Liu Quan, Liu Yang, Chen Kun, Wang Lei, Li Zhilei, Ai Qingsong, Ma Li
School of Information Engineering, Wuhan University of Technology, Wuhan 430070, China.
Entropy (Basel). 2021 Apr 13;23(4):457. doi: 10.3390/e23040457.
With the rapid development of modern social science and technology, the pace of life is getting faster, and brain fatigue has become a sub-health state that seriously affects the normal life of people. Electroencephalogram (EEG) signals reflect changes in the central nervous system. Using EEG signals to assess mental fatigue is a research hotspot in related fields. Most existing fatigue detection methods are time-consuming or don't achieve satisfactory results due to insufficient features extracted from EEG signals. In this paper, a 2-back task is designed to induce fatigue. The weight value of each channel under a single feature is calculated by ReliefF algorithm. The classification accuracy of each channel under the corresponding features is analyzed. The classification accuracy of each single channel is combined to perform weighted summation to obtain the weight value of each channel. The first half channels sorted in descending order based on the weight value is chosen as the common channels. Multi-features in frequency and time domains are extracted from the common channel data, and the sparse representation method is used to perform feature fusion to obtain sparse fused features. Finally, the SRDA classifier is used to detect the fatigue state. Experimental results show that the proposed methods in our work effectively reduce the number of channels for computation and also improve the mental fatigue detection accuracy.
随着现代社会科技的飞速发展，生活节奏越来越快，大脑疲劳已成为严重影响人们正常生活的一种亚健康状态。脑电图（EEG）信号反映中枢神经系统的变化。利用EEG信号评估精神疲劳是相关领域的研究热点。现有的大多数疲劳检测方法耗时较长，或者由于从EEG信号中提取的特征不足而无法取得令人满意的结果。本文设计了一个2-back任务来诱发疲劳。通过ReliefF算法计算单个特征下每个通道的权重值。分析每个通道在相应特征下的分类准确率。将每个单通道的分类准确率进行加权求和，得到每个通道的权重值。选择权重值降序排列的前半部分通道作为公共通道。从公共通道数据中提取频域和时域的多特征，并采用稀疏表示方法进行特征融合，得到稀疏融合特征。最后，使用SRDA分类器检测疲劳状态。实验结果表明，本文提出的方法有效地减少了计算通道数量，同时提高了精神疲劳检测准确率。
