934/mbe.2024210 · Cited by 6 · see citing works
EEG Data Augmentation Using Generative Adversarial Network for Improved Emotion Recognition
Acharjee, R., & Ahamed, S. R. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-78195-7_16 · Cited by 6 · see citing works
Temporal spiking generative adversarial networks for heading direction decoding
Shen, J., Wang, K., Gao, W., Liu, J. K., Xu, Q., Pan, G., Chen, X., & Tang, H. (2024) · Neural Networks · doi:10.1016/j.neunet.2024.106975 · Cited by 6 · see citing works
Multi-Scale Adaptive Graph Learning Based on Multi-Wave EEG Data for Dementia Diagnosis
Wei, J., Jiao, B., Lin, H., Tian, X., Shen, L., & Liu, J. (2025) · Big Data Mining and Analytics · doi:10.26599/bdma.2025.9020015 · Cited by 5 · see citing works
Ouyang, J., Liang, Y., Sun, H., Zhang, X., Chen, J., Liu, G., Liu, Z., & Liu, Y. (2025) · Applied Soft Computing · doi:10.1016/j.asoc.2025.113958 · Cited by 5 · see citing works
Qi, N., Piao, Y., Wang, Q., Li, X., & Wang, Y. (2024) · IEEE Access · doi:10.1109/access.2024.3447901 · Cited by 5 · see citing works
EEG Data Augmentation Method Based on the Gaussian Mixture Model
Liao, C., Zhao, S., Wang, X., Zhang, J., Liao, Y., & Wu, X. (2025) · Mathematics · doi:10.3390/math13050729 · Cited by 6 · see citing works
Kuang, D., Michoski, C., Li, W., & Guo, R. (2023) · Applied Intelligence · doi:10.1007/s10489-023-04561-0 · Cited by 5 · see citing works
Enhancing Brain Age Prediction: A Generative AI Approach for EEG Machine Learning Models
Cook, Z., Sinha, G., Wang, J., Zhao, C., Belacel, N., Doesburg, S. M., Medvedev, G., Ribary, U., Vakorin, V. A., & Xi, P. (2024) · IEEE International Instrumentation and Measurement Technology Conference · doi:10.1109/i2mtc60896.2024.10560863 · Cited by 5 · see citing works
- Authors
- Luo, Y., Zhu, L.-Z., Wan, Z.-Y., & Lu, B.-L.
- Affiliations
- Shanghai Jiao Tong University (CN)
- Corresponding author
- Lu, Bao-Liang
- Venue
- Journal of Neural Engineering
- Publisher
- IOP Publishing
- Published
- 2020-10-01
- Volume
- 17
- Issue
- 5
- Pages
- 056021
- ISSN
- 1741-2552 (ISSN-L)
- 1741-2560
- OpenAlex
- W3083255269
- Cited by
- 162
- References
- 51
- Citations by year
- 2026: 15 · 2025: 28 · 2024: 39 · 2023: 37 · 2022: 33 · 2021: 8 · 2020: 2
- FWCI
- 19.32
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Journal of Neural Engineering (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Natural Science Foundation of China (61673266)
- 111 Project
- National Key Research and Development Program of China (2017YFB1002501)
- SJTU Trans-med Awards Research (WF540162605)
- Fundamental Research Funds for the Central Universities
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Physical Sciences › Computer Science › Signal Processing › Biometric Identification and Security
- Keywords
- EEG-based emotion recognition, data augmentation, deep generative models, variational autoencoder, generative adversarial networks, conditional Wasserstein GAN, data scarcity, power spectral density, differential entropy, affective model, SEED dataset, DEAP dataset
- MeSH terms
- Machine Learning, Algorithms, Electroencephalography, Emotions, Neural Networks, Computer
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