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.
CITATION STYLE
Jadhav, N., Manthalkar, R., & Joshi, Y. (2017). Electroencephalography-based emotion recognition using gray-level co-occurrence matrix features. In Advances in Intelligent Systems and Computing (Vol. 459 AISC, pp. 335–343). Springer Verlag. https://doi.org/10.1007/978-981-10-2104-6_30
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