Abstract
Concentration level plays a significant role while performing cognitive actions. There are many ways to predict the concentration level, such as with the help of physical reflection, facial expressions, and body language. Self- evaluation on the scale of 0 to 1 can also be used to measure the concentration level. In this paper, a publicly available dataset is used for classifying the concentration level using students’ brain signals recorded through Electroencephalogram (EEG) device while performing different tasks that require varied concentration level. The study aims to find the appropriate Machine Learning (ML) model that predicts the concentration level through brain signal analysis. For this purpose, five different ML classifiers are used for comparative analysis, namely: Adaboost, Navie Bays, Artificial Neural Networ (ANN), Support Vector Machine (SVM) and Decision Tree. The ANN model gives the highest accuracy, i.e. 71.46% as compared to other classifiers for the concentration level measurement.
V. T. Lokare—Presently working as an Assistant Professor, Rajarambapu Institute of Technology, Sakharale, Affiliated to Shivaji University, Kolhapur.
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Lokare, V.T., Netak, L.D. (2021). Concentration Level Prediction System for the Students Based on Physiological Measures Using the EEG Device. In: Singh, M., Kang, DK., Lee, JH., Tiwary, U.S., Singh, D., Chung, WY. (eds) Intelligent Human Computer Interaction. IHCI 2020. Lecture Notes in Computer Science(), vol 12615. Springer, Cham. https://doi.org/10.1007/978-3-030-68449-5_3
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