Análise de Sinais Cerebrais para Detecção de Níveis de Atenção em Jogos Digitais
Resumo
Este estudo propõe o uso de sinais cerebrais para detecção e análise de foco de atenção em 4 jogos utilizando mouse e touchpad. A abordagem utilizada recorre ao dispositivo Bitalino para capturar as ondas cerebrais e decomposições de Wavelets e análises de espectrogramas para detectar diferentes níveis de atenção. Resultados identificaram um baseline médio de referência de 55% e indicaram que jogos sem tempo limite ajudaram a aumentar a atenção dos participantes ao longo do tempo, enquanto outros apresentaram dificuldade em manter o foco, independentemente do dispositivo utilizado. As descobertas destacam a possibilidade de projetar tecnologias interativas que promovam maior engajamento e considerem a dinâmica natural da atenção humana.Referências
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Hassan, R., Hasan, M. S., Hasan, J., Jamader, M. R., Eisenberg, D., and Pias, T. (2020). Machine learning based human attention recognition from brain-eeg signals.
Imah, E. M., Dewi, E. S., and Buditjahjanto, I. G. P. A. (2021). A comparative analysis of machine learning methods for joint attention classification in autism spectrum disorder using electroencephalography brain computer interface. International Journal of Intelligent Engineering Systems, 14(3).
Kaji, Y., Yamamoto, Y., Kawata, J., Morimoto, J., and Fujisawa, S. (2020). Eeg variations during measurement of cognitive functions using biosignal acquisition toolkit. Journal of Robotics and Mechatronics, 32(4):753–760.
Klem, G. H. (1999). The ten-twenty electrode system of the international federation. the international federation of clinical neurophysiology. Electroencephalogr. Clin. Neurophysiol. Suppl., 52:3–6.
MacLeod, C. M. (1991). Half a century of research on the stroop effect: An integrative review. Psychological Bulletin, 109(2):163–203.
Mallat, S. (2008). A Wavelet Tour of Signal Processing: The Sparse Way. Academic Press, 3rd edition.
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Stroop, J. R. (1935). Studies of interference in serial verbal reactions. Journal of Experimental Psychology, 18(6):643–662.
Torrence, C. and Compo, G. P. (1998). A practical guide to wavelet analysis. Bulletin of the American Meteorological Society, 79(1):61–78.
Zyma, I., Tukaev, S., Seleznov, I., Kiyono, K., Popov, A., Chernykh, M., and Shpenkov, O. (2019). Electroencephalograms during mental arithmetic task performance. Data, 4(1).
Batista, D., Plácido da Silva, H., Fred, A., Moreira, C., Reis, M., and Ferreira, H. A. (2019). Benchmarking of the bitalino biomedical toolkit against an established gold standard. Healthcare Technology Letters, 6(2):32–36.
Comon, P. (1994). Independent component analysis, a new concept? Signal Processing, 36(3):287–314.
Feijão, A. R. and Galvão, M. T. G. (2007). Ações de educação em saúde na atenção primária: revelando métodos, técnicas e bases teóricas. Revista da Rede de Enfermagem do Nordeste, 8(2):41–49.
Goldberger, A. L., Amaral, L. A. N., 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 e physionet: Componentes de um novo recurso de pesquisa para sinais fisiológicos complexos. Circulação [Online], 101(23):e215–e220.
Gonzalez, R. and Woods, R. (2009). Processamento Digital De Imagens. ADDISON WESLEY BRA.
Hassan, R., Hasan, M. S., Hasan, J., Jamader, M. R., Eisenberg, D., and Pias, T. (2020). Machine learning based human attention recognition from brain-eeg signals.
Imah, E. M., Dewi, E. S., and Buditjahjanto, I. G. P. A. (2021). A comparative analysis of machine learning methods for joint attention classification in autism spectrum disorder using electroencephalography brain computer interface. International Journal of Intelligent Engineering Systems, 14(3).
Kaji, Y., Yamamoto, Y., Kawata, J., Morimoto, J., and Fujisawa, S. (2020). Eeg variations during measurement of cognitive functions using biosignal acquisition toolkit. Journal of Robotics and Mechatronics, 32(4):753–760.
Klem, G. H. (1999). The ten-twenty electrode system of the international federation. the international federation of clinical neurophysiology. Electroencephalogr. Clin. Neurophysiol. Suppl., 52:3–6.
MacLeod, C. M. (1991). Half a century of research on the stroop effect: An integrative review. Psychological Bulletin, 109(2):163–203.
Mallat, S. (2008). A Wavelet Tour of Signal Processing: The Sparse Way. Academic Press, 3rd edition.
PLUX Wireless Biosignals, S. (2021). BITalino (r)evolution User Manual. Available at [link].
Python Core Team (2019). Python: A dynamic, open source programming language. Python Software Foundation. Python version 3.8.
Ramos, R., Valdez-Salas, B., Zlatev, R., Wiener, M. S., and Rull, J. M. B. (2017). The discrete wavelet transform and its application for noise removal in localized corrosion measurements. International Journal of Corrosion, 2017.
Reitan, R. M. (1958). Validity of the Trail Making Test as an indicator of organic brain damage, volume 8. Perceptual and Motor Skills.
Smith, A. (1982). Symbol Digit Modalities Test: Manual. Western Psychological Services.
Stroop, J. R. (1935). Studies of interference in serial verbal reactions. Journal of Experimental Psychology, 18(6):643–662.
Torrence, C. and Compo, G. P. (1998). A practical guide to wavelet analysis. Bulletin of the American Meteorological Society, 79(1):61–78.
Zyma, I., Tukaev, S., Seleznov, I., Kiyono, K., Popov, A., Chernykh, M., and Shpenkov, O. (2019). Electroencephalograms during mental arithmetic task performance. Data, 4(1).
Publicado
09/06/2025
Como Citar
SILVA, Carla Estefany Caetano; TREVISAN, Daniela Gorski; SAADE, Débora Christina Muchaluat.
Análise de Sinais Cerebrais para Detecção de Níveis de Atenção em Jogos Digitais. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO APLICADA À SAÚDE (SBCAS), 25. , 2025, Porto Alegre/RS.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 557-568.
ISSN 2763-8952.
DOI: https://doi.org/10.5753/sbcas.2025.7576.
