Implementation of EEG Emotion Recognition System Based on Hierarchical Convolutional Neural Networks

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

Going deeper with convolutions

Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298594 · Cited by 48,562 · see citing works

Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion

(2010) · Journal of Machine Learning Research · doi:10.5555/1756006.1953039 · Cited by 4,051 · see citing works

Feature Selection for Classification

Dash, M., & Liu, H. (1997) · Intelligent Data Analysis · doi:10.1016/s1088-467x(97)00008-5 · Cited by 3,594 · see citing works

Investigating Critical Frequency Bands and Channels for EEG-Based Emotion Recognition with Deep Neural Networks

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

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 1,225 · see citing works

Deep belief networks

Hinton, G. E. (2009) · Scholarpedia · doi:10.4249/scholarpedia.5947 · Cited by 907 · see citing works

Differential lateralization for positive and negative emotion in the human brain: EEG spectral analysis

Ahern, G. L., & SCHWARTZ, G. E. R. (1985) · Neuropsychologia · doi:10.1016/0028-3932(85)90081-8 · Cited by 439 · see citing works

Emotion classification based on gamma-band EEG

Li, M., & Lu, B. (2009) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/iembs.2009.5334139 · Cited by 413 · see citing works

Inducing and assessing differentiated emotion-feeling states in the laboratory

Philippot, P. (1993) · Cognition & Emotion · doi:10.1080/02699939308409183 · Cited by 391 · see citing works

Real-Time EEG-Based Emotion Recognition and Its Applications

Liu, Y., Sourina, O., & Nguyen, M. K. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-22336-5_13 · Cited by 273 · see citing works

EEG-based vigilance estimation using extreme learning machines

Shi, L.-C., & Lu, B. (2012) · Neurocomputing · doi:10.1016/j.neucom.2012.02.041 · Cited by 126 · see citing works

A deep learning approach to machine transliteration

Thomas Deselaers, B., Hasan, S., Bender, O., & Ney, H. (2009) · Conference on Machine Translation (WMT) · doi:10.3115/1626431.1626476 · Cited by 120 · see citing works

EEG-Based Classification of Music Appraisal Responses Using Time-Frequency Analysis and Familiarity Ratings

Hadjidimitriou, S., & Hadjileontiadis, L. J. (2013) · IEEE Transactions on Affective Computing · doi:10.1109/t-affc.2013.6 · Cited by 103 · see citing works

A multiwavelet-based time-varying model identification approach for time–frequency analysis of EEG signals

Li, Y., Luo, M.-L., & Li, K. (2016) · Neurocomputing · doi:10.1016/j.neucom.2016.01.062 · Cited by 89 · see citing works

Estimation of task workload from EEG data: New and current tools and perspectives

Kothe, C. A., & Makeig, S. (2011) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/iembs.2011.6091615 · Cited by 64 · see citing works

A survey on deep learning-based non-invasive brain signals: recent advances and new frontiers

Zhang, X., Yao, L., Wang, X., Monaghan, J., McAlpine, D., & Zhang, Y. (2021) · Journal of Neural Engineering · doi:10.1088/1741-2552/abc902 · Cited by 296 · see citing works

Deep learning for human affect recognition: Insights and new developments

Rouast, P. V., Adam, M. T. P., & Chiong, R. (2021) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2018.2890471 · Cited by 243 · see citing works

Performance Comparison of Three Types of Autoencoder Neural Networks

Tan, C. C., & Eswaran, C. (2008) · Asia International Conference on Modelling & Simulation (AMS) · doi:10.1109/ams.2008.105 · Cited by 29 · see citing works

A Comparative Evaluation of AutoEncoder-Based Unsupervised Anomaly Detection Methods Applied on Space Payload

Du, J., Song, L., Zheng, T., et al. (2020) · International Conference on Dependable Systems and Their Applications (DSA) · doi:10.1109/dsa.2019.00041

The Learning Effect of Different Hidden Layers Stacked Autoencoder

Xu, Q., Zhang, C., Zhang, L., et al. (2016) · International Conference on Intelligent Human-Machine Systems and Cybernetics · doi:10.1109/ihmsc.2016.280 · Cited by 21 · see citing works

Combining an Autoencoder and a Variational Autoencoder for Explaining the Machine Learning Model Predictions

Utkin, L. V., DROBINTSEV, P. D., Kovalev, M. S., & Konstantinov, A. V. (2021) · Conference of Open Innovations Association (FRUCT) · doi:10.23919/fruct50888.2021.9347612 · Cited by 5 · see citing works

A Speedy Brain-Computer Interface Combined with Computer Vision

Liu, K., Yu, Y., Liu, Y., & Zhou, Z. (2021) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-981-16-5854-9_16 · Cited by 1 · see citing works