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.
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Acknowledgement
The authors honorably appreciate Universiti Pertahanan Nasional Malaysia for continuous support and the financial sponsorship through research grant (UPNM/2021/GPJP/ICT/2). We also wish to thank the volunteer participants for their generous cooperation.
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Mohamad Amran, M.F. et al. (2024). Integrating Machine Learning Algorithms with EEG Signals to Identify Emotions Among University Students. In: Silhavy, R., Silhavy, P. (eds) Software Engineering Methods in Systems and Network Systems. CoMeSySo 2023. Lecture Notes in Networks and Systems, vol 909. Springer, Cham. https://doi.org/10.1007/978-3-031-53549-9_34
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