论文标题
使用3D CNN从脑电图检测基于MEMD-HHT的情绪
MEMD-HHT based Emotion Detection from EEG using 3D CNN
论文作者
论文摘要
在这项研究中，将多元经验模式分解（MEMD）应用于多通道EEG，以获得与尺度一致的固有模式函数（IMF）作为情绪检测的输入特征。 IMF捕获与情绪变化有关的局部信号变化。在提取的IMF中，发现高振荡性的IMF对于预期的任务很重要。边缘希尔伯特光谱（MHS）是从所选IMFS计算的。采用了3D卷积神经网络（CNN）来执行情绪检测，并使用空间频谱特征表示，这些特征表示是通过在连续信号段上堆叠多通道MHS来构建的。在公开可用的DEAP数据库中评估了所提出的方法。关于价和唤醒水平的二元分类（高对低），所达到的精度分别为89.25％和86.23％，这显着超过了先前报道的具有2D CNN和/或常规时间和光谱特征的系统。
In this study, the Multivariate Empirical Mode Decomposition (MEMD) is applied to multichannel EEG to obtain scale-aligned intrinsic mode functions (IMFs) as input features for emotion detection. The IMFs capture local signal variation related to emotion changes. Among the extracted IMFs, the high oscillatory ones are found to be significant for the intended task. The Marginal Hilbert spectrum (MHS) is computed from the selected IMFs. A 3D convolutional neural network (CNN) is adopted to perform emotion detection with spatial-temporal-spectral feature representations that are constructed by stacking the multi-channel MHS over consecutive signal segments. The proposed approach is evaluated on the publicly available DEAP database. On binary classification of valence and arousal level (high versus low), the attained accuracies are 89.25% and 86.23% respectively, which significantly outperform previously reported systems with 2D CNN and/or conventional temporal and spectral features.
