Perception-Guided EEG Analysis: A Deep Learning Approach Inspired by Level of Detail (LOD) Theory
This work addresses personalized psychological therapy by improving EEG-based perceptual state analysis, though it is incremental with limitations in user alignment and dataset scope.
This study tackled the problem of perceptual state identification and guidance using EEG analysis by developing a deep learning approach inspired by Level of Detail theory, achieving 94.05% accuracy in classification and 92.45% success rate in guiding subjects to target states with an average of 13.2 rhythm cycles.
Objective: This study explores a novel deep learning approach for EEG analysis and perceptual state guidance, inspired by Level of Detail (LOD) theory. The goal is to improve perceptual state identification accuracy and advance personalized psychological therapy. Methods: Portable EEG devices and music rhythm signals were used for data collection. LOD theory was applied to dynamically adjust EEG signal processing, extracting core perceptual features. A Unity-based software system integrated EEG data with audio materials. The deep learning model combined a CNN for feature extraction and classification, and a DQN for reinforcement learning to optimize rhythm adjustments. Results: The CNN achieved 94.05% accuracy in perceptual state classification. The DQN guided subjects to target states with a 92.45% success rate, averaging 13.2 rhythm cycles. However, only 50% of users reported psychological alignment with the target state, indicating room for improvement. Discussion: The results validate the potential of LOD-based EEG biofeedback. Limitations include dataset source, label subjectivity, and reward function optimization. Future work will expand to diverse subjects, incorporate varied musical elements, and refine reward functions for better generalization and personalization.