论文标题
脑电图在局部脑叶上进行精神状态检测的多元经验模式分解
Multivariate Empirical Mode Decomposition of EEG for Mental State Detection at Localized Brain Lobes
论文作者
论文摘要
在这项研究中，多元经验模式分解（MEMD）方法用于从多通道EEG信号中提取用于精神状态分类的特征。 MEMD是一种数据自适应分析方法，它特别适用于EEG等多维非线性信号。应用MEMD会导致一组称为固有模式函数（IMF）的振荡模式。由于分解过程依赖于数据，因此IMFS根据功能性脑活动引起的信号变化而变化。在提取的IMF中，发现与高振荡模式相对应的人最有用，可用于检测不同的精神状态。非线性特征是从IMF中计算出的，这些特征对精神状态检测贡献最大。这些MEMD功能显示出与傅立叶变换和小波变换获得的常规速度光谱特征相比，性能增长显着。通过分析从相关的EEG通道中提取的MEMD特征来观察特定大脑区域的主导性。发现额叶区域最为重要，分类精度为98.06％。这种多维分解接近持续的渠道特性，并为基于脑电图的精神状态检测产生最歧视性的特征。
In this study, the Multivariate Empirical Mode Decomposition (MEMD) approach is applied to extract features from multi-channel EEG signals for mental state classification. MEMD is a data-adaptive analysis approach which is suitable particularly for multi-dimensional non-linear signals like EEG. Applying MEMD results in a set of oscillatory modes called intrinsic mode functions (IMFs). As the decomposition process is data-dependent, the IMFs vary in accordance with signal variation caused by functional brain activity. Among the extracted IMFs, it is found that those corresponding to high-oscillation modes are most useful for detecting different mental states. Non-linear features are computed from the IMFs that contribute most to mental state detection. These MEMD features show a significant performance gain over the conventional tempo-spectral features obtained by Fourier transform and Wavelet transform. The dominance of specific brain region is observed by analysing the MEMD features extracted from associated EEG channels. The frontal region is found to be most significant with a classification accuracy of 98.06%. This multi-dimensional decomposition approach upholds joint channel properties and produces most discriminative features for EEG based mental state detection.
