Jirayucharoensak Suwicha, Pan-Ngum Setha, Israsena Pasin
Department of Computer Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok 10330, Thailand ; National Electronics and Computer Technology Center, Thailand Science Park, Khlong Luang, Pathum Thani 12120, Thailand.
Department of Computer Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok 10330, Thailand.
ScientificWorldJournal. 2014;2014:627892. doi: 10.1155/2014/627892. Epub 2014 Sep 1.
Automatic emotion recognition is one of the most challenging tasks. To detect emotion from nonstationary EEG signals, a sophisticated learning algorithm that can represent high-level abstraction is required. This study proposes the utilization of a deep learning network (DLN) to discover unknown feature correlation between input signals that is crucial for the learning task. The DLN is implemented with a stacked autoencoder (SAE) using hierarchical feature learning approach. Input features of the network are power spectral densities of 32-channel EEG signals from 32 subjects. To alleviate overfitting problem, principal component analysis (PCA) is applied to extract the most important components of initial input features. Furthermore, covariate shift adaptation of the principal components is implemented to minimize the nonstationary effect of EEG signals. Experimental results show that the DLN is capable of classifying three different levels of valence and arousal with accuracy of 49.52% and 46.03%, respectively. Principal component based covariate shift adaptation enhances the respective classification accuracy by 5.55% and 6.53%. Moreover, DLN provides better performance compared to SVM and naive Bayes classifiers.
自动情感识别是最具挑战性的任务之一。为了从非平稳脑电信号中检测情感，需要一种能够表示高级抽象的复杂学习算法。本研究提出利用深度学习网络（DLN）来发现输入信号之间未知的特征相关性，这对于学习任务至关重要。DLN采用分层特征学习方法，通过堆叠自编码器（SAE）实现。网络的输入特征是来自32名受试者的32通道脑电信号的功率谱密度。为了缓解过拟合问题，应用主成分分析（PCA）来提取初始输入特征的最重要成分。此外，还对主成分进行协变量偏移自适应，以最小化脑电信号的非平稳效应。实验结果表明，DLN能够分别以49.52%和46.03%的准确率对三种不同水平的效价和唤醒度进行分类。基于主成分的协变量偏移自适应分别将分类准确率提高了5.55%和6.53%。此外，与支持向量机（SVM）和朴素贝叶斯分类器相比，DLN具有更好的性能。
