Annu Int Conf IEEE Eng Med Biol Soc. 2022 Jul;2022:3694-3697. doi: 10.1109/EMBC48229.2022.9871890.
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.
在这项研究中，多元经验模态分解（MEMD）方法被应用于从多通道 EEG 信号中提取特征，以进行心理状态分类。MEMD 是一种数据自适应分析方法，特别适用于 EEG 等多维非线性信号。应用 MEMD 会产生一组称为固有模态函数（IMF）的振荡模式。由于分解过程是数据相关的，因此 IMF 会根据功能大脑活动引起的信号变化而变化。在所提取的 IMF 中，发现那些对应于高振荡模式的 IMF 对于检测不同的心理状态最有用。从对心理状态检测贡献最大的 IMF 中计算出非线性特征。与通过傅里叶变换和小波变换获得的传统时频谱特征相比，这些 MEMD 特征显示出显著的性能提升。通过分析与相关 EEG 通道提取的 MEMD 特征，可以观察到特定脑区的主导地位。发现额叶区域最为显著，分类准确率为 98.06%。这种多维分解方法支持联合通道特性，并为基于 EEG 的心理状态检测生成最具判别力的特征。
