Fused CNN-LSTM deep learning emotion recognition model using electroencephalography signals

References

• Craik A, He Y, Contreras-Vidal JL. Deep learning for electroencephalogram (EEG) classification tasks: a review. J Neural Eng. 2019;16(3):031001.
   PubMed Web of Science ®Google Scholar
• Chao H, Zhi H, Dong L, et al. Recognition of emotions using multichannel EEG data and DBN-GC-based ensemble deep learning framework. Comput Intell Neurosci. 2018;2018:1–11.
   Web of Science ®Google Scholar
• Ramzan M, Dawn S. Learning based classification of Valence Emotion from Electroencephalography (EEG). Int J Neurosci. 2019;129:1085–1093.
   PubMed Web of Science ®Google Scholar
• Wang YT, Nakanishi M, Wang Y, et al. An online brain-computer interface based on SSVEPsmeasured from non-hair-bearing areas. IEEE Trans Neural Syst Rehabil Eng. 2016;25(1):14–21.
   Web of Science ®Google Scholar
• Yaacoub C, Mhanna G, Rihana S. A genetic-based feature selection approach in the identification of left/right hand motor imagery for a brain-computer interface. Brain Sci. 2017;7(12):12.
   PubMed Web of Science ®Google Scholar
• Petrantonakis PC, Hadjileontiadis LJ. Emotion recognition from brain signals using hybrid adaptive filtering and higher order crossings analysis. IEEE Trans Affective Comput. 2010;1(2):81–97.
   Web of Science ®Google Scholar
• Chen P, Zhang J. 2017. Performance comparison of machine learning algorithms for EEG-Signal-based emotion recognition. In: Lintas A, Rovetta S, Verschure PFMJ, Villa AEP, editors. International Conference on Artificial Neural Networks. Cham: Springer; 2017. p. 208–216.
   Google Scholar
• Qazi EUH, Hussain M, Abo Alsamh H, et al. 2019. Automatic Emotion Recognition (AER) System based on Two-LevelEnsemble of Lightweight Deep CNN Models. arXiv preprint arXiv:1904.13234.
   Google Scholar
• Chen JX, Zhang PW, Mao ZJ, et al. Accurate EEG-based emotion recognition on combined features using deep convolutional neural networks. IEEE Access. 2019;7:44317–44328.
   Web of Science ®Google Scholar
• Koelstra S, Muehl C, Soleymani M, et al. DEAP: a database for emotion analysis using physiological signals. IEEE Trans Affect Comput. 2012;3(1):18–31.
   Web of Science ®Google Scholar
• Tripathi S, Acharya S, Sharma R-D, et al. Using deep and convolutional neural networks for accurate emotion classification on DEAP dataset. In Proceedings of Twenty-Ninth AAAI Conference on Innovative Applications of Artificial Intelligence. AAAI, San Francisco, CA, USA, February 2017. pp. 4746–4752.
   Google Scholar
• Li X, Song D-W, Zhang P, et al. Emotion recognition from multi-channel EEG data through convolutional recurrent neural network. Proceedings of IEEE International Conference on Bioinformatics and Biomedicine. Shenzhen, China: IEEE; December 2016. pp. 352–359.
   Google Scholar
• Michielli N, Acharya UR, Molinari F. Cascaded LSTM recurrent neural network for automated sleep stage classification using single-channel EEG signals. Comput Biol Med. 2019;106:71–81.
   PubMed Web of Science ®Google Scholar
• Anzanello MJ, Fogliatto FS. Learning curve models and applications: Literature review and research directions. Int. J. Ind. Ergon. 2011;41(5):573–583.
   Web of Science ®Google Scholar
• James G, Witten D, Hastie T, et al.. An introduction to statistical learning. Vol. 112. New York: Springer; 2013. p. 18.
   Google Scholar
• Zheng WL, Lu BL. Investigating critical frequency bands and channels for EEG-based emotion recognition with deep neuralnetworks. IEEE Trans Auton Mental Develop. 2015;7(3):162–175.
   Google Scholar
• Xia B, Li Q, Jia J, et al. Electrooculogram based sleep stage classification using deep belief network. In 2015 International Joint Conference on Neural Networks (IJCNN), IEEE; 2015. pp. 1–5.
   Google Scholar
• Du LH, Liu W, Zheng WL, et al. Detecting driving fatigue with multimodal deep learning. In 2017 8th International IEEE/EMBS Conference on Neural Engineering (NER). IEEE; 2017. pp. 74–77.
   Google Scholar
• Zhang J, Li S, Wang R. Pattern recognition of momentary mental workload based on multi-channel electrophysiological data and ensemble convolutional neural networks. Front Neurosci. 2017;11:310.
   PubMed Web of Science ®Google Scholar
• Zhu X, Zheng WL, Lu BL, et al. EOG-based drowsiness detection using convolutional neural networks. In 2014 International Joint Conference on Neural Networks (IJCNN). IEEE; July 2014. pp. 128–134.
   Google Scholar
• Längkvist M, Karlsson L, Loutfi A. Sleep stage classification using unsupervised feature learning. Advances in Artificial Neural Systems. 2012;2012:1–9.
   Google Scholar
• Faust O, Hagiwara Y, Hong TJ, et al. Deep learning for healthcare applications based on physiological signals: A review. Comput Methods Programs Biomed. 2018;161:1–13.
   PubMed Web of Science ®Google Scholar