VR Eye-tracking Data Analysis Platform

English | 中文

---

⚠️ CRITICAL DATA SAFETY WARNING / 数据安全警告

🔴 BEFORE ANY OPERATIONS / 操作前必读

ALWAYS backup critical data files before:

• Running any metadata rebuild scripts
• Modifying subject_metadata.json
• Updating subject information
• Performing batch operations

关键数据文件，操作前务必备份：

• 运行任何元数据重建脚本前
• 修改 subject_metadata.json 前
• 更新受试者信息前
• 执行批量操作前

📂 Critical Data Locations / 关键数据位置

MUST BACKUP / 必须备份:
├── new_project/data/01_raw/clinical/subject_metadata.json (元数据索引)
├── new_project/data/02_processed/ (校正后的数据)
├── new_project/data/subject_info/ (受试者信息)
├── data/*_raw/ (V1原始数据)
└── eye_tracking_data/ (V2原始数据)

🛡️ Data Recovery Tools / 数据恢复工具

If tasks_available is lost: python restore_tasks_available.py

See DATA_RECOVERY.md for details.

---

📋 Project Overview

This is a Python + Flask based eye-tracking data analysis platform specifically designed for processing and analyzing VR eye-tracking experimental data. The system supports multiple analysis modes including Recurrence Quantification Analysis (RQA), trajectory visualization, and Region of Interest (ROI) analysis.

🎯 Key Features

• ✅ Eye-tracking Data Preprocessing - Time calibration, noise filtering, data normalization
• ✅ Recurrence Quantification Analysis (RQA) - 1D/2D signal analysis, recurrence plot generation, quantitative metrics calculation
• ✅ Visual Analysis - Trajectory plots, heatmaps, amplitude plots, recurrence plots
• ✅ ROI Region Analysis - Precise ROI coloring and annotation based on All_Events.csv
• ✅ Web Interface - Modern responsive interface with parameter configuration and result viewing
• 🆕 RQA Parameterized Analysis Pipeline - Complete five-step automated analysis workflow with parameter management and result comparison
• 🆕 Eye Movement Coefficient vs MMSE Comparison Analysis - Cognitive assessment comparison based on eye movement features, supporting multi-dimensional correlation studies

🏗️ System Architecture

Core Module Architecture

VR Eye-tracking Data Analysis System
├── 📊 Data Processing Module
│   ├── Time Calibration
│   ├── Data Preprocessing
│   └── Data Validation
├── 🔬 RQA Analysis Module
│   ├── Signal Embedding
│   ├── Recurrence Matrix Calculation
│   ├── RQA Measures Extraction
│   └── Visualization Rendering
├── 🎨 Visualization Module
│   ├── Trajectory Plots
│   ├── Heatmaps
│   ├── ROI Analysis Plots
│   └── Recurrence Plots
├── 🔄 RQA Analysis Pipeline Module 🆕
│   ├── RQA Calculation
│   ├── Data Merging
│   ├── Feature Enrichment
│   ├── Statistical Analysis
│   ├── Visualization Generation
│   └── Parameter Management
├── 📊 Data Integration Module (Module 7) 🆕
│   ├── Multi-source Data Loading
│   ├── Feature Extraction & Integration
│   ├── 10-Feature Normalization
│   ├── Intelligent Outlier Handling
│   ├── RQA Parameter Management
│   └── Structured Data Storage
├── 🧠 Eye Movement vs MMSE Comparison Module (Module 8) 🆕
│   ├── Eye Movement Data Processing
│   ├── Eye Movement Coefficient Calculation
│   ├── MMSE Data Loading
│   ├── Multi-dimensional Comparison
│   ├── Sub-question Analysis
│   ├── 5-Chart Visualization
│   ├── Correlation Analysis
│   └── Auto CSV Export
└── 🌐 Web Interface Module
    ├── Data Management Interface
    ├── Analysis Configuration Interface
    ├── Result Display Interface
    ├── 🆕 RQA Pipeline Interface
    ├── 🆕 Data Integration Interface (Module 7)
    ├── 🆕 Eye Movement vs MMSE Interface (Module 8)
    └── API Endpoints

Technology Stack

• Backend: Python 3.8+, Flask, NumPy, Pandas, Matplotlib
• Frontend: HTML5, CSS3, JavaScript (ES6+), Bootstrap
• Data Processing: SciPy, scikit-learn
• Visualization: Matplotlib, Seaborn
• API: RESTful API, JSON data exchange

📁 Project Structure

vr-eyetracking-analysis/
├── 📂 analysis/                    # Core analysis modules
│   ├── rqa_batch_renderer.py      # RQA batch renderer (core class)
│   ├── time_calibration.py        # Time calibration module
│   └── data_processor.py          # Data preprocessor
├── 📂 visualization/               # Visualization modules
│   ├── rqa_api_extension.py       # RQA API extension
│   ├── rqa_pipeline_api.py        # 🆕 RQA pipeline API
│   ├── mmse_api_extension.py      # 🆕 MMSE data API extension
│   ├── real_data_integration_api.py # 🆕 Real data integration API
│   ├── web_api.py                 # Web API interface
│   └── templates/
│       └── enhanced_index.html    # Main interface template
├── 📂 data/                       # Data directory
│   ├── calibrated/                # Eye-tracking calibrated data
│   ├── MMSE_Score/                # 🆕 MMSE cognitive assessment data
│   ├── event_analysis_results/    # ROI event analysis results
│   ├── normalized_features/       # 🆕 Normalized feature data
│   ├── module7_integrated_results/ # 🆕 Module 7 integration results
│   ├── module8_analysis_results/  # 🆕 Module 8 analysis results
│   └── rqa_pipeline_results/      # 🆕 RQA pipeline results
├── 📂 static/                     # Static resources
├── start_server.py                # Server startup script
└── README.md                      # Project documentation

🚀 Quick Start

Environment Setup

# 1. Create virtual environment
python -m venv venv
source venv/bin/activate  # Linux/Mac
# or
venv\Scripts\activate     # Windows

# 2. Install dependencies
pip install flask numpy pandas matplotlib scipy scikit-learn

# 3. Start server
python start_server.py

Access the System

# Start web server
python start_server.py

# Access interface
http://localhost:8080

📊 Usage Guide

Data Preparation

1. Data Format Requirements:

timestamp,x,y,milliseconds,ROI,SequenceID
1641024000000,500.2,300.1,0,BG,0
1641024000016,502.1,301.5,16,INST,1
...

2. File Naming Convention:

{group}{id}q{question}_preprocessed_calibrated.csv
Example: n1q1_preprocessed_calibrated.csv (Control group 1, Q1)
        m1q1_preprocessed_calibrated.csv (MCI group 1, Q1)

Running Analysis

Traditional RQA Analysis

1. Start system: python start_server.py
2. Access interface: http://localhost:8080
3. Select RQA Analysis tab
4. Configure parameters:
   • Analysis mode: 1D signal (X coordinate)/1D signal (amplitude)/2D signal (X,Y coordinates)
   • Distance metric: 1D absolute difference/Euclidean distance
   • Embedding dimension: typically 2-10
   • Time delay: typically 1
   • Recurrence threshold: 0.01-0.1 range
   • Minimum line length: 2-5
5. Start rendering: Click "Start RQA Rendering"
6. View results in the results area

🆕 RQA Analysis Pipeline (Recommended)

1. Start system and access interface
2. Select "RQA Analysis Pipeline" tab
3. Configure RQA parameters:
   • Embedding dimension (m): 2 (default)
   • Time delay (τ): 1 (default)
   • Recurrence threshold (ε): 0.05 (default)
   • Minimum line length (l_min): 2 (default)
4. View parameter signature: System auto-generates m2_tau1_eps0.05_lmin2
5. Execute analysis workflow:
   • Click "Step 1: RQA Calculation" or
   • Click "Complete Pipeline" (one-click execution)
6. Monitor progress with five-step progress indicator
7. View results in visualization area
8. Manage history using "Parameter History" feature

🆕 Module 7: Data Integration

1. Select "Module 7 - Data Integration" tab
2. Choose RQA parameter configuration from dropdown
3. View real-time statistics:
   • Total subjects: dynamically calculated
   • Game sessions: real-time updates
   • VR-MMSE tasks: task type statistics
   • Normalized features: feature count statistics
4. Execute data integration
5. View standardization details
6. Generate visualizations
7. Export integrated data

🆕 Module 8: Eye Movement vs MMSE Analysis

1. Select "Module 8 - MMSE Comparison" tab
2. Select data source from Module 7
3. Load eye movement data
4. Calculate eye movement coefficients
5. Perform MMSE comparison analysis
6. View multi-dimensional results:
   • Individual view: detailed comparison per subject
   • Group view: statistics by group with correlation analysis
   • Main task mode: Q1-Q5 task-level analysis
   • Sub-question mode: detailed analysis of 17 specific sub-questions
7. Smart visualization:
   • Q1-Q5 separated scatter plots
   • Three-color grouping: Blue=Control, Orange=MCI, Red=AD
   • Completion rate axis: Y-axis shows MMSE completion rate (0-100%)
8. Export analysis reports

🔬 Module Details

Data Processing Module

Function: Eye-tracking data preprocessing and standardization Core Features:

• ⏰ Time calibration: millisecond-level timestamp standardization
• 🔧 Data cleaning: anomaly detection and filtering
• 📊 Format conversion: multiple data format support
• ✅ Data validation: completeness and consistency checks

RQA Analysis Module

Function: Complete implementation of Recurrence Quantification Analysis Analysis Modes:

• 🔢 1D signal (X coordinate): 1d_x
• 📈 1D signal (amplitude): 1d_amplitude
• 📊 2D signal (X,Y coordinates): 2d_xy

Parameters:

{
    "analysis_mode": "2d_xy",
    "distance_metric": "euclidean",
    "embedding_dimension": 2,        # m
    "time_delay": 1,                 # τ
    "recurrence_threshold": 0.05,    # ε
    "min_line_length": 2,            # l_min
    "color_theme": "green_gradient"
}

Module 7: Data Integration 🆕

Core Features:

• 🔗 Multi-source data integration
• 📊 Intelligent data standardization
• 🎯 RQA parameterized configuration
• 💾 Result caching mechanism
• 📈 Real-time statistics updates

10 Normalized Features:

• game_duration: Game duration
• roi_kw_time: KW-ROI time
• roi_inst_time: INST-ROI time
• roi_bg_time: BG-ROI time
• rr_1d, det_1d, ent_1d: 1D RQA metrics
• rr_2d, det_2d, ent_2d: 2D RQA metrics

Module 8: Eye Movement vs MMSE Analysis 🆕

Core Features:

• 🧠 MMSE data integration
• 📊 Eye movement coefficient calculation
• 🔍 Multi-dimensional comparison
• 📈 5-chart visualization
• 🔗 Correlation analysis
• 📁 Auto CSV export

Eye Movement Coefficient Calculation:

Eye_Movement_Coefficient = mean(
  inverted(game_duration, roi_times) + 
  direct(rqa_metrics)
) / 10

🔧 Technical Details

RQA Algorithm Implementation

# 1. Signal embedding (Phase Space Reconstruction)
embedded = embed_signal(signal, m=2, tau=1)

# 2. Distance matrix calculation
distances = compute_distance_matrix(embedded, metric='euclidean')

# 3. Recurrence matrix generation
recurrence_matrix = distances < threshold

# 4. RQA metrics calculation
RR = np.sum(recurrence_matrix) / (N * N)
DET = calculate_determinism(recurrence_matrix)
ENT = calculate_entropy(recurrence_matrix)

Performance Optimization

• ⚡ Batch Processing: Parallel processing of multiple data files
• 💾 Memory Management: Timely release of graphics objects and memory
• 🔄 Incremental Rendering: Support for incremental updates
• 📁 Result Caching: Results organized by parameter signatures

🐛 FAQ

Q: Rendering failed?

A: Check data format, file paths, and parameter settings. Check server logs for detailed error information.

Q: Module 7 data integration failed?

A:

• Check if data/calibrated directory contains calibrated data
• Confirm data/event_analysis_results/All_ROI_Summary.csv exists
• Verify RQA results in data/rqa_pipeline_results
• Check server logs for details

Q: Module 8 MMSE data loading error?

A:

• Confirm data/MMSE_Score directory contains three group CSV files
• Check CSV file column name format
• Verify subject ID format matching
• Ensure Module 7 data is generated first

📞 Support

For issues or suggestions:

• 📧 Create an Issue
• 📝 Check project Wiki
• 🔧 Submit Pull Requests

---

眼动数据分析系统 (中文版)

📋 项目概述

这是一个基于Python + Flask的眼动数据分析平台，专门用于处理和分析眼球追踪实验数据。系统支持多种分析模式，包括递归量化分析(RQA)、轨迹可视化、ROI(感兴趣区域)分析等功能。

🎯 主要功能

• ✅ 眼动数据预处理 - 时间校准、噪声过滤、数据标准化
• ✅ 递归量化分析(RQA) - 1D/2D信号分析、递归图生成、量化指标计算
• ✅ 可视化分析 - 轨迹图、热力图、amplitude图、递归图
• ✅ ROI区域分析 - 基于All_Events.csv的精确ROI着色和标注
• ✅ Web界面 - 现代化响应式界面，支持参数配置和结果查看
• 🆕 RQA参数化分析流程 - 完整的五步骤自动化分析流程，支持参数管理和结果对比
• 🆕 眼动系数与MMSE对比分析 - 基于眼动特征的认知评估对比分析，支持多维度相关性研究

🏗️ 系统架构

核心模块架构

眼动数据分析系统
├── 📊 数据处理模块 (Data Processing)
│   ├── 时间校准 (Time Calibration)
│   ├── 数据预处理 (Preprocessing) 
│   └── 数据验证 (Validation)
├── 🔬 RQA分析模块 (RQA Analysis)
│   ├── 信号嵌入 (Signal Embedding)
│   ├── 递归矩阵计算 (Recurrence Matrix)
│   ├── 量化指标提取 (RQA Measures)
│   └── 可视化渲染 (Visualization)
├── 🎨 可视化模块 (Visualization)
│   ├── 轨迹图 (Trajectory Plots)
│   ├── 热力图 (Heatmaps)
│   ├── ROI分析图 (ROI Analysis)
│   └── 递归图 (Recurrence Plots)
├── 🔄 RQA分析流程模块 (RQA Pipeline) 🆕
│   ├── RQA计算 (RQA Calculation)
│   ├── 数据合并 (Data Merging)
│   ├── 特征补充 (Feature Enrichment)
│   ├── 统计分析 (Statistical Analysis)
│   ├── 可视化生成 (Visualization Generation)
│   └── 参数管理 (Parameter Management)
├── 📊 数据整合模块 (Module 7) 🆕
│   ├── 多源数据加载 (Multi-source Data Loading)
│   ├── 特征抽取整合 (Feature Extraction & Integration)
│   ├── 十属性归一化 (10-Feature Normalization)
│   ├── 智能异常值处理 (Intelligent Outlier Handling)
│   ├── RQA参数化管理 (RQA Parameter Management)
│   └── 结构化数据存储 (Structured Data Storage)
├── 🧠 眼动系数与MMSE对比分析模块 (Module 8) 🆕
│   ├── 眼动数据处理 (Eye Movement Data Processing)
│   ├── 眼动系数计算 (Eye Movement Coefficient Calculation)
│   ├── MMSE数据加载 (MMSE Data Loading)
│   ├── 多维度对比分析 (Multi-dimensional Comparison)
│   ├── 子问题详细分析 (Sub-question Analysis)
│   ├── 5图表可视化 (5-Chart Visualization)
│   ├── 相关性分析 (Correlation Analysis)
│   └── 自动CSV导出 (Auto CSV Export)
└── 🌐 Web界面模块 (Web Interface)
    ├── 数据管理界面
    ├── 分析配置界面
    ├── 结果展示界面
    ├── 🆕 RQA分析流程界面
    ├── 🆕 数据整合界面 (模块7)
    ├── 🆕 眼动系数与MMSE对比界面 (模块8)
    └── API接口

技术栈

• 后端: Python 3.8+, Flask, NumPy, Pandas, Matplotlib
• 前端: HTML5, CSS3, JavaScript (ES6+), Bootstrap
• 数据处理: SciPy, scikit-learn
• 可视化: Matplotlib, Seaborn
• API: RESTful API, JSON数据交换

📁 项目文件结构

az/
├── 📂 analysis/                    # 核心分析模块
│   ├── rqa_batch_renderer.py      # RQA批量渲染器 (核心类)
│   ├── time_calibration.py        # 时间校准模块
│   └── data_processor.py          # 数据预处理器
├── 📂 visualization/               # 可视化模块  
│   ├── rqa_api_extension.py       # RQA API扩展
│   ├── rqa_pipeline_api.py        # 🆕 RQA分析流程API
│   ├── mmse_api_extension.py      # 🆕 MMSE数据API扩展
│   ├── real_data_integration_api.py # 🆕 真实数据整合API
│   ├── web_api.py                 # Web API接口
│   └── templates/
│       └── enhanced_index.html    # 主界面模板(含模块7-8)
├── 📂 data/                       # 数据目录
│   ├── calibrated/                # 眼动校准数据(按组别目录)
│   ├── MMSE_Score/                # 🆕 MMSE认知评估数据
│   ├── event_analysis_results/    # ROI事件分析结果
│   ├── normalized_features/       # 🆕 标准化特征数据(模块7)
│   ├── module7_integrated_results/ # 🆕 模块7数据整合结果
│   ├── module8_analysis_results/  # 🆕 模块8分析结果
│   └── rqa_pipeline_results/      # 🆕 RQA分析流程结果
├── 📂 static/                     # 静态资源
├── start_server.py                # 服务器启动脚本
└── README.md                      # 项目文档

🚀 开发指南

环境配置

# 1. 创建虚拟环境
python -m venv venv
source venv/bin/activate  # Linux/Mac
# 或
venv\Scripts\activate     # Windows

# 2. 安装依赖
pip install flask numpy pandas matplotlib scipy scikit-learn

# 3. 启动服务
python start_server.py

启动系统

# 启动Web服务器
python start_server.py

# 访问界面
http://localhost:8080

📊 使用说明

数据准备

1. 数据格式要求:

timestamp,x,y,milliseconds,ROI,SequenceID
1641024000000,500.2,300.1,0,BG,0
1641024000016,502.1,301.5,16,INST,1
...

2. 文件命名规范:

{group}{id}q{question}_preprocessed_calibrated.csv
例如: n1q1_preprocessed_calibrated.csv (对照组1号Q1)
     m1q1_preprocessed_calibrated.csv (MCI组1号Q1)

运行分析

传统RQA分析

1. 启动系统: python start_server.py
2. 访问界面: http://localhost:8080
3. 选择RQA分析选项卡
4. 配置参数:
   • 分析模式: 1D信号(X坐标)/1D信号(幅度)/2D信号(X,Y坐标)
   • 距离度量: 1D绝对差/欧几里得距离
   • 嵌入维度: 通常为2-10
   • 时间延迟: 通常为1
   • 递归阈值: 0.01-0.1范围
   • 最小线长: 2-5
5. 启动渲染: 点击"开始RQA渲染"
6. 查看结果: 渲染完成后在结果区域查看

🆕 RQA分析流程 (推荐)

1. 启动系统: python start_server.py
2. 访问界面: http://localhost:8080
3. 选择"RQA分析流程"选项卡
4. 配置RQA参数:
   • 嵌入维度(m): 2 (默认)
   • 时间延迟(τ): 1 (默认)
   • 递归阈值(ε): 0.05 (默认)
   • 最小线长(l_min): 2 (默认)
5. 查看参数签名: 系统自动生成 m2_tau1_eps0.05_lmin2
6. 执行分析流程:
   • 点击"步骤1: RQA计算" 或
   • 点击"完整流程" (一键执行所有步骤)
7. 监控进度: 观察五步骤进度指示器
8. 查看结果: 在可视化区域查看生成的图表
9. 管理历史: 使用"历史参数"功能管理和对比不同参数的结果

🆕 模块7: 数据整合分析

1. 选择"模块7-数据整合"选项卡
2. 选择RQA参数配置
3. 查看实时统计:
   • 受试者总数: 动态计算
   • 游戏会话数: 实时更新
   • VR-MMSE任务: 任务类型统计
   • 归一化特征: 特征数量统计
4. 执行数据整合
5. 查看标准化说明
6. 可视化分析
7. 数据导出

🆕 模块8: 眼动系数与MMSE对比分析

1. 选择"模块8-MMSE对比分析"选项卡
2. 选择数据源
3. 加载眼动数据
4. 计算眼动系数
5. MMSE对比分析
6. 多维度分析结果:
   • 个人视图: 每个受试者的详细对比数据
   • 群体视图: 按组别统计的平均值和相关性分析
   • 主问题模式: Q1-Q5任务级别分析
   • 子问题模式: 17个具体子问题的精细分析
7. 智能可视化:
   • Q1-Q5分离式散点图: 任务特异性相关性展示
   • 三色分组: 蓝色=Control, 橙色=MCI, 红色=AD
   • 完成率轴: Y轴显示MMSE完成率(0-100%)
8. 智能数据导出

🔬 模块详解

数据处理模块

功能: 眼动数据的预处理和标准化 核心功能:

• ⏰ 时间校准: 毫秒级时间戳标准化
• 🔧 数据清洗: 异常值检测和过滤
• 📊 格式转换: 多种数据格式支持
• ✅ 数据验证: 完整性和一致性检查

RQA分析模块

功能: 递归量化分析的完整实现 分析模式:

• 🔢 1D信号(X坐标): 1d_x
• 📈 1D信号(幅度): 1d_amplitude
• 📊 2D信号(X,Y坐标): 2d_xy

参数设置:

{
    "analysis_mode": "2d_xy",           # 分析模式
    "distance_metric": "euclidean",     # 距离度量
    "embedding_dimension": 2,           # 嵌入维度(m)
    "time_delay": 1,                    # 时间延迟(τ)
    "recurrence_threshold": 0.05,       # 递归阈值(ε)
    "min_line_length": 2,               # 最小线长(l_min)
    "color_theme": "green_gradient"     # 渲染主题
}

模块7: 数据整合 🆕

核心特性:

• 🔗 多源数据整合: 自动整合校准数据、ROI分析结果、RQA计算结果
• 📊 智能数据标准化: 支持百分位截断和Min-Max标准化策略
• 🎯 RQA参数化配置: 动态检测和选择不同RQA参数组合
• 💾 结果缓存机制: 基于RQA参数的智能缓存和增量更新
• 📈 实时统计更新: 动态计算受试者、会话、特征数量

10个标准化特征:

• game_duration: 游戏时长
• roi_kw_time: KW-ROI时间
• roi_inst_time: INST-ROI时间
• roi_bg_time: BG-ROI时间
• rr_1d, det_1d, ent_1d: 1D RQA指标
• rr_2d, det_2d, ent_2d: 2D RQA指标

模块8: 眼动系数与MMSE对比分析 🆕

核心特性:

• 🧠 MMSE数据整合: 自动加载对照组、MCI组、AD组认知评估数据
• 📊 眼动系数计算: 基于10个标准化特征的综合眼动表现系数
• 🔍 多维度对比: 个人级、群体级、子问题级三种分析维度
• 📈 5图表可视化: Q1-Q5任务的分离式散点图展示
• 🔗 相关性分析: Pearson相关系数和标准差统计
• 📁 自动CSV导出: 所有分析结果自动保存为CSV格式

眼动系数计算:

眼动系数 = mean(
  反转(游戏时长, ROI时间) + 
  直接(RQA指标)
) / 10

🔧 技术细节

RQA算法实现

# 1. 信号嵌入 (Phase Space Reconstruction)
embedded = embed_signal(signal, m=2, tau=1)

# 2. 距离矩阵计算
distances = compute_distance_matrix(embedded, metric='euclidean')

# 3. 递归矩阵生成
recurrence_matrix = distances < threshold

# 4. RQA指标计算
RR = np.sum(recurrence_matrix) / (N * N)
DET = calculate_determinism(recurrence_matrix)
ENT = calculate_entropy(recurrence_matrix)

性能优化

• ⚡ 批量处理: 并行处理多个数据文件
• 💾 内存管理: 及时释放图形对象和内存
• 🔄 增量渲染: 支持参数变更时的增量更新
• 📁 结果缓存: 按参数签名组织结果文件

🐛 常见问题

Q: 渲染失败怎么办？

A: 检查数据格式、文件路径和参数设置，查看服务器日志获取详细错误信息。

Q: 模块7数据整合失败？

A:

• 检查data/calibrated目录是否包含校准数据
• 确认data/event_analysis_results/All_ROI_Summary.csv文件存在
• 验证data/rqa_pipeline_results中有对应RQA参数的结果
• 查看服务器日志获取详细错误信息

Q: 模块8 MMSE数据加载异常？

A:

• 确认data/MMSE_Score目录包含三个组别的CSV文件
• 检查CSV文件的列名格式(受试者/试者列名不一致)
• 验证受试者ID格式匹配(如n01 vs n1q)
• 确保先在模块7中生成对应RQA配置的数据

📞 技术支持

如有问题或建议，请通过以下方式联系：

• 📧 创建Issue描述问题
• 📝 查看项目Wiki获取更多信息
• 🔧 提交Pull Request参与开发

---

版本: v1.3.0
最后更新: 2025年8月5日
开发状态: 活跃开发中 🚀