北京生物医学工程

基于TGAM模块的便携式脑力疲劳检测系统

Design of portablemental fatigue detecting system based on TGAM module

作者：杨荣李增勇魏鹏绪王丽张宁宋亮

单位：国家康复辅具研究中心，北京市老年功能障碍康复辅助技术重点实验室（北京 100176) 国家康复辅具研究中心附属康复医院（北京 100176)

关键词： TGAM脑电模块; Java平台; 听觉诱发电位; Android; 服务器; 脑力疲劳检测

分类号：R318.04

出版年·卷·期（页码）：2021·40·1（31-37）

摘要：

目的现代社会中脑力劳动者脑力疲劳容易引发事故和心理疾病，设计一种便携式脑力疲劳检测系统及时检测脑力疲劳并提醒劳动者休息的具有重要的现实意义。方法在Android系统平台下，进行脑力疲劳检测系统的应用设计，主要包括分别脑电采集端、Android移动端、服务器端三个模块。系统首先通过脑电采集端进行听觉诱发EEG信号的采集，再将EEG信号通过Android移动端传输至服务器端，利用多种解析算法提取P300中P3b成分的幅值进行分析，最后将分析结果返回Android移动端。结果脑电采集端可实现对EEG进行采集、放大、滤波及传输，并通过蓝牙模块传输至服务器端进行解析和疲劳评判。通过Android端可实现查看原始脑波、P300波形、疲劳评判结果以及历史数据，也可登陆服务器端查看原始脑电数据和数据处理结果。结论所构建的系统可以实时采集用户的脑电数据并进行P300波形分析，方便快捷的得到用户的脑力疲劳状态。未来将进一步优化算法模型，减轻个体差异性对准确度的影响。

Objective Mental fatigue of brain workers is likely to trigger accidents and mental illness in modern society. It is of great practical significance to detect mental fatigue and remind workers to rest in a timely and convenient manner. Methods A mental fatigue detection system is developed on Android platform, which mainly includes three modules: the EEG acquisition terminal, the Android mobile terminal, and the server. The auditory evoked EEG is collected through the EEG acquisition terminal. Then through the Android mobile terminal, EEG signal is transmitted to the server. The amplitude of P3b in the P300 is extracted and analyzed based on various analytic algorithms. Finally, the analysis result is returned to the Android mobile terminal. Results Through the EEG acquisition terminal, EEG is collected, amplified, filtered and transmitted to the server by Bluetooth module for analysis and fatigue evaluation. The raw brain wave, P300 wave, fatigue evaluation results and historical data can be viewed through the Android mobile terminal. The raw EEG and data processing results can also be viewed on the server. ConclusionsThis system can be used to realize the EEG data collection in real time and P300 wave analysis. The mental fatigue state of the user can be get conveniently and quickly. The optimization for the algorithm model will be carried out in further studies to reduce the impact of individual differences in accuracy.

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