Keywords
Electroencephalogram
Attention
Machine Learning
How to Cite
Abstract
In this era, technology has paved the way to new dimensions of research in academia in terms of students’ performance, learning outcomes and capability. Evidently, Brain-Computer Interface (BCI) has shown to be essential in monitoring students’ brain activity through electroencephalogram (EEG) signals. Attention is prerequisite to the evaluation of student learning process. This paper proposed a recognition of attention level in e-learning environment. It was divided by two states, attention and inattention(distratced). EEG signals were extracted using non-invasive device (Emotiv Insight) and processed data for noise removal through the Finite Impulse Response (FIR) filter. A machine learning appraoch has been used for classification of data. The data acquired through the channels is continous for which Support vector machines (SVM) has proven to be the best fit classification algorithm according to recent researches. The selected features then are classified. The obtained accuracy for attention level is 90.07% in an e-learning environment.
References
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