Annu Int Conf IEEE Eng Med Biol Soc. 2022 Jul;2022:284-287. doi: 10.1109/EMBC48229.2022.9871012.
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.
在这项研究中，多变量经验模态分解 (MEMD) 被应用于多通道 EEG 中，以获得与情绪变化相关的局部信号变化的尺度对齐固有模态函数 (IMF) 作为情绪检测的输入特征。IMF 捕获。在所提取的 IMF 中，发现高振荡 IMF 对预期任务具有重要意义。从所选 IMF 计算 Marginal Hilbert 谱 (MHS)。采用三维卷积神经网络 (CNN) 通过构建由多通道 MHS 堆叠在连续信号段上来进行时空频谱特征表示的情绪检测。所提出的方法在公开可用的 DEAP 数据库上进行了评估。在 valence 和 arousal 水平的二进制分类（高与低）中，分别达到了 89.25%和 86.23%的准确率，明显优于以前使用 2D CNN 和/或传统时间和频谱特征的报告系统。
