Reconhecimento de padroes biométricos utilizando máquinas de aprendizado profundo
Resumo
A interface cérebro-computador é um dos campos emergentes da interação homem-computador devido ao seu amplo espectro de aplicações, especialmente as que lidam com a cognição humana. Neste trabalho, a eletroencefalografia (EEG) é usada como dado base para classificar o estado dos olhos baseado em redes Long Short Term Memory (LSTM). Para fins de benchmarking, foi utilizado o conjunto de dados do estado do olho do EEG disponível no repositório de aprendizado de máquina da UCI. Os resultados obtidos indicaram que o modelo é aplicável para a classificação dos dados e que seu desempenho é bom comparado aos modelos mais caros computacionalmente.
Referências
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Hamilton, C. R., Shahryari, S., and Rasheed, K. M. (2015). Eye state prediction from eeg data using boosted rotational forests. In Machine Learning and Applications (ICMLA), 2015 IEEE 14th International Conference on, pages 429–432. IEEE.
Kalchbrenner, N., Danihelka, I., and Graves, A. (2015). Grid long short-term memory. arXiv preprint arXiv:1507.01526.
Kim, Y., Lee, C., and Lim, C. (2016). Computing intelligence approach for an eye state classification with eeg signal in bci. In Software Engineering and Information Techno_x005flogy: Proceedings of the 2015 International Conference on Software Engineering and Information Technology (SEIT2015), pages 265–270. World Scientific.
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Narejo, S., Pasero, E., and Kulsoom, F. (2016). Eeg based eye state classification using deep belief network and stacked autoencoder. International Journal of Electrical and Computer Engineering (IJECE), 6(6):3131–3141.
Northrop, R. B. (2012). Analysis and Application of Analog Electronic Circuits to Bio_x0002_medical Instrumentation, Second Edition. Biomedical engineering series (Boca Raton Fla.). CRC Press, 2nd ed edition.
Rosler, O. and Suendermann, D. (2013). A first step towards eye state prediction using eeg. Proc. of the AIHLS.
Sabancı, K. and Koklu, M. (2015). The classification of eye state by using knn and mlp classification models according to the eeg signals. International Journal of Intelligent Systems and Applications in Engineering, 3(4):127–130.
Wang, T., Guan, S.-U., Man, K. L., and Ting, T. (2014). Eeg eye state identification using incremental attribute learning with time-series classification. Mathematical Problems in Engineering, 2014.
Zaremba, W., Sutskever, I., and Vinyals, O. (2014). Recurrent neural network regularization. arXiv preprint arXiv:1409.2329
Acharya, J. N., Hani, A. J., Cheek, J., Thirumala, P., and Tsuchida, T. N. (2016). Ame?rican clinical neurophysiology society guideline 2: guidelines for standard electrode position nomenclature. The Neurodiagnostic Journal, 56(4):245–252.
Bashivan, P., Rish, I., Yeasin, M., and Codella, N. (2015). Learning representati?ons from eeg with deep recurrent-convolutional neural networks. arXiv preprint ar?Xiv:1511.06448.
Cahn, B. R. and Polich, J. (2006). Meditation states and traits: Eeg, erp, and neuroimaging studies. Psychological bulletin, 132(2):180.
Chollet, F. et al. (2015). Keras. https://keras.io.
Cohen, M. X. (2014). Analyzing neural time series data: theory and practice. MIT Press.
Fonseca, J. P. C. (2016). Fpga implementation of a lstm neural network.
Graves, A. (2012). Supervised Sequence Labelling with Recurrent Neural Networks. Studies in Computational Intelligence 385. Springer-Verlag Berlin Heidelberg, 1 edition.
Greff, K., Srivastava, R. K., Koutn´ık, J., Steunebrink, B. R., and Schmidhuber, J. (2017). Lstm: A search space odyssey. IEEE transactions on neural networks and learning systems.
Hamilton, C. R., Shahryari, S., and Rasheed, K. M. (2015). Eye state prediction from eeg data using boosted rotational forests. In Machine Learning and Applications (ICMLA), 2015 IEEE 14th International Conference on, pages 429–432. IEEE.
Kalchbrenner, N., Danihelka, I., and Graves, A. (2015). Grid long short-term memory. arXiv preprint arXiv:1507.01526.
Kim, Y., Lee, C., and Lim, C. (2016). Computing intelligence approach for an eye state classification with eeg signal in bci. In Software Engineering and Information Techno_x005flogy: Proceedings of the 2015 International Conference on Software Engineering and Information Technology (SEIT2015), pages 265–270. World Scientific.
LeCun, Y., Bengio, Y., and Hinton, G. (2015). Deep learning. Nature, 521(7553):436–444.
Malmivuo, J. and Plonsey, R. (1995). Bioelectromagnetism: principles and applications of bioelectric and biomagnetic fields. Oxford University Press, USA.
Narejo, S., Pasero, E., and Kulsoom, F. (2016). Eeg based eye state classification using deep belief network and stacked autoencoder. International Journal of Electrical and Computer Engineering (IJECE), 6(6):3131–3141.
Northrop, R. B. (2012). Analysis and Application of Analog Electronic Circuits to Bio_x0002_medical Instrumentation, Second Edition. Biomedical engineering series (Boca Raton Fla.). CRC Press, 2nd ed edition.
Rosler, O. and Suendermann, D. (2013). A first step towards eye state prediction using eeg. Proc. of the AIHLS.
Sabancı, K. and Koklu, M. (2015). The classification of eye state by using knn and mlp classification models according to the eeg signals. International Journal of Intelligent Systems and Applications in Engineering, 3(4):127–130.
Wang, T., Guan, S.-U., Man, K. L., and Ting, T. (2014). Eeg eye state identification using incremental attribute learning with time-series classification. Mathematical Problems in Engineering, 2014.
Zaremba, W., Sutskever, I., and Vinyals, O. (2014). Recurrent neural network regularization. arXiv preprint arXiv:1409.2329
Publicado
22/08/2018
Como Citar
SOARES, Gabriel; PRADO, Bruno; SILVA, Gilton; MACEDO, Hendrik; MATOS, Leonardo.
Reconhecimento de padroes biométricos utilizando máquinas de aprendizado profundo. In: ESCOLA REGIONAL DE COMPUTAÇÃO BAHIA, ALAGOAS E SERGIPE (ERBASE), 18. , 2018, Aracaju.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2018
.
p. 298-307.
