A Hybrid Data Mining Method for EEG Analysis in Online Education Enhanced by Background Music

Online teaching are facing dramatic challenges due to the COVID19 pandemic requiring massive online education. Students are experiencing mental and physical isolation during this period. This research aims to find an efficient way to discover students emotion status through EEG pattern recognition (PR). Traditional PR methods have been applied extensively in EEG recognition including Artificial Neuron Networks (ANN), Support Vector Machine(SVM), K-Nearest Neighbors (KNN), and so on. In this paper, a association rule-based PR method has been introduced through incorporating clustering and Apriori association rule methods. The experimental results demonstrate that the optimized association rule-based EEG PR model can improve real-time recognition efficiency. The proposed model can be used for identifying students cognitive statuses and improve educational performance in COVID19 period.