Advancing EEG Analysis for Confusion Detection in Educational Settings Using BiLSTM Deep Learning Techniques

Our study focuses on detecting confusion from EEG data in higher education. Using BiLSTM models, we enhance EEG analysis efficiency and precision. Starting with extensive feature extraction and preprocessing, we improve data quality. We then apply diverse deep learning methods, including BiLSTM models, to predict perplexity-associated EEG signals. Unique data augmentation techniques like random noise injection and synthetic data synthesis bolster model resilience and generalization. Rigorously evaluating our approach with real-world EEG datasets, we achieve a significant accuracy improvement of 99.89%. The evaluation includes analyzing the confusion matrix and classification report, validating our methods' efficacy. Our research offers a comprehensive framework with state-of-the-art deep learning algorithms, particularly BiLSTM models, advancing EEG pattern identification and classification in educational contexts.