Integrating Machine Learning Algorithms with EEG Signals to Identify Emotions Among University Students

Abstract

Emotion level of the students during their academic sessions has a significant effect on their performance and overall academic grades. The most pre-eminent way of evaluating emotion levels is by analysing the EEG signals obtained from the brain. This paper showcases the experimental study on obtaining the Electroencephalogram (EEG) signals from the students during their academic sessions and classifying it to the types of emotions using machine learning algorithms. This paper explores the method of how the data collection session is conducted and recorded. The data collected is then compiled and divided for the machine learning algorithms. Furthermore, this paper proposed a method of acquiring the emotion labels without prior inducing by using a standard normal distribution method. Finally, this paper also proposed a deep learning neural network model and machine learning models that can be used to determine the emotion level from the EEG signals. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

Keywords

Author Keywords

Machine Learning Emotions EEG

Index Keywords

Learning systems Students University students Machine-learning Learning algorithms Performance Deep learning emotion Machine learning algorithms Data collection Normal distribution Academic sessions Electroencephalogram signals Integrating machines Standard normal distributions Electroencephalography

Author Affiliations

Department of Computer Science, National Defense University of Malaysia, Kuala Lumpur, Malaysia

Funding & Acknowledgements

No funding information

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Integrating Machine Learning Algorithms with EEG Signals to Identify Emotions Among University Students

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