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EEG-based emotion recognition with deep convolutional neural networks by Mehmet Akif Ozdemir; Murside Degirmenci; Elif Izci; Aydin Akan is a Medicine article available to read on EtoBox.

What is EEG-based emotion recognition with deep convolutional neural networks about?

## Abstract The emotional state of people plays a key role in physiological and behavioral human interaction. Emotional state analysis entails many fields such as neuroscience, cognitive sciences, and biomedical engineering because the parameters of interest contain the complex neuronal activities of the brain. Electroencephalogram (EEG) signals are processed to communicate brain signals with external systems and make predictions over emotional states. This paper proposes a novel method for emotion recognition based on deep convolutional neural networks (CNNs) that are used to classify Valence, Arousal, Dominance, and Liking emotional states. Hence, a novel approach is proposed for emotion recognition with time series of multi-channel EEG signals from a Database for Emotion Analysis and Using Physiological Signals (DEAP). We propose a new approach to emotional state estimation utilizing CNN-based classification of multi-spectral topology images obtained from EEG signals. In contrast to most of the EEG-based approaches that eliminate spatial information of EEG signals, converting EEG signals into a sequence of multi-spectral topology images, temporal, spectral, and spatial informati

Who reads EEG-based emotion recognition with deep convolutional neural networks?

Author

Mehmet Akif Ozdemir; Murside Degirmenci; Elif Izci; Aydin Akan

Publisher

Walter de Gruyter GmbH

Published

2020

Language

EN

Field

Medicine (Health Sciences)