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Original Research ARTICLE Provisionally accepted The full-text will be published soon. Notify me

Front. Comput. Neurosci. | doi: 10.3389/fncom.2019.00080

Optimization of real-time EEG artifact removal and emotion estimation for human-robot interaction applications

  • 1Universidad Politécnica de Cartagena, Spain
  • 2National University of Distance Education (UNED), Spain
  • 3Department of Electronics, Computer Technology and Projects, Polytechnic University of Cartagena, Spain
  • 4Institute of Bioengineering, Miguel Hernández University, Spain

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

Keywords: Real-time, EEG, emotion estimation, HRI, Artifact Removal (AR)

Received: 30 Jun 2019; Accepted: 08 Nov 2019.

Copyright: © 2019 Calvo, Álvarez-Sánchez, Ferrandez and Fernandez. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence:
PhD. Mikel V. Calvo, Universidad Politécnica de Cartagena, Cartagena, Spain,
Dr. José Ramón Álvarez-Sánchez, National University of Distance Education (UNED), Madrid, 28015, Madrid, Spain,