Emotion Detection Using EEG Signals Based on Machine Learning

Author(s)	Adeeba Arshiya, Abdul Soharab Hussain, Aditya Kumar, Ehteshaam Hussain
Country	India
Abstract	Emotions describe what an individual feels and can be interpreted through various methods like voice, facial expressions and physiological signals. In this, Electroencephalogram signals are used to detect human emotions. An electroencephalogram (EEG) measures brain signals (brain waves) using a headset placed on the scalp. Using EEG signals plays an important role in Human-Computer Interaction (HCI). EEG signals are typically divided into different frequency bands such as delta, alpha, beta, and gamma. Machine learning models combine features from different frequency bands to make accurate predictions. In this research, a publicly available dataset, titled ‘EEG brainwave Dataset: feeling emotions’, is used. A Support Vector Machine (SVM) classifier is used, which gave an accuracy of 89%.
Field	Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In	Volume 8, Issue 2, March-April 2026
Published On	2026-04-19

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