Perception-Guided EEG Analysis: A Deep Learning Approach Inspired by Level of Detail (LOD) Theory

TL;DR

This paper introduces a deep learning approach for EEG analysis guided by Level of Detail theory, improving perceptual state classification and biofeedback for personalized psychological therapy.

Contribution

It presents a novel LOD-inspired method combining CNN and reinforcement learning for dynamic EEG feature extraction and perceptual state guidance.

Findings

CNN achieved 94.05% accuracy in classification

DQN guided subjects with 92.45% success rate

50% user reported psychological alignment

Abstract

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.