In the status quo, dementia is yet to be cured. Precise diagnosis prior to the onset of the symptoms can prevent the rapid progression of the emerging cognitive impairment. Recent progress has shown that Electroencephalography (EEG) is the promising and cost-effective test to facilitate the detection of neurocognitive disorders. However, most of the existing works have been using only resting-state EEG. The efficiencies of EEG signals from various cognitive tasks, for dementia classification, have yet to be thoroughly investigated. In this study, we designed four cognitive tasks that engage different cognitive performances: attention, working memory, and executive function. We investigated these tasks by using statistical analysis on both time and frequency domains of EEG signals from three classes of human subjects: Dementia (DEM), Mild Cognitive Impairment (MCI), and Normal Control (NC). We also further evaluated the classification performances of two features extraction methods: Principal Component Analysis (PCA) and Filter Bank Common Spatial Pattern (FBCSP). We found that the working memory related tasks yielded good performances for dementia recognition in both cases using PCA and FBCSP. Moreover, FBCSP with features combination from four tasks revealed the best sensitivity of 0.87 and the specificity of 0.80. To our best knowledge, this is the first work that concurrently investigated several cognitive tasks for dementia recognition using both statistical analysis and classification scores. Our results yielded essential information to design and aid in conducting further experimental tasks to early diagnose dementia patients.
翻译:在现状中,痴呆症还有待于治愈。在症状开始之前的精确诊断可以防止新出现的认知缺陷的迅速发展。最近的进展表明,电脑phallalogy(EEEG)是有助于检测神经认知紊乱的有希望且成本效益高的测试。然而,大多数现有作品只使用了休眠状态 EEG。各种认知任务(为痴呆症分类)的EEEG信号的效率有待彻底调查。在本研究中,我们设计了四种认知任务,涉及不同的认知表现:注意力、工作记忆和执行功能。我们利用对三种人类课题(Dementia(DEM)、Mild Conitive 缺陷(MCI)和正常控制(NC))的时间和频率的统计分析来调查这些任务。我们还进一步评估了两种特征提取方法(主要成分分析(PCA)和过滤银行共同空间模式(FBCSP)的分类绩效。我们发现,在使用CARC和FBCSP 4 进行最精确的诊断性分析,以及我们用BC7 最精确的统计特征进行最佳的统计分析。