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Cited by 12EEG and Brain-Computer InterfacesMuscle activation and electromyography studiesEmotion and Mood Recognition

The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.

Cross-Subject EEG Signal Classification with Deep Neural Networks Applied to Motor Imagery

Riyad, Mouad, Khalil, Mohammed, Adib, Abdellah·2019·Lecture Notes in Computer Science
DOI 10.1007/978-3-030-22885-9_12
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Crossref · 10.1007/978-3-030-22885-9_12

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Citations

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Show all 5 references

Research works printed but not linked: 23

  1. Baldi, P., Sadowski, P.J.: Understanding dropout. In: Burges, C.J.C., Bottou, L., Welling, M., Ghahramani, Z., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems 26, vol. 00125, pp. 2814–2822. Curran Associates, Inc. (2013)
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  11. Mao, W., Zhu, J., Li, X., Zhang, X., Sun, S.: Resting state EEG based depression recognition research using deep learning method. In: Wang, S., Yamamoto, V., Su, J., Yang, Y., Jones, E., Iasemidis, L., Mitchell, T. (eds.) BI 2018. LNCS (LNAI), vol. 11309, pp. 329–338. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-05587-5_31 · DOI
  12. McFarland, D.J., Wolpaw, J.R.: Brain-computer interfaces for communication and control. Commun. ACM 54(5), 60 (2011). https://doi.org/10.1145/1941487.1941506 . 06351 · DOI
  13. Nijholt, A.: BCI for games: a ‘state of the art’ survey. In: Stevens, S.M., Saldamarco, S.J. (eds.) ICEC 2008. LNCS, vol. 5309, pp. 225–228. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-89222-9_29 · DOI
  14. Pascanu, R., Mikolov, T., Bengio, Y.: On the difficulty of training Recurrent Neural Networks. arXiv:1211.5063 [cs], November 2012 · arXiv
  15. Ramadan, R.A., Vasilakos, A.V.: Brain computer interface: control signals review. Neurocomputing 223, 26–44 (2017). https://doi.org/10.1016/j.neucom.2016.10.024 · DOI
  16. Schirrmeister, R.T., Gemein, L., Eggensperger, K., Hutter, F., Ball, T.: Deep learning with convolutional neural networks for decoding and visualization of EEG pathology. arXiv:1708.08012 [cs, stat], August 2017 · DOI
  17. Smith, S.L., Kindermans, P.J., Ying, C., Le, Q.V.: Don’t decay the learning rate, increase the batch size. arXiv:1711.00489 [cs, stat], November 2017 · arXiv
  18. Sun, M., Wang, F., Min, T., Zang, T., Wang, Y.: Prediction for high risk clinical symptoms of epilepsy based on deep learning algorithm. IEEE Access 1–1 (2018). https://doi.org/10.1109/ACCESS.2018.2883562 · DOI
  19. Tan, C., Sun, F., Zhang, W., Chen, J., Liu, C.: Multimodal classification with deep convolutional-recurrent neural networks for electroencephalography. arXiv:1807.10641 [cs], July 2018 · DOI
  20. Tan, C., Sun, F., Zhang, W., Chen, J., Liu, C.: Multimodal classification with deep convolutional-recurrent neural networks for electroencephalography. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, E.S. (eds.) ICONIP 2017. LNCS, vol. 10635, pp. 767–776. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-70096-0_78 · DOI
  21. Tangermann, M., et al.: Review of the BCI competition IV. Front. Neurosci. 6, (2012). https://doi.org/10.3389/fnins.2012.00055 · DOI · Paper page
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  23. Wang, P., Jiang, A., Liu, X., Shang, J., Zhang, L.: LSTM-based EEG classification in motor imagery tasks. IEEE Trans. Neural Syst. Rehabil. Eng. 26(11), 2086–2095 (2018). https://doi.org/10.1109/TNSRE.2018.2876129 · DOI

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Metadata

Authors
Riyad, M., Khalil, M., & Adib, A.
Corresponding author
Riyad, Mouad
ORCID
Venue
Lecture Notes in Computer Science
Publisher
Springer International Publishing
Published
2019
Pages
124-139
ISSN
  • 0302-9743 (ISSN-L)
  • 1611-3349
DOI
10.1007/978-3-030-22885-9_12
OpenAlex
W2951451556
Cited by
12
References
5
Citations by year
2024: 2 · 2023: 3 · 2022: 1 · 2021: 2 · 2020: 4
FWCI
3.02
Type
conference paper
Language
en
Open access
closed
Licence
www.springer.com/tdm (tdm)
Locations
Lecture notes in computer science (publishedVersion)
Topics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
  • Physical Sciences › Engineering › Biomedical Engineering › Muscle activation and electromyography studies
  • Physical Sciences › Computer Science › Artificial Intelligence › Emotion and Mood Recognition
Keywords
motor imagery, sensorimotor rhythm, EEG classification, cross-subject classification, brain-computer interface, deep neural network, temporal convolution, spatial convolution, recurrent layers, dropout regularization, EEGNet

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