Convolutional Neural Network Approach for EEG-based Emotion Recognition using Brain Connectivity and its Spatial Information

Emotion recognition based on electroencephalography (EEG) has received attention as a way to implement human-centric services. However, there is still much room for improvement, particularly in terms of the recognition accuracy. In this paper, we propose a novel deep learning approach using convolut...

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Bibliographic Details
Published in:arXiv.org 2018-09
Main Authors: Seong-Eun Moon, Jang, Soobeom, Jong-Seok, Lee
Format: Article
Language:English
Subjects:
Artificial neural networks
Brain
Electroencephalography
Emotion recognition
Machine learning
Neural networks
Spatial data
Online Access:Get full text
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Summary:Emotion recognition based on electroencephalography (EEG) has received attention as a way to implement human-centric services. However, there is still much room for improvement, particularly in terms of the recognition accuracy. In this paper, we propose a novel deep learning approach using convolutional neural networks (CNNs) for EEG-based emotion recognition. In particular, we employ brain connectivity features that have not been used with deep learning models in previous studies, which can account for synchronous activations of different brain regions. In addition, we develop a method to effectively capture asymmetric brain activity patterns that are important for emotion recognition. Experimental results confirm the effectiveness of our approach.
ISSN:2331-8422