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77
10. Kiranyaz, S., Ince, T., Abdeljaber, O., Avci, O., Gabbouj, M.: 1-D convolutional
neural networks for signal processing applications. In: ICASSP, IEEE International
Conference on Acoustics, Speech and Signal Processing - Proceedings, pp. 8360–
8364, May 2019
11. Dwivedi, K., Biswaranjan, K., Sethi, A.: Drowsy driver detection using representation learning. In: IEEE International Advance Computing Conference, IACC, pp.
995–999, February 2014
12. Yu, J., Park, S., Lee, S., Jeon, M.: Driver drowsiness detection using conditionadaptive representation learning framework. IEEE Trans. Intell. Transp. Syst. 20,
4206–4218 (2018)
13. Strmiska, M., Koudelkova, Z.: Analysis of performance metrics using Emotiv
EPOC+. MATEC Web Conf. 210, 4–7 (2018)
14. Laruelo, A., et al.: Hybrid sparse regularization for magnetic resonance spectroscopy. In: IEEE International Conference of Engineering in Medicine and Biology Society (EMBC), pp. 3–7, July 2013
15. Chaari, L., Tourneret, J.-Y., Chaux, C.: Sparse signal recovery using a
Bernouilli generalized gaussian prior. In: European Signal Processing Conference
(EUSIPCO), Nice, France, 31 August–4 September 2015 (2015)
16. Surangsrirat, D., Intarapanich, A.: Analysis of the meditation brainwave from consumer EEG device. In: IEEE SOUTHEASTCON, pp. 1–6, June 2015
17. Sol´ e-Casals, J., et al.: A novel deep learning approach with data augmentation to
classify motor imagery signals. IEEE Access 7, 15945–15954 (2019)
18. Jung, J.J., Youn, Y.C., Camacho, D., Li, G., Lee, C.H.: Deep learning for EEG
data analytics: a survey. Concurr. Comput. (2019)
19. Shaf, A., Ali, T., Farooq, W., Javaid, S., Draz, U., Yasin, S.: Two classes classification using different optimizers in convolutional neural network. In: International
Multi-topic Conference (INMIC), pp. 1–6 (2018)
20. Tafsast, A., Ferroudji, K., Hadjili, M.L., Bouakaz, A., Benoudjit, N.: Automatic
microemboli characterization using convolutional neural networks and radio frequency signals. In: 2018 International Conference on Communications and Electrical Engineering (ICCEE), pp. 1–4, December 2018
21. Reddy, S.V.G., Reddy, K.T., ValliKumari, V.: Optimization of deep learning using
various optimizers, loss functions and dropout. Int. J. Innov. Technol. Explor. Eng
22. Nugraha, B.T., Sarno, R., Asfani, D.A., Igasaki, T., Munawar, M.N.: Classification
of driver fatigue state based on EEG using Emotiv EPOC+. J. Theor. Appl. Inf.
Technol. 86, 347–359 (2016)
23. Sarno, R., Nugraha, B.T., Munawar, M.N.: Real time fatigue-driver detection from
electroencephalography using Emotiv EPOC+. Int. Rev. Comput. Softw. (IRECOS) 11, 214 (2016)
24. Osmalina, R., Rahmatillah, A.: Drowsiness analysis using common spatial pattern and extreme learning machine based on electroencephalogram signal. J. Med.
Signals Sens. 9(2), 130–136 (2019)