Clasificación de señales EEG para aplicaciones BCI de imaginación motora

  1. Omari, Sara 1
  2. Omari, Adil 1
  3. Abderrahim Fichouche, Mohamed 1
  4. Adu-Dakka, Fares J. 2
  1. 1 Universidad Carlos III de Madrid
    info

    Universidad Carlos III de Madrid

    Madrid, España

    ROR https://ror.org/03ths8210

  2. 2 Facultad de Ingeniería, Mondragon Unibertsitatea
Revista:
Jornadas de Automática
  1. Cruz Martín, Ana María (coord.)
  2. Arévalo Espejo, V. (coord.)
  3. Fernández Lozano, Juan Jesús (coord.)

ISSN: 3045-4093

Año de publicación: 2024

Número: 45

Tipo: Artículo

DOI: 10.17979/JA-CEA.2024.45.10957 DIALNET GOOGLE SCHOLAR lock_openAcceso abierto editor

Resumen

EEG signal decoding serves as the foundation for the majority of brain-computer interface studies. A prominent preprocessing technique for these signals involves the use of spatial covariance matrices. This method has gained extensive application in recent years, particularly in EEG signal processing and spatial filtering for BCI motor imagery. Since spatial covariance matrices reside within the Riemannian manifold of Symmetric Positive-Definite matrices, the application of Riemannian geometry has provento be a robust and high-performing approach. In order to interpret brain signals for future applications in medical robotics and control systems, this paper presents a method that projects these covariance matrices from their native Riemannian spaceto multiple tangent spaces, thereby enabling the use of conventional classifiers such as logistic regression and support vector machines. Our results demonstrate that this approach significantly outperforms the single projection method.

Referencias bibliográficas

  • Barachant, A., 2012. Commande robuste d’un effecteur par une interface cerveau machine eeg asynchrone. Ph.D. thesis, Université de Grenoble.
  • Barachant, A., Bonnet, S., Congedo, M., Jutten, C., 2010. Riemannian geometry applied to bci classification. In: International conference on latent variable analysis and signal separation. Springer, pp. 629–636. DOI: 10.1007/978-3-642-15995-478 DOI: https://doi.org/10.1007/978-3-642-15995-4_78
  • Barachant, A., Bonnet, S., Congedo, M., Jutten, C., 2012. Bci signal classification using a riemannian-based kernel. In: ESANN 2012-20th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Michel Verleysen, pp. 97–102.
  • Barachant, A., Bonnet, S., Congedo, M., Jutten, C., 2013. Classification of covariance matrices using a riemannian-based kernel for bci applications. Neurocomputing 112, 172–178. DOI: 10.1016/j.neucom.2012.12.039 DOI: https://doi.org/10.1016/j.neucom.2012.12.039
  • Brunner, C., Leeb, R., M ̈uller-Putz, G., Schl ̈ogl, A., Pfurtscheller, G., 2008. Bci competition 2008–graz data set a. Institute for Knowledge Discovery (Laboratory of Brain-Computer Interfaces), Graz University of Technology 16, 1–6. DOI: 10.21227/katb-zv89
  • Congedo, M., Barachant, A., Bhatia, R., 2017. Riemannian geometry for eeg-based brain-computer interfaces; a primer and a review. Brain-Computer Interfaces 4 (3), 155–174. DOI: 10.1080/2326263X.2017.1297192 DOI: https://doi.org/10.1080/2326263X.2017.1297192
  • Fazli, S., Grozea, C., Dan ́oczy, M., Blankertz, B., Popescu, F., M ̈uller, K.-R., 2009. Subject independent eeg-based bci decoding. Advances in Neural Information Processing Systems 22, 513–521. DOI: https://doi.org/10.1016/j.neunet.2009.06.003
  • Liao, L.-D., Chen, C.-Y., Wang, I.-J., Chen, S.-F., Li, S.-Y., Chen, B.-W., Chang, J.-Y., Lin, C.-T., 2012. Gaming control using a wearable and wire- less eeg-based brain-computer interface device with novel dry foam-based sensors. Journal of neuroengineering and rehabilitation 9 (1), 1–12. DOI: 10.1186/1743-0003-9-5 DOI: https://doi.org/10.1186/1743-0003-9-5
  • Lin, C.-T., Lin, F.-C., Chen, S.-A., Lu, S.-W., Chen, T.-C., Ko, L.-W., 2010. Eeg-based brain-computer interface for smart living environmental auto- adjustment. Journal of Medical and Biological Engineering 30 (4), 237–245. DOI: 10.1109/JSYST.2012.2192756 DOI: https://doi.org/10.1109/JSYST.2012.2192756
  • Omari, S., Omari, A., Abderrahim, M., 2023. Multiple tangent space projection for motor imagery eeg classification. Applied Intelligence 53 (18), 21192–21200. DOI: 10.1007/s10489-023-04551-2 DOI: https://doi.org/10.1007/s10489-023-04551-2
  • Park, J. L., Fairweather, M. M., Donaldson, D. I., 2015. Making the case for mobile cognition: Eeg and sports performance. Neuroscience & Biobeha-vioral Reviews 52, 117–130. DOI: 10.1016/j.neubiorev.2015.02.014 DOI: https://doi.org/10.1016/j.neubiorev.2015.02.014
  • Tang, Z., Li, C., Sun, S., 2017. Single-trial eeg classification of motor imagery using deep convolutional neural networks. Optik 130, 11–18. DOI: 10.1016/j.ijleo.2016.10.117 DOI: https://doi.org/10.1016/j.ijleo.2016.10.117
  • Torres P, E. P., Torres, E. A., Hern ́andez- ́Alvarez, M., Yoo, S. G., et al., 2020. Eeg-based bci emotion recognition: A survey. Sensors 20 (18), 5083. DOI: 10.3390/s20185083 DOI: https://doi.org/10.3390/s20185083
  • Tortora, S., Ghidoni, S., Chisari, C., Micera, S., Artoni, F., 2020. Deep learning-based bci for gait decoding from eeg with lstm recurrent neural network. Journal of Neural Engineering 17 (4), 046011. DOI: 10.1088/1741-2552/ab9842 DOI: https://doi.org/10.1088/1741-2552/ab9842
  • Wang, P., Jiang, A., Liu, X., Shang, J., Zhang, L., 2018. Lstm-based eeg classification in motor imagery tasks. IEEE transactions on neural systems and rehabilitation engineering 26 (11), 2086–2095. DOI: 10.1109/TNSRE.2018.2876129 DOI: https://doi.org/10.1109/TNSRE.2018.2876129
  • Wang, Y.-T., Wang, Y., Jung, T.-P., 2011. A cell-phone-based brain–computer interface for communication in daily life. Journal of neural engineering 8 (2), 025018. DOI: 10.1088/1741-2560/8/2/025018 DOI: https://doi.org/10.1088/1741-2560/8/2/025018
  • Zhang, K., Robinson, N., Lee, S.-W., Guan, C., 2021. Adaptive transfer learning for eeg motor imagery classification with deep convolutional neural network. Neural Networks 136, 1–10. DOI: 10.1016/j.neunet.2020.12.013 DOI: https://doi.org/10.1016/j.neunet.2020.12.013
Fuente de los datos: Dialnet