Uma Abordagem para Classificação de Fadiga Mental baseada em Sinais de Eletroencefalografia (EEG)
Resumo
A complexidade da análise da fadiga mental em pessoas saudáveis é evidenciada pela ausência de pertubações abruptas no sinal de eletroencefalografia e pela singularidade e variabilidade do perfil cognitivo de cada indivíduo. Identificar esse tipo de estado mental requer a análise de fatores que o caracterize, como o comportamento das bandas de frequência e das regiões cerebrais. Este trabalho propõe classificar a fadiga mental a partir da análise de bandas de frequência e razões dessas bandas em dois modelos de aprendizado de máquina: Rede Neural Perceptron de Múltiplas camadas e Redes Neurais auto-associativas encadeadas. Três frequências e quatro razões foram calculadas a partir dos dados eletroencefalográficos em termos de densidade de energia espectral: α, β, θ, e as razões θ/α, (α+θ)/β, β/α e (α+θ)/(α+ β). Propõe-se também uma estratégia para seleção de canais baseada na significância estatística de Wilcoxon entre as amostras do dado normal e fadigado. Além disso, emprega-se a normalização do vetor de características de forma a reduzir a variabilidade dos dados e melhorar a caracterização dos estados. Os testes mostram que o uso da normalização aumenta efetivamente a acurácia da classificação, independente do modelo utilizado. A seleção de canais reduziu a quantidade de sensores de 30 para 11 e impactou levemente a acurácia dos modelos. A acurácia máxima de 99, 97% foi atingida quando usados dados normalizados com seleção de canais, treinados com Redes Neurais auto-associativas.
Palavras-chave:
Eletroencefalografia, Redes neurais artificiais, Fadiga, Seleção de características
Referências
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Dimitrakopoulos, G. N., Kakkos, I., Dai, Z., Wang, H., Sgarbas, K., Thakor, N., Bezerianos, A., and SUN, Y. Functional connectivity analysis of mental fatigue reveals different network topological alterations between driving and vigilance tasks. IEEE Transactions on Neural Systems and Rehabilitation Engineering 4320 (c): 1–10, 2018.
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Li, J., Lim, J., Chen, Y., Wong, K., Thakor, N., Bezerianos, A., and Sun, Y. Mid-Task Break Improves Global Integration of Functional Connectivity in Lower Alpha Band. Frontiers in Human Neuroscience 10 (June): 1–12, 2016.
Liu, Y. T., Wu, S. L., Chou, K. P., Lin, Y. Y., Lu, J., Zhang, G., Lin, W. C., and Lin, C. T. Driving fatigue prediction with pre-event electroencephalography (EEG) via a recurrent fuzzy neural network. 2016 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2016, 2016.
Min, J., Wang, P., and Hu, J. Driver fatigue detection through multiple entropy fusion analysis in an EEG-based system. PLOS ONE 12 (12): e0188756, dec, 2017.
Pyun, H. and Kim, J. A study on the effect of emotion-evoking advertisement with eeg analysis. In Proceedings of 2000 Joint Conference of KIIE and KORMS, KIIE and KORMS, Seoul. Vol. 413416, 2000.
Schier, M. A. Changes in eeg alpha power during simulated driving: a demonstration. International Journal of Psychophysiology 37 (2): 155–162, 2000.
Scott G. Paris, A. H. P. Classroom applications of research on self-regulated learning. Educational Psychologist 36 (2): 89–101, 2001.
Siravenha, A. C. and Carvalho, S. R. Plant classification from leaf textures. In Digital Image Computing: Techniques and Applications (DICTA), 2016 International Conference on. IEEE, pp. 1–8, 2016.
Wascher, E., Rasch, B., Sunger, J., Hoffmann, S., Schneider, D., Rinkenauer, G., Heuer, H., and Gutberlet, I. Frontal theta activity reflects distinct aspects of mental fatigue. Biological Psychology 96 (1): 57–65, 2014.
Brookhuis, K. A. and De Waard, D. The use of psychophysiology to assess driver status. Ergonomics 36 (9): 1099–1110, 1993.
Cajochen, C., Brunner, D. P., Krauchi, K., Graw, P., and Wirz-Justice, A. Power density in theta/alpha frequencies of the waking eeg progressively increases during sustained wakefulness. Sleep 18 (10): 890–894, 1995.
Chalder, T., Berelowitz, G., Pawlikowska, T., Watts, L., Wessely, S., Wright, D., and Wallace, E. Development of a fatigue scale. Journal of psychosomatic research 37 (2): 147–153, 1993.
Cheron, G., Petit, G., Cheron, J., Leroy, A., Cebolla, A., Cevallos, C., Petieau, M., Hoellinger, T., Zarka, D., Clarinval, A.-M., et al. Brain oscillations in sport: toward eeg biomarkers of performance. Frontiers in psychology vol. 7, pp. 246, 2016.
Dimitrakopoulos, G. N., Kakkos, I., Dai, Z., Wang, H., Sgarbas, K., Thakor, N., Bezerianos, A., and SUN, Y. Functional connectivity analysis of mental fatigue reveals different network topological alterations between driving and vigilance tasks. IEEE Transactions on Neural Systems and Rehabilitation Engineering 4320 (c): 1–10, 2018.
Eoh, H. J., Chung, M. K., and Kim, S.-H. Electroencephalographic study of drowsiness in simulated driving with sleep deprivation. International Journal of Industrial Ergonomics 35 (4): 307–320, 2005.
Helton, W. S. and Russell, P. N. Working memory load and the vigilance decrement. Experimental Brain Research 212 (3): 429–437, 2011.
Hinton, G. E. and Zemel, R. S. Autoencoders, minimum description length and helmholtz free energy. In Advances in neural information processing systems. pp. 3–10, 1994.
Jap, B. T., Lal, S., Fischer, P., and Bekiaris, E. Using eeg spectral components to assess algorithms for detecting fatigue. Expert Systems with Applications 36 (2): 2352–2359, 2009.
Kohavi, R. A study of cross-validation and bootstrap for accuracy estimation and model selection. In Proceedings of the 14th International Joint Conference on Artificial Intelligence - Volume 2. IJCAI’95. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, pp. 1137–1143, 1995.
Li, J., Lim, J., Chen, Y., Wong, K., Thakor, N., Bezerianos, A., and Sun, Y. Mid-Task Break Improves Global Integration of Functional Connectivity in Lower Alpha Band. Frontiers in Human Neuroscience 10 (June): 1–12, 2016.
Liu, Y. T., Wu, S. L., Chou, K. P., Lin, Y. Y., Lu, J., Zhang, G., Lin, W. C., and Lin, C. T. Driving fatigue prediction with pre-event electroencephalography (EEG) via a recurrent fuzzy neural network. 2016 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2016, 2016.
Min, J., Wang, P., and Hu, J. Driver fatigue detection through multiple entropy fusion analysis in an EEG-based system. PLOS ONE 12 (12): e0188756, dec, 2017.
Pyun, H. and Kim, J. A study on the effect of emotion-evoking advertisement with eeg analysis. In Proceedings of 2000 Joint Conference of KIIE and KORMS, KIIE and KORMS, Seoul. Vol. 413416, 2000.
Schier, M. A. Changes in eeg alpha power during simulated driving: a demonstration. International Journal of Psychophysiology 37 (2): 155–162, 2000.
Scott G. Paris, A. H. P. Classroom applications of research on self-regulated learning. Educational Psychologist 36 (2): 89–101, 2001.
Siravenha, A. C. and Carvalho, S. R. Plant classification from leaf textures. In Digital Image Computing: Techniques and Applications (DICTA), 2016 International Conference on. IEEE, pp. 1–8, 2016.
Wascher, E., Rasch, B., Sunger, J., Hoffmann, S., Schneider, D., Rinkenauer, G., Heuer, H., and Gutberlet, I. Frontal theta activity reflects distinct aspects of mental fatigue. Biological Psychology 96 (1): 57–65, 2014.
Publicado
22/10/2018
Como Citar
FERREIRA, Mylena N. M. R.; SIRAVENHA, Ana C. Q.; CARVALHO, Schubert R.; GOMES, Bruno D.; ZAMPOLO, Ronaldo F.; CASTRO, Agostinho S.; CASTRO, Adriana R. G..
Uma Abordagem para Classificação de Fadiga Mental baseada em Sinais de Eletroencefalografia (EEG). In: SYMPOSIUM ON KNOWLEDGE DISCOVERY, MINING AND LEARNING (KDMILE), 6. , 2018, São Paulo/SP.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2018
.
p. 73-80.
ISSN 2763-8944.
DOI: https://doi.org/10.5753/kdmile.2018.27387.
