用细节层次理论优化脑电分析，实现精准情绪引导
Perception-Guided EEG Analysis: A Deep Learning Approach Inspired by Level of Detail (LOD) Theory
- 基于LOD理论动态调整脑电信号处理，提取核心感知特征
- 情绪状态识别准确率达94.05%，目标状态达成率92.45%
- 适合心理治疗与个性化神经反馈系统研发者参考
本研究提出一种受细节层次（LOD）理论启发的深度学习方法，用于脑电图（EEG）分析与感知状态引导，旨在提升情绪状态识别精度并推动个性化心理治疗发展。通过便携式EEG设备与音乐节奏信号采集数据，应用LOD理论动态调节信号处理，提取关键感知特征。构建基于Unity的软件系统，集成EEG数据与音频材料。采用卷积神经网络（CNN）进行特征提取与分类，结合深度强化学习中的深度Q网络（DQN）优化节奏调节策略。实验结果显示，CNN在感知状态分类任务中达到94.05%的准确率，DQN成功引导受试者进入目标状态的比率为92.45%，平均需13.2个节奏周期。然而，仅50%用户报告心理状态与目标状态一致，表明仍存在改进空间。讨论指出，该方法验证了基于LOD的脑电生物反馈潜力，但受限于数据来源、标签主观性及奖励函数设计。未来工作将拓展至多样人群，融合多种音乐元素，并优化奖励函数以增强泛化性与个性化能力。
原文摘要 · Abstract (English)
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.
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