Exploring Ensemble Machine Learning Models for Attention and Memory Assesment

EEG analysis for attention and memory assessment is an important research topic that has gained a lot of attention in recent years. Virtual Reality (VR) is an innovative technology that provides the ability to design experiments suitable for research in this field. This study explores the feasibility of utilizing ensemble classification algorithms to analyze EEG signals obtained during VR stimulation for the purpose of evaluating memory encoding and attention orienting. The obtained results indicate that EEG analysis can accurately assess memory and attention levels in VR environment setting with a high degree of certainty (84.90% accuracy, and an average of 0.86 true positive rate, 0.88 positive predicted value, 0.89 area under the ROC curve, and 0.89 true negative rate).