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

arXiv: https://arxiv.org/abs/2501.10428 · HTML: https://mlsanity.com/paper/2501.10428/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 25 references that are in the map.
- First posted on arXiv 11 Jan 2025.

## Builds on (most cited first, 10 of 10)

- [Deep learning-based electroencephalography analysis: a systematic review](https://mlsanity.com/paper/1901.05498/) (2019)
- [Learning Representations from EEG with Deep Recurrent-Convolutional Neural Networks](https://mlsanity.com/paper/1511.06448/) (2015)
- [EEG-Deformer: A Dense Convolutional Transformer for Brain-computer Interfaces](https://mlsanity.com/paper/2405.00719/) (2024)
- [Self-supervised Learning for Electroencephalogram: A Systematic Survey](https://mlsanity.com/paper/2401.05446/) (2024)
- [Investigating the Gestalt Principle of Closure in Deep Convolutional Neural Networks](https://mlsanity.com/paper/2411.00627/) (2024)
- [The Gestalt Computational Model by Persistent Homology](https://mlsanity.com/paper/2405.20583/) (2024)
- [Deep Learning Decoding of Mental State in Non-invasive Brain Computer Interface](https://mlsanity.com/paper/1911.05661/) (2019)
- [Hybrid Quantum Deep Learning Model for Emotion Detection using raw EEG Signal Analysis](https://mlsanity.com/paper/2411.17715/) (2024)
- [Comparative Analysis of Deep Learning Approaches for Harmful Brain Activity Detection Using EEG](https://mlsanity.com/paper/2412.07878/) (2024)
- [BRAIN2DEPTH: Lightweight CNN Model for Classification of Cognitive States from EEG Recordings](https://mlsanity.com/paper/2106.06688/) (2021)

## Built on by (most cited first, 1 of 1)

- [A Graph Neural Network based on a Functional Topology Model: Unveiling the Dynamic Mechanisms of Non-Suicidal Self-Injury in Single-Channel EEG](https://mlsanity.com/paper/2508.11684/) (2025)

## Abstract (arXiv)

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.