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EEG signal based Modified Kohonen Neural Networks for Classification of Human Mental Emotions

D. Jude Hemanth1,*

Corresponding Author:

D. Jude Hemanth

Affiliation(s):

1. Department of ECE, Karunya Institute of Technology and Sciences, Coimbatore, India
*Corresponding Author: D. Jude Hemanth, Email: [email protected]

Abstract:

Identifying human emotions is very important in human machine interaction(HMI). These emotions will affect communication between people, and their mood.Emotion detection will give a clear idea aboutcustomer satisfaction in e-learning, marketing, entertainments and behavior of criminals in law. Artificial neural networksare essential for machine learning and emotion detection.The emotions are detected from EEG signals which can give better performance to audio and facial signals. In this work, several modified Kohonen neural networks are proposed for human emotion classification.EEG signals from DEAP Database are used asinput for ANN to detect the human emotions. Angry, Happy, Sad and Relax are the emotions classified using KohonenNeural Networks.Experimental results show promising results for the proposed approaches.

Keywords:

Kohonen neural network, brain signals, human emotions, classification accuracy

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Cite This Paper:

D. Jude Hemanth (2020). EEG signal based Modified Kohonen Neural Networks for Classification of Human Mental Emotions. Journal of Artificial Intelligence and Systems, 2, 1–13. https://doi.org/10.33969/AIS.2020.21001.

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