On the Use of Lateralization for Lightweight and Accurate Methodology for EEG Real Time Emotion Estimation Using Gaussian-Process Classifier
| Autor: | José Manuel Ferrández Vicente, José Ramón Álvarez-Sánchez, Eduardo Fernández-Jover, Mikel Val-Calvo, Alejandro Díaz-Morcillo |
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| Rok vydání: | 2019 |
| Předmět: |
medicine.diagnostic_test
Computer science business.industry Speech recognition Robotics 02 engineering and technology Electroencephalography Human–robot interaction DEAP 03 medical and health sciences symbols.namesake 0302 clinical medicine 0202 electrical engineering electronic engineering information engineering symbols medicine Robot 020201 artificial intelligence & image processing Artificial intelligence business OpenBCI Gaussian process Classifier (UML) 030217 neurology & neurosurgery |
| Zdroj: | Understanding the Brain Function and Emotions ISBN: 9783030195908 IWINAC (1) |
| Popis: | Emotional estimation systems based on electroencephalography (EEG) signals are gaining special attention in recent years due to the possibilities they offer. The field of human-robot interactions (HRI) will benefit from a broadened understanding of brain emotional encoding and thus, improve the capabilities of robots to fully engage with the user’s emotional reactions. In this paper, a methodology for real-time emotion estimation aimed for its use in the field of HRI is proposed. The proposed methodology takes advantage of the lateralization produced in brain oscillations during emotional stimuli and the use of meaningful features related to intrinsic EEG patterns. In the validation procedure, both DEAP and SEED databases have been used. A mean performance of 88.34% was obtained using four categories of the valence-arousal space, and 97.1% using three discrete categories; both of them obtained with a Gaussian-Process classifier. This lightweight method could run on inexpensive, portable devices such as the openBCI system. |
| Databáze: | OpenAIRE |
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