Rede Neural Convolucional e LSTM para Biometria Baseada em EEG no Modo de Identificação
Resumo
Com o avanço da biometria e a necessidade de sistemas de segurança mais robustos, outros tipos de características humanas além das mais utilizadas foram levadas em consideração no desenvolvimento de sistemas biométricos. Uma destas características é o eletroencefalograma (sinais cerebrais). Este trabalho então avalia uma rede neural, cuja arquitetura combina camadas de Redes Neurais Convolucionais e camadas de Long Short-Term Memory (LSTM), em um sistema biométrico no modo de identificação, e utiliza os dados dos 109 indivíduos presentes na base de dados EEG Motor Movement/Imagery Dataset. Ao utilizar um tamanho de janela de 12 seg., um resultado estado-da-arte de 99,7% de acurácia foi atingido, provando a eficiência da metodologia aplicada.Referências
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Obaidat, M. S., Rana, S. P., Maitra, T., Giri, D., and Dutta, S. (2019). Biometric security and internet of things (iot). In Biometric-based physical and cybersecurity systems, pages 477-509. Springer.
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Schons, T., Moreira, G. J. P., Silva, P. H. L., Coelho, V. N., and da S. Luz, E. J. (2017). Convolutional network for eeg-based biometric. In 22nd Iberoamerican Congress on Pattern Recognition, pages 601-608.
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Bobkowska, K., Nagaty, K., and Przyborski, M. (2019). Incorporating iris, fingerprint and face biometric for fraud prevention in e-passports using fuzzy vault. IET Image Processing, 13(13):2516-2528.
Carrión-Ojeda, D., Fonseca-Delgado, R., and Pineda, I. (2021). Analysis of factors that influence the performance of biometric systems based on eeg signals. Expert Systems with Applications, 165:113967.
Damasevicius, R., Maskeliunas, R., Kazanavicius, E., and Wozniak, M. (2018). Combining cryptography with EEG biometrics. Computational Intelligence and Neuroscience, 2018:1867548:1-1867548:11.
Das, R., Maiorana, E., and Campisi, P. (2016). Eeg biometrics using visual stimuli: A longitudinal study. IEEE Signal Processing Letters, 23(3):341-345.
Das, R., Maiorana, E., and Campisi, P. (2017). Visually evoked potential for EEG biometrics using convolutional neural network. In 25th European Signal Processing Conference, EUSIPCO 2017, August 28 September 2, 2017, pages 951-955, Kos, Greece. IEEE.
Goldberger, A. L., Amaral, L. A., Glass, L., Hausdorff, J. M., Ivanov, P. C., Mark, R. G., Mietus, J. E., Moody, G. B., Peng, C.-K., and Stanley, H. E. (2000). Physiobank, physiotoolkit, and physionet: components of a new research resource for complex physiologic signals. circulation, 101(23):e215-e220.
Graves, A., Fernández, S., and Schmidhuber, J. (2005). Bidirectional LSTM networks for improved phoneme classification and recognition. In 15th International Conference on Artificial Neural Networks, pages 799-804.
Jamil, Z., Jamil, A., and Majid, M. (2021). Artifact removal from eeg signals recorded in non-restricted environment. Biocybernetics and Biomedical Engineering, 41(2):503-515.
Jijomon, C. and Vinod, A. (2021). Person-identification using familiar-name auditory evoked potentials from frontal eeg electrodes. Biomedical Signal Processing and Control, 68:102739.
Lee, Y.-Y. and Hsieh, S. (2014). Classifying different emotional states by means of eeg-based functional connectivity patterns. PloS one, 9(4):e95415.
Lin, H. W. and Tegmark, M. (2017). Critical behavior in physics and probabilistic formal languages. Entropy, 19(7):299.
Liu, Y. H. (2018). Feature extraction and image recognition with convolutional neural networks. Journal of Physics: Conference Series, 1087(6):062032.
Lumini, A. and Nanni, L. (2017). Overview of the combination of biometric matchers. Information Fusion, 33:71-85.
Mota, M. R., Silva, P. H., Luz, E. J., Moreira, G. J., Schons, T., Moraes, L. A., and Menotti, D. (2021). A deep descriptor for cross-tasking eeg-based recognition. PeerJ Computer Science, 7:e549.
Obaidat, M. S., Rana, S. P., Maitra, T., Giri, D., and Dutta, S. (2019). Biometric security and internet of things (iot). In Biometric-based physical and cybersecurity systems, pages 477-509. Springer.
Schalk, G., McFarland, D. J., Hinterberger, T., Birbaumer, N., and Wolpaw, J. R. (2004). Bci2000: a general-purpose brain-computer interface (bci) system. IEEE Transactions on biomedical engineering, 51(6):1034-1043.
Schons, T., Moreira, G. J. P., Silva, P. H. L., Coelho, V. N., and da S. Luz, E. J. (2017). Convolutional network for eeg-based biometric. In 22nd Iberoamerican Congress on Pattern Recognition, pages 601-608.
Sherstinsky, A. (2020). Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physica D: Nonlinear Phenomena, 404:132306.
Sun, Y., Lo, F. P.-W., and Lo, B. (2019). Eeg-based user identification system using 1d-convolutional long short-term memory neural networks. Expert Systems with Applications, 125:259-267.
Wu, Y. and Qin, Y. (2022). Machine translation of english speech: Comparison of multiple algorithms. Journal of Intelligent Systems, 31(1):159-167.
Yang, S., Deravi, F., and Hoque, S. (2018). Task sensitivity in eeg biometric recognition. Pattern Analysis and Applications, 21(1):105-117.
Publicado
07/06/2022
Como Citar
FREITAS, Carlos; SILVA, Pedro; MOREIRA, Gladston; LUZ, Eduardo.
Rede Neural Convolucional e LSTM para Biometria Baseada em EEG no Modo de Identificação. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO APLICADA À SAÚDE (SBCAS), 22. , 2022, Teresina.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2022
.
p. 256-267.
ISSN 2763-8952.
DOI: https://doi.org/10.5753/sbcas.2022.222647.
