北京生物醫(yī)學(xué)工程

基于TGAM模塊的便攜式腦力疲勞檢測(cè)系統(tǒng)

Design of portablemental fatigue detecting system based on TGAM module

作者：楊榮李增勇魏鵬緒王麗張寧宋亮

單位：國(guó)家康復(fù)輔具研究中心，北京市老年功能障礙康復(fù)輔助技術(shù)重點(diǎn)實(shí)驗(yàn)室（北京 100176) 國(guó)家康復(fù)輔具研究中心附屬康復(fù)醫(yī)院（北京 100176)

關(guān)鍵詞： TGAM腦電模塊; Java平臺(tái); 聽(tīng)覺(jué)誘發(fā)電位; Android; 服務(wù)器; 腦力疲勞檢測(cè)

分類(lèi)號(hào)：R318.04

出版年·卷·期（頁(yè)碼）：2021·40·1（31-37）

摘要：

目的現(xiàn)代社會(huì)中腦力勞動(dòng)者腦力疲勞容易引發(fā)事故和心理疾病，設(shè)計(jì)一種便攜式腦力疲勞檢測(cè)系統(tǒng)及時(shí)檢測(cè)腦力疲勞并提醒勞動(dòng)者休息的具有重要的現(xiàn)實(shí)意義。方法在Android系統(tǒng)平臺(tái)下，進(jìn)行腦力疲勞檢測(cè)系統(tǒng)的應(yīng)用設(shè)計(jì)，主要包括分別腦電采集端、Android移動(dòng)端、服務(wù)器端三個(gè)模塊。系統(tǒng)首先通過(guò)腦電采集端進(jìn)行聽(tīng)覺(jué)誘發(fā)EEG信號(hào)的采集，再將EEG信號(hào)通過(guò)Android移動(dòng)端傳輸至服務(wù)器端，利用多種解析算法提取P300中P3b成分的幅值進(jìn)行分析，最后將分析結(jié)果返回Android移動(dòng)端。結(jié)果腦電采集端可實(shí)現(xiàn)對(duì)EEG進(jìn)行采集、放大、濾波及傳輸，并通過(guò)藍(lán)牙模塊傳輸至服務(wù)器端進(jìn)行解析和疲勞評(píng)判。通過(guò)Android端可實(shí)現(xiàn)查看原始腦波、P300波形、疲勞評(píng)判結(jié)果以及歷史數(shù)據(jù)，也可登陸服務(wù)器端查看原始腦電數(shù)據(jù)和數(shù)據(jù)處理結(jié)果。結(jié)論所構(gòu)建的系統(tǒng)可以實(shí)時(shí)采集用戶的腦電數(shù)據(jù)并進(jìn)行P300波形分析，方便快捷的得到用戶的腦力疲勞狀態(tài)。未來(lái)將進(jìn)一步優(yōu)化算法模型，減輕個(gè)體差異性對(duì)準(zhǔn)確度的影響。

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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