Impacto da Taxa de Amostragem em Redes Neurais Convolucionais para BCIs baseadas em SSVEP
Resumo
Interfaces Cérebro-Computador (BCIs) baseadas em Potenciais Evocados Visuais de Regime Permanente (SSVEPs) têm mostrado grande potencial para aplicações que auxiliam na comunicação e controle de dispositivos para indivíduos com defici ências motoras. Recentemente, Redes Neurais Convolucionais (CNNs) melhoraram significativamente a precisão da detecção de SSVEPs, aprimorando a interação em tempo real nos sistemas BCI. Um fator importante que pode influenciar o processamento de sinais em uma BCI é a taxa de amostragem, que impacta tanto a resolução do sinal quanto a eficiência computacional. Este estudo investiga o impacto de diferentes taxas de amostragem na abordagem CNN para BCIs baseadas em SSVEPs. A arquitetura compacta EEGNet, bem estabelecida, foi utilizada para avaliar três taxas de amostragem: 256, 128 e 64 Hz. A análise foi conduzida utilizando um conjunto de dados EEG contendo 12 estímulos visuais, selecionados de uma faixa de frequências comumente empregada em sistemas BCI (9,25 a 14,75 Hz, com um passo de 0,5 Hz). Os resultados indicam que uma taxa de amostragem de 64 Hz proporciona uma redução significativa no custo computacional e no uso de memória, enquanto mantém a acurácia da classificação. Esses achados demonstram que a escolhaótima da taxa de amostragem pode tornar a CNN mais eficiente, fornecendo insights valiosos para o design de BCIs baseadas em SSVEPs mais otimizadas para aplicações práticas.
Palavras-chave:
ICC, SSVEP, Frequência de Amostragem
Referências
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A. Floriano, D. Delisle-Rodriguez, P. F. Diez, and T. F. Bastos-Filho, “Assessment of high-frequency steady-state visual evoked potentials from below-the-hairline areas for a brain-computer interface based on depthof-field,” Computer methods and programs in biomedicine, vol. 184, p. 105271, 2020.
A. Floriano, V. L. Carmona, P. F. Diez, and T. F. Bastos-Filho, “A study of ssvep from below-the-hairline areas in low-, medium-, and high-frequency ranges,” Research on Biomedical Engineering, vol. 35, pp. 71–76, 2019.
X. Gu, Z. Cao, A. Jolfaei, P. Xu, D. Wu, T.-P. Jung, and C.-T. Lin, “Eeg-based brain-computer interfaces (bcis): A survey of recent studies on signal sensing technologies and computational intelligence approaches and their applications,” IEEE/ACM transactions on computational biology and bioinformatics, vol. 18, no. 5, pp. 1645–1666, 2021.
B. Allison, T. Luth, D. Valbuena, A. Teymourian, I. Volosyak, and A. Graser, “Bci demographics: How many (and what kinds of) people can use an ssvep bci?” IEEE transactions on neural systems and rehabilitation engineering, vol. 18, no. 2, pp. 107–116, 2010.
C. Guger, B. Z. Allison, B. Großwindhager, R. Prückl, C. Hintermüller, C. Kapeller, M. Bruckner, G. Krausz, and G. Edlinger, “How many people could use an ssvep bci?” Frontiers in neuroscience, vol. 6, p. 169, 2012.
H. Rivera-Flor, D. Gurve, A. Floriano, D. Delisle-Rodriguez, R. Mello, and T. Bastos-Filho, “Cca-based compressive sensing for ssvep-based brain-computer interfaces to command a robotic wheelchair,” IEEE Transactions on Instrumentation and Measurement, vol. 71, pp. 1–10, 2022.
Z. Al-Qaysi, A. Albahri, M. Ahmed, R. A. Hamid, M. Alsalem, O. Albahri, A. Alamoodi, R. Z. Homod, G. G. Shayea, and A. M. Duhaim, “A comprehensive review of deep learning power in steady-state visual evoked potentials,” Neural Computing and Applications, pp. 1–24, 2024.
B. J. Edelman, S. Zhang, G. Schalk, P. Brunner, G. Müller-Putz, C. Guan, and B. He, “Non-invasive brain-computer interfaces: State of the art and trends,” IEEE Reviews in Biomedical Engineering, 2024.
A. Craik, Y. He, and J. L. Contreras-Vidal, “Deep learning for electroencephalogram (eeg) classification tasks: a review,” Journal of neural engineering, vol. 16, no. 3, p. 031001, 2019.
Y. Roy, H. Banville, I. Albuquerque, A. Gramfort, T. H. Falk, and J. Faubert, “Deep learning-based electroencephalography analysis: a systematic review,” Journal of neural engineering, vol. 16, no. 5, p. 051001, 2019.
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M. Teplan et al., “Fundamentals of eeg measurement,” Measurement science review, vol. 2, no. 2, pp. 1–11, 2002.
M. Weiergräber, A. Papazoglou, K. Broich, and R. Müller, “Sampling rate, signal bandwidth and related pitfalls in eeg analysis,” Journal of neuroscience methods, vol. 268, pp. 53–55, 2016.
M. Nakanishi, Y. Wang, Y.-T. Wang, and T.-P. Jung, “A comparison study of canonical correlation analysis based methods for detecting steady-state visual evoked potentials,” PloS one, vol. 10, no. 10, p. e0140703, 2015.
A. Floriano, P. F. Diez, and T. Freire Bastos-Filho, “Evaluating the influence of chromatic and luminance stimuli on ssveps from behindthe-ears and occipital areas,” Sensors, vol. 18, no. 2, p. 615, 2018.
N. Waytowich, V. J. Lawhern, J. O. Garcia, J. Cummings, J. Faller, P. Sajda, and J. M. Vettel, “Compact convolutional neural networks for classification of asynchronous steady-state visual evoked potentials,” Journal of neural engineering, vol. 15, no. 6, p. 066031, 2018.
G. Menghani, “Efficient deep learning: A survey on making deep learning models smaller, faster, and better,” ACM Computing Surveys, vol. 55, no. 12, pp. 1–37, 2023.
Y. Liu, X. Jiang, T. Cao, F. Wan, P. U. Mak, P.-I. Mak, and M. I. Vai, “Implementation of ssvep based bci with emotiv epoc,” in 2012 IEEE International Conference on Virtual Environments Human-Computer Interfaces and Measurement Systems (VECIMS) Proceedings. IEEE, 2012, pp. 34–37.
Y.-P. Lin, Y. Wang, and T.-P. Jung, “Assessing the feasibility of online ssvep decoding in human walking using a consumer eeg headset,” Journal of neuroengineering and rehabilitation, vol. 11, no. 1, p. 119, 2014.
B. Wittevrongel and M. M. Van Hulle, “Frequency-and phase encoded ssvep using spatiotemporal beamforming,” PloS one, vol. 11, no. 8, p. e0159988, 2016.
B. Saha, R. Samanta, S. K. Ghosh, and R. B. Roy, “Efficiency redefined: Impact of reducing data acquisition rate for optimized tinyml in resourceconstrained iot devices,” in 2025 17th International Conference on COMmunication Systems and NETworks (COMSNETS). IEEE, 2025, pp. 842–846.
A. Floriano, D. Delisle-Rodriguez, P. F. Diez, and T. F. Bastos-Filho, “Assessment of high-frequency steady-state visual evoked potentials from below-the-hairline areas for a brain-computer interface based on depthof-field,” Computer methods and programs in biomedicine, vol. 184, p. 105271, 2020.
A. Floriano, V. L. Carmona, P. F. Diez, and T. F. Bastos-Filho, “A study of ssvep from below-the-hairline areas in low-, medium-, and high-frequency ranges,” Research on Biomedical Engineering, vol. 35, pp. 71–76, 2019.
X. Gu, Z. Cao, A. Jolfaei, P. Xu, D. Wu, T.-P. Jung, and C.-T. Lin, “Eeg-based brain-computer interfaces (bcis): A survey of recent studies on signal sensing technologies and computational intelligence approaches and their applications,” IEEE/ACM transactions on computational biology and bioinformatics, vol. 18, no. 5, pp. 1645–1666, 2021.
B. Allison, T. Luth, D. Valbuena, A. Teymourian, I. Volosyak, and A. Graser, “Bci demographics: How many (and what kinds of) people can use an ssvep bci?” IEEE transactions on neural systems and rehabilitation engineering, vol. 18, no. 2, pp. 107–116, 2010.
C. Guger, B. Z. Allison, B. Großwindhager, R. Prückl, C. Hintermüller, C. Kapeller, M. Bruckner, G. Krausz, and G. Edlinger, “How many people could use an ssvep bci?” Frontiers in neuroscience, vol. 6, p. 169, 2012.
H. Rivera-Flor, D. Gurve, A. Floriano, D. Delisle-Rodriguez, R. Mello, and T. Bastos-Filho, “Cca-based compressive sensing for ssvep-based brain-computer interfaces to command a robotic wheelchair,” IEEE Transactions on Instrumentation and Measurement, vol. 71, pp. 1–10, 2022.
Z. Al-Qaysi, A. Albahri, M. Ahmed, R. A. Hamid, M. Alsalem, O. Albahri, A. Alamoodi, R. Z. Homod, G. G. Shayea, and A. M. Duhaim, “A comprehensive review of deep learning power in steady-state visual evoked potentials,” Neural Computing and Applications, pp. 1–24, 2024.
B. J. Edelman, S. Zhang, G. Schalk, P. Brunner, G. Müller-Putz, C. Guan, and B. He, “Non-invasive brain-computer interfaces: State of the art and trends,” IEEE Reviews in Biomedical Engineering, 2024.
A. Craik, Y. He, and J. L. Contreras-Vidal, “Deep learning for electroencephalogram (eeg) classification tasks: a review,” Journal of neural engineering, vol. 16, no. 3, p. 031001, 2019.
Y. Roy, H. Banville, I. Albuquerque, A. Gramfort, T. H. Falk, and J. Faubert, “Deep learning-based electroencephalography analysis: a systematic review,” Journal of neural engineering, vol. 16, no. 5, p. 051001, 2019.
A. Akan and L. F. Chaparro, Signals and systems using MATLAB®. Elsevier, 2024.
M. Teplan et al., “Fundamentals of eeg measurement,” Measurement science review, vol. 2, no. 2, pp. 1–11, 2002.
M. Weiergräber, A. Papazoglou, K. Broich, and R. Müller, “Sampling rate, signal bandwidth and related pitfalls in eeg analysis,” Journal of neuroscience methods, vol. 268, pp. 53–55, 2016.
M. Nakanishi, Y. Wang, Y.-T. Wang, and T.-P. Jung, “A comparison study of canonical correlation analysis based methods for detecting steady-state visual evoked potentials,” PloS one, vol. 10, no. 10, p. e0140703, 2015.
A. Floriano, P. F. Diez, and T. Freire Bastos-Filho, “Evaluating the influence of chromatic and luminance stimuli on ssveps from behindthe-ears and occipital areas,” Sensors, vol. 18, no. 2, p. 615, 2018.
N. Waytowich, V. J. Lawhern, J. O. Garcia, J. Cummings, J. Faller, P. Sajda, and J. M. Vettel, “Compact convolutional neural networks for classification of asynchronous steady-state visual evoked potentials,” Journal of neural engineering, vol. 15, no. 6, p. 066031, 2018.
G. Menghani, “Efficient deep learning: A survey on making deep learning models smaller, faster, and better,” ACM Computing Surveys, vol. 55, no. 12, pp. 1–37, 2023.
Y. Liu, X. Jiang, T. Cao, F. Wan, P. U. Mak, P.-I. Mak, and M. I. Vai, “Implementation of ssvep based bci with emotiv epoc,” in 2012 IEEE International Conference on Virtual Environments Human-Computer Interfaces and Measurement Systems (VECIMS) Proceedings. IEEE, 2012, pp. 34–37.
Y.-P. Lin, Y. Wang, and T.-P. Jung, “Assessing the feasibility of online ssvep decoding in human walking using a consumer eeg headset,” Journal of neuroengineering and rehabilitation, vol. 11, no. 1, p. 119, 2014.
B. Wittevrongel and M. M. Van Hulle, “Frequency-and phase encoded ssvep using spatiotemporal beamforming,” PloS one, vol. 11, no. 8, p. e0159988, 2016.
B. Saha, R. Samanta, S. K. Ghosh, and R. B. Roy, “Efficiency redefined: Impact of reducing data acquisition rate for optimized tinyml in resourceconstrained iot devices,” in 2025 17th International Conference on COMmunication Systems and NETworks (COMSNETS). IEEE, 2025, pp. 842–846.
Publicado
22/10/2025
Como Citar
FLORIANO, Alan; MALINOWSKI, Erich Lacerda; PEIXE FILHO, Jair da Silva; FIGUEIREDO, Gregory Vinícius Conor; SILVA, Paulo Ricardo de Souza; COELHO, Jailton Junior de Sousa.
Impacto da Taxa de Amostragem em Redes Neurais Convolucionais para BCIs baseadas em SSVEP. In: CONGRESSO LATINO-AMERICANO DE SOFTWARE LIVRE E TECNOLOGIAS ABERTAS (LATINOWARE), 22. , 2025, Foz do Iguaçu/PR.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 362-367.
DOI: https://doi.org/10.5753/latinoware.2025.16448.
