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Background Modeling Using Temporal-Local Sample Density Outlier Detection Read chapter Read first chapter

2017 | OriginalPaper | Chapter

Electroencephalography-Based Emotion Recognition Using Gray-Level Co-occurrence Matrix Features

Authors : Narendra Jadhav, Ramchandra Manthalkar, Yashwant Joshi

Published in: Proceedings of International Conference on Computer Vision and Image Processing

Publisher: Springer Singapore

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Abstract

Emotions are very essential for our day-to-day activities such as communication, decision-making and learning. Electroencephalography (EEG) is a non-invasive method to record electrical activity of the brain. To make Human–Machine Interaction (HMI) more natural, human emotion recognition is important. Over the past decade, various signal processing methods are used for analysing EEG-based emotion recognition (ER). This paper proposes a novel technique for ER using Gray-Level Co-occurrence Matrix (GLCM)-based features. The features are validated on benchmark DEAP database upto four emotions and classified using K-nearest neighbor (K-NN) classifier.

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Metadata
Title
Electroencephalography-Based Emotion Recognition Using Gray-Level Co-occurrence Matrix Features
Authors
Narendra Jadhav
Ramchandra Manthalkar
Yashwant Joshi
Copyright Year
2017
Publisher
Springer Singapore
Book
Proceedings of International Conference on Computer Vision and Image Processing

Print ISBN: 978-981-10-2103-9

Electronic ISBN: 978-981-10-2104-6

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-981-10-2104-6_30