论文标题
Spectro Perimal EEG生物标志物用于二进制情绪分类
Spectro Temporal EEG Biomarkers For Binary Emotion Classification
论文作者
论文摘要
脑电图（EEG）是情绪检测最可靠的生理信号之一。本质上是非平稳的，通过光谱时间表示可以更好地分析脑电图。诸如离散小波转换（DWT）之类的标准功能可以代表脑电图的光谱动力学的时间变化，但不足以其他方式提取信息，即时间动力学的光谱变化。另一方面，基于经验模式分解（EMD）特征对于弥合上述差距很有用。朝这个方向朝着这个方向迈出了两个新型特征，即（a）基于EMD的边缘希尔伯特光谱（MHS）和（b）Holo-Hilbert Spectral Analysis（HHSA），以更好地表示2D唤醒价值（A-V）空间中的情绪。这些特征对脑电图分类的有用性通过使用最新分类器进行广泛的实验研究。此外，在DEAP数据集上进行的实验以在两个A-V空间中进行二进制情绪分类，揭示了所提出的特征在标准的时间和光谱特征集中的功效。
Electroencephalogram (EEG) is one of the most reliable physiological signal for emotion detection. Being non-stationary in nature, EEGs are better analysed by spectro temporal representations. Standard features like Discrete Wavelet Transformation (DWT) can represent temporal changes in spectral dynamics of an EEG, but is insufficient to extract information other way around, i.e. spectral changes in temporal dynamics. On the other hand, Empirical mode decomposition (EMD) based features can be useful to bridge the above mentioned gap. Towards this direction, we extract two novel features on top of EMD, namely, (a) marginal hilbert spectrum (MHS) and (b) Holo-Hilbert spectral analysis (HHSA) based on EMD, to better represent emotions in 2D arousal-valence (A-V) space. The usefulness of these features for EEG emotion classification is investigated through extensive experiments using state-of-the-art classifiers. In addition, experiments conducted on DEAP dataset for binary emotion classification in both A-V space, reveal the efficacy of the proposed features over the standard set of temporal and spectral features.
