Yohanes Rendi E J, Ser Wee, Huang Guang-bin
Nanyang Technological University, Singapore.
Annu Int Conf IEEE Eng Med Biol Soc. 2012;2012:2251-4. doi: 10.1109/EMBC.2012.6346410.
In this paper, we propose to use DWT coefficients as features for emotion recognition from EEG signals. Previous feature extraction methods used power spectra density values dervied from Fourier Transform or sub-band energy and entropy derived from Wavelet Transform. These feature extracion methods eliminate temporal information which are essential for analyzing EEG signals. The DWT coefficients represent the degree of correlation between the analyzed signal and the wavelet function at different instances of time; therefore, DWT coefficients contain temporal information of the analyzed signal. The proposed feature extraction method fully utilizes the simultaneous time-frequency analysis of DWT by preserving the temporal information in the DWT coefficients. In this paper, we also study the effects of using different wavelet functions (Coiflets, Daubechies and Symlets) on the performance of the emotion recognition system. The input EEG signals were obtained from two electrodes according to 10-20 system: F(p1) and F(p2). Visual stimuli from International Affective Picture System (IAPS) were used to induce two emotions: happy and sad. Two classifiers were used: Extreme Learning Machine (ELM) and Support Vector Machine (SVM). Experimental results confirmed that the proposed DWT coefficients method showed improvement of performance compared to previous methods.
在本文中，我们建议使用离散小波变换（DWT）系数作为从脑电信号中进行情感识别的特征。先前的特征提取方法使用从傅里叶变换得到的功率谱密度值，或从小波变换得到的子带能量和熵。这些特征提取方法消除了对分析脑电信号至关重要的时间信息。DWT系数表示在不同时刻分析信号与小波函数之间的相关程度；因此，DWT系数包含了被分析信号的时间信息。所提出的特征提取方法通过在DWT系数中保留时间信息，充分利用了DWT的同步时频分析。在本文中，我们还研究了使用不同小波函数（Coiflets、Daubechies和Symlets）对情感识别系统性能的影响。根据10-20系统，从两个电极（F(p1)和F(p2)）获取输入脑电信号。使用国际情感图片系统（IAPS）的视觉刺激来诱发两种情绪：快乐和悲伤。使用了两种分类器：极限学习机（ELM）和支持向量机（SVM）。实验结果证实，与先前方法相比，所提出的DWT系数方法在性能上有了提高。
