Li Mu, Lu Bao-Liang
Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
Annu Int Conf IEEE Eng Med Biol Soc. 2009;2009:1323-6. doi: 10.1109/IEMBS.2009.5334139.
In this paper, we use EEG signals to classify two emotions-happiness and sadness. These emotions are evoked by showing subjects pictures of smile and cry facial expressions. We propose a frequency band searching method to choose an optimal band into which the recorded EEG signal is filtered. We use common spatial patterns (CSP) and linear-SVM to classify these two emotions. To investigate the time resolution of classification, we explore two kinds of trials with lengths of 3s and 1s. Classification accuracies of 93.5% +/- 6.7% and 93.0%+/-6.2% are achieved on 10 subjects for 3s-trials and 1s-trials, respectively. Our experimental results indicate that the gamma band (roughly 30-100 Hz) is suitable for EEG-based emotion classification.
在本文中，我们使用脑电图（EEG）信号对两种情绪——快乐和悲伤进行分类。通过向受试者展示微笑和哭泣面部表情的图片来诱发这些情绪。我们提出了一种频带搜索方法，以选择一个最优频带，将记录的EEG信号滤波到该频带中。我们使用共同空间模式（CSP）和线性支持向量机（linear-SVM）对这两种情绪进行分类。为了研究分类的时间分辨率，我们探索了两种时长分别为3秒和1秒的试验。对于10名受试者，3秒试验和1秒试验的分类准确率分别达到了93.5%±6.7%和93.0%±6.2%。我们的实验结果表明，伽马波段（大致为30 - 100赫兹）适用于基于EEG的情绪分类。
