Optimization of Real-Time EEG Artifact Removal and Emotion Estimation for Human-Robot Interaction Applications

Journal ar
Frontiers in Computational Neuroscience
  • Volumen: 13
  • Fecha: 26 noviembre 2019
  • ISSN: 16625188
  • Tipo de fuente: Revista
  • DOI: 10.3389/fncom.2019.00080
  • Tipo de documento: Artículo
  • Editorial: Frontiers Media
© Copyright © 2019 Val-Calvo, Álvarez-Sánchez, Ferrández-Vicente and Fernández.Affective human-robot interaction requires lightweight software and cheap wearable devices that could further this field. However, the estimation of emotions in real-time poses a problem that has not yet been optimized. An optimization is proposed for the emotion estimation methodology including artifact removal, feature extraction, feature smoothing, and brain pattern classification. The challenge of filtering artifacts and extracting features, while reducing processing time and maintaining high accuracy results, is attempted in this work. First, two different approaches for real-time electro-oculographic artifact removal techniques are tested and compared in terms of loss of information and processing time. Second, an emotion estimation methodology is proposed based on a set of stable and meaningful features, a carefully chosen set of electrodes, and the smoothing of the feature space. The methodology has proved to perform on real-time constraints while maintaining high accuracy on emotion estimation on the SEED database, both under subject dependent and subject independent paradigms, to test the methodology on a discrete emotional model with three affective states.

Palabras clave del autor

    Palabras clave indexadas

      Detalles de financiación