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2017 | OriginalPaper | Buchkapitel

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

verfasst von : Narendra Jadhav, Ramchandra Manthalkar, Yashwant Joshi

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

Verlag: 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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Metadaten
Titel
Electroencephalography-Based Emotion Recognition Using Gray-Level Co-occurrence Matrix Features
verfasst von
Narendra Jadhav
Ramchandra Manthalkar
Yashwant Joshi
Copyright-Jahr
2017
Verlag
Springer Singapore
DOI
https://doi.org/10.1007/978-981-10-2104-6_30

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