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Review Article

Deep Learning Techniques for EEG Signal Applications – A Review

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References

  • M. C. Tjepkema-Cloostermans, R. C. de Carvalho, and M. J. van Putten, “Deep learning for detection of focal epileptiform discharges from scalp EEG recordings,” Clin. Neurophys., Vol. 129, no. 10, pp. 2191–6, July 2018. doi: 10.1016/j.clinph.2018.06.024
  • J. Birjandtalab, M. Heydarzadeh, and M. Nourani, “Automated EEG-based epileptic seizure detection using deep neural networks,” in IEEE Conf. Healthcare Informatics (ICHI), Aug. 2017, pp. 552–5.
  • B. Yang, K. Duan, C. Fan, C. Hu, and J. Wang, “Automatic ocular artifacts removal in EEG using deep learning,” Biomed. Signal. Process. Control., Vol. 43, pp. 148–58, May 2018. doi: 10.1016/j.bspc.2018.02.021
  • X. Zhang, L. Yao, Q. Sheng, S. S. Kanhere, T. Gu, and D. Zhang, “Converting your thoughts to texts: Enabling brain typing via deep feature learning of EEG signals,” IEEE International Conference on Pervasive Computing and Communications, Vol. 1709.08820, pp. 1–10, 2018.
  • S. Ding, N. Zhang, X. Xu, L. Guo, and J. Zhang, “Deep extreme learning machine and its application in EEG classification,” Math. Probl. Eng., Vol. 2015, p. 11, 2015.
  • J. A. Mioranda-Correa, and I. Patras, “A multi-task cascaded network for prediction of affect, personality, mood and social context using EEG signals,” in 13th IEEE Conf. Automatic Face & Gesture Recognition (FG 2018), May 2018, pp. 373–80.
  • E. Nurse, B. S. Mashford, A. J. Yepes, I. Kiral-Kornek, S. Harrer, and D. R. Freestone, “Decoding EEG and LFP signals using deep learning: Heading TrueNorth,” in Proceedings of the ACM international Conf. Computing Frontiers, May 2016, pp. 259–66.
  • N. Singh, and S. Dehuri, “Usage of deep learning in epileptic seizure detection through EEG signal,” in Nanoelectronics, Circuits and Communication systems, Vijay Nath and Jyotsna Kumar Mandal, Eds. Singapore: Springer, 2019, pp. 219–28.
  • Y. Hao, H. M. Khoo, N. von Ellenrieder, N. Zazubovits, and J. Gotman, “DeepIED: An epileptic discharge detector for EEG-fMRI based on deep learning,” NeuroImage: Clin., Vol. 17, pp. 962–75, Jul. 2018. doi: 10.1016/j.nicl.2017.12.005
  • A. Antoniades, L. Spyrou, D. Martin-Lopez, A. Valentin, G. Alarcon, S. Sanei, and C. C. Took, “Deep neural architectures for mapping scalp to intracranial EEG,” Int. J. Neural sys., Vol. 28, no. 8, pp. 1850009, Mar. 2018. doi: 10.1142/S0129065718500090
  • Z. Jiao, X. Gao, Y. Wang, J. Li, and H. Xu, “Deep Convolutional neural networks for mental load classification based on EEG data,” Pattern Recognit., Vol. 76, pp. 582–95, Apr. 2018. doi: 10.1016/j.patcog.2017.12.002
  • M. J. Van Putten, J. Hofmeijer, B. J. Ruijter, and M. C. Tjepkema-Cloostermans. “Deep learning for outcome prediction of postanoxic coma,” in EMBEC and NBC. Singapore: Springer, Jun. 2017, pp. 506–9.
  • S. R. Carvalho, I. Cordeiro Filho, D. O. De Resende, A. C. Siravenha, C. De Souza, H. G. Debarba, and R. Boulic, “A deep learning Approach for classification of reaching targets from EEG images,” in 30th SIBGRAPI Conf. Graphics, Patterns and Images (SIBGRAPI), Oct. 2017, pp. 178–84.
  • R. T. Schirrmeister, J. T. Springenberg, L. D. J. Fiederer, M. Glasstetter, K. Eggensperger, M. Tangermann, and T. Ball, “Deep learning with convolutional neural networks for EEG decoding and visualization,” Hum. Brain Mapp., Vol. 38, no. 11, pp. 5391–420, Nov. 2017. doi: 10.1002/hbm.23730
  • J. Behncke, R. T. Schirrmeister, W. Burgard, and T. Ball, “The signature of robot action success in EEG signals of a human observer: Decoding and visualization using deep convolutional neural networks,” in 6th International Conf. Brain-computer Interface (BCI), Jan. 2018, pp. 1–6.
  • M. Völker, R. T. Schirrmeister, L. D. Fiederer, W. Burgard, and T. Ball, “Deep transfer learning for error decoding from non-invasive EEG,” in 6th International Conf. Brain-computer Interface (BCI)s, Jan. 2018, pp. 1–6.
  • I. Ullah, M. Hussain, and H. Aboalsamh, “An automated system for epilepsy detection using EEG brain signals based on deep learning approach,” Expt. Sys. App., Vol. 107, pp. 61–71, Oct. 2018. doi: 10.1016/j.eswa.2018.04.021
  • A. Vilamala, K. H. Madsen, and L. K. Hansen. “Deep convolutional neural networks for interpretable analysis of EEG sleep stage scoring,” in Proceedings of the 2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP), IEEE. 2017.
  • U. R. Acharya, S. L. Oh, Y. Hagiwara, J. H. Tan, H. Adeli, and D. P. Subha, “Automated EEG-based screening of depression using deep convolutional neural network,” Comp. Methods Program Biomed., Vol. 161, pp. 103–13, Jul. 2018. doi: 10.1016/j.cmpb.2018.04.012
  • U. R. Acharya, S. L. Oh, Y. Hagiwara, J. H. Tan, and H. Adeli, “Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals,” Comput. Biol. Med., Vol. 100, pp. 270–8, Sep. 2018. doi: 10.1016/j.compbiomed.2017.09.017
  • J. Zhang, and Y. Wu, “Complex-valued unsupervised convolutional neural networks for sleep stage classification,” Comp. Method Program Biomed., Vol. 164, pp. 181–91, Oct. 2018. doi: 10.1016/j.cmpb.2018.07.015
  • H. Dose, J. S. Møller, H. K. Iversen, and S. Puthusserypady, “An end-to-end deep learning approach to MI-EEG signal classification for BCIs,” Expert. Syst. Appl., Vol. 114, pp. 532–42, Dec. 2018. doi: 10.1016/j.eswa.2018.08.031
  • P. Arnau-Gonzalez, S. Katsigiannis, N. Ramzan, D. Tolson, and M. Arevalillo-Herrez, “ES1D: A deep network for EEG-based subject identification,” in IEEE 17th international Conf. Bioinformatics and Bioengineering (BIBE), Oct. 2017, pp. 81–5.
  • S. L. Oh, Y. Hagiwara, U. Raghavendra, R. Yuvaraj, N. Arunkumar, M. Murugappan, and U. R. Acharya, “A deep learning approach for Parkinson’s disease diagnosis from EEG signals,” Neural Comput. Appl., pp. 1–7, 2018. https://doi.org/10.1007/s00521-018-3689-5
  • H. Zeng, C. Yang, G. Dai, F. Qin, J. Zhang, and W. Kong, “EEG classification of driver mental states by deep learning,” Cogn. Neuro. Dyn., Vol. 12, no. 6, pp. 597–606, 2018. doi: 10.1007/s11571-018-9496-y
  • Y. R. Tabar, and U. Halici, “A novel deep learning approach for classification of EEG motor imagery signals,” J. Neural Eng., Vol. 14, no. 1, p. 016003, Nov. 2016. doi: 10.1088/1741-2560/14/1/016003
  • Z. Yin, and J. Zhang, “Cross-session classification of mental workload levels using EEG and an adaptive deep learning model,” Biomed. Signal Process Cont., Vol. 33, pp. 30–47, Mar. 2017. doi: 10.1016/j.bspc.2016.11.013
  • ΚΜ Tsiouris, V. C. Pezoulas, M. Zervakis, S. Konitsiotis, D. D. Koutsouris, and D. I. Fotiadis, “A long short-term memory deep learning network for the prediction of epileptic seizures using EEG signals,” Comput. Biol. Med., Vol. 99, pp. 24–37, Aug. 2018. doi: 10.1016/j.compbiomed.2018.05.019
  • P. Bashivan, I. Rish, M. Yeasin, and N. Codella. 2015 Learning representations from EEG with deep recurrent-convolutional neural networks ICLR 2016.
  • W. L. Zheng, J. Y. Zhu, Y. Peng, and B. L. Lu, “EEG-based emotion classification using deep belief networks,” in IEEE international Conf. Multimedia and Expo (ICME), Jul. 2014, pp. 1–6.
  • Y. Gao, H. J. Lee, and R. M. Mehmood, “Deep learning of EEG signals for emotion recognition,” in IEEE International Conf. Multimedia and Expo Workshops (ICMEW)s, Jun. 2015, pp. 1–5.
  • U. Orhan, M. Hekim, and M. Ozer, “EEG signals classification using the K-means clustering and a multilayer perceptron neural network model,” Exp. Sys. Appl., Vol. 38, no. 10, pp. 13475–81, Sep. 2011. doi: 10.1016/j.eswa.2011.04.149
  • S. Raghu, and N. Sriraam, “Optimal configuration of multilayer perceptron neural network classifier for recognition of intracranial epileptic seizures,” Exp. Sys. Appl, Vol. 89, pp. 205–21, Dec. 2017. doi: 10.1016/j.eswa.2017.07.029
  • R. Hussein, et al., “Optimized deep neural network architecture for robust detection of epileptic seizures using EEG signals,” Clin. Neurophysiol., Vol. 130, no. 1, pp. 25–37, Jan. 2019. doi: 10.1016/j.clinph.2018.10.010

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