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
Revue:
Jornadas de Automática
  1. Cruz Martín, Ana María (coord.)
  2. Arévalo Espejo, V. (coord.)
  3. Fernández Lozano, Juan Jesús (coord.)

ISSN: 3045-4093

Année de publication: 2024

Número: 45

Type: Article

DOI: 10.17979/JA-CEA.2024.45.10957 DIALNET GOOGLE SCHOLAR lock_openAccès ouvert editor

Résumé

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.

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