Muhammad Azam, Derek Jacoby and Yvonne Coady, University of Victoria, Canada

Electroencephalogram (EEG) records electrical activity at different locations in the brain. It is used to identify abnormalities and support the diagnoses of different disease conditions. The accessibility of low-cost EEG devices has seen the analysis of this data become more common in other research domains. In this work, we assess the performance of using Approximate entropy, Sample entropy, and Reyni entropy as features in the classification of fatigue from EEG data captured by a MUSE 2 headset. We test 5 classifiers: Naive Bayes, Radial Basis Function Network, Support Vector Machine, K-Nearest Neighbor, and Best First Decision Tree. We achieved the highest accuracy of 77.5% using the Support Vector Machine classifier and present possible enhancements to improve this.

EEG, Electroencephalogram, Approximate entropy, Sample entropy, Reyni entropy, Fatigue detection, Automatic classification, MUSE 2