基于EEG信号的脑疲劳检测方法

Method of brain fatigue detection based on EEG signal

摘要

脑疲劳是指大脑因长时间高度集中和过度记忆思考,导致脑部神经元活性降低、能耗增加的现象。其危害巨大,不仅降低人的注意力和反应力,长期还可能引发健康问题。为预防大脑疲劳对人们所带来的身心危害,提出一种基于多特征融合和改进LSTM神经网络的脑疲劳检测方法。该方法以脑电信号为分析对象,综合运用小波包变换、小波包熵以及样本熵进行多维度特征提取。将不同方法提取的特征采用单一和融合的方法,分别输入分类器进行识别。实验结果表明,所选特征组合对大脑清醒与疲劳状态具有良好区分度,各类分类模型均能实现有效识别。其中改进的LSTM网络分类方法识别率最高,能够达到95.83%。多组对照实验验证,该方法在识别精度、算法稳定性上均优于常规方法。

Abstract

Brain fatigue refers to a phenomenon where the activity of brain neurons decreases and energy consumption increases due to prolonged high concentration, excessive memory and thinking. It is extremely harmful, as it not only reduces peoples′ attention and responsiveness, but may also lead to health problems in the long run. To prevent the physical and mental hazards caused by brain fatigue to people, this paper proposes a brain fatigue detection method based on multi-feature fusion and improved LSTM neural network. Taking electroencephalographic (EEG) signals as the analysis object, this method comprehensively uses wavelet packet transform, wavelet packet entropy and sample entropy for multi-dimensional feature extraction. The features extracted by different methods are input into the classifier for recognition in the form of single feature and feature fusion respectively. Experimental results show that the selected feature combination has a good distinguishability between the awake and fatigued states of the brain, and all types of classification models can achieve effective recognition. Among them, the improved LSTM network classification method has the highest recognition rate, reaching 95.83%. Multiple comparative experiments verify that this method outperforms conventional methods in terms of recognition accuracy and algorithm stability.

关键词

疲劳检测 / 脑电 / 多特征融合 / 特征分类 / 神经网络

Key words

fatigue detection / EEG / multi-feature fusion / feature classification / neural network