基于对称双通道脑电信号的注意力检测

Attention Detection Based on Symmetrical Dual-channel EEG Signals

注意力不能集中是一种注意力障碍,该现象普遍存在于青少年中,这直接影响人们的学习和工作效率.传统的注意力检测方法大多依赖对表情、姿势等行为的观察,难以客观精准地反映注意力情况.随着生理检测技术的迅猛发展,基于脑电信号的注意力检测近年来受到极大的关注.然而,相关研究仍存在检测准确率不高的问题.本研究收集了155位大学生在注意力集中、注意力非集中和放松3种状态下的脑电信号,并基于信号的小波特征、微分熵特征及功率谱特征,采用多种机器学习方法对3种注意力状态进行了识别.结果表明,脑电信号的小波特征,微分熵特征及功率谱特征可以有效区分被试的注意力状态,且基于对称双通道特征的平均准确率为(80.84±3)%,其检测精度明显高于基于单通道特征的检测精度.

Lack of concentration is an attention disorder that is common among teenagers,and it directly affects people’s learning and work efficiency.Most of the traditional attention detection methods rely on the observation of expressions,postures,and other behaviors and fail to objectively and accurately reflect attention states.Amid the rapid development of physiological detection technology,attention detection based on electroencephalography(EEG)signals has received considerable attention recently.However,related studies still have the problem of low detection accuracy.In this study,the EEG signals of 155 college students in the three states of being focused,distracted,and relaxed are collected,and the three attention states are identified by various machine learning methods on the basis of the wavelet features,differential entropy features and power spectrum features of the signals.The results show that these features of EEG signals can effectively distinguish the attention states of the subjects.The average accuracy of the detection method based on symmetrical dual-channel features is(80.84±3)%,and the detection precision of this method is significantly higher than that of the method based on single-channel features.