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The Cognitive Load Evaluation Based on EEG with K-Nearest Neighbor Algorithm

This paper explores the impact of different cognitive loads and learning outcomes. It uses Electroencephalography (EEG) to assess learners’ cognitive load status, collects data through subjective and objective measurements, and trains a set of models using machine learning and K-nearest neighbor (KNN) algorithm. To evaluate the effectiveness of the model we use kappa one-time check. The experimental results show that the highest accuracy rate of 10-fold cross-validation is 76.5%, the one-time test of kappa is 0.718, the accuracy rate of Holdout cross-validation is 76.0%, and the one-time test of kappa is 0.711. This proves the effectiveness of the proposed method.