Feature Extraction and Selection for Emotion Recognition from EEG
Feature Extraction and Selection for Emotion Recognition from EEG
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DOI：
10.1109/taffc.2014.2339834
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发表时间：
2014-07-01
影响因子：
11.2
通讯作者：
Buss, Martin
中科院分区：
文献类型：
--
作者：
Jenke, Robert;Peer, Angelika;Buss, Martin
从EEG信号的情感识别允许直接评估用户的“内部”状态，这被认为是人机交互中的重要因素。已经研究了许多用于特征提取的方法，并且通常基于神经科学发现来选择适当的特征和电极位置。然而，它们对情感识别的适用性已经使用少量不同的特征集和不同的，通常是小数据集进行了测试。一个主要的局限性是，没有系统的比较功能存在。因此，我们回顾了33个研究的基础上，从脑电信号的情绪识别的特征提取方法。一个实验进行比较这些功能，使用机器学习技术的特征选择自记录的数据集。结果相对于不同的功能选择方法，使用选定的功能类型，和电极位置的选择性能。多变量方法选择的特征略优于单变量方法。先进的特征提取技术被发现具有优势，比常用的光谱功率带。结果还表明，偏好的顶叶和中央顶叶的位置。
Emotion recognition from EEG signals allows the direct assessment of the "inner" state of a user, which is considered an important factor in human-machine-interaction. Many methods for feature extraction have been studied and the selection of both appropriate features and electrode locations is usually based on neuro-scientific findings. Their suitability for emotion recognition, however, has been tested using a small amount of distinct feature sets and on different, usually small data sets. A major limitation is that no systematic comparison of features exists. Therefore, we review feature extraction methods for emotion recognition from EEG based on 33 studies. An experiment is conducted comparing these features using machine learning techniques for feature selection on a self recorded data set. Results are presented with respect to performance of different feature selection methods, usage of selected feature types, and selection of electrode locations. Features selected by multivariate methods slightly outperform univariate methods. Advanced feature extraction techniques are found to have advantages over commonly used spectral power bands. Results also suggest preference to locations over parietal and centro-parietal lobes.