摘要
为了找到一种综合分析方法,提高对脑电信号情感多分类识别的分类精确度,将DEAP数据库中的脑电数据采用经验模态分解的方法分解为多个本征模函数,并对本征模函数按不同的时长窗口进行分片,提取其功率谱密度作为脑电信号特征.将被试对音乐视频的情感评价指数用于生成情感分类标签,按"唤醒度"和"效价"2个维度将评价指数映射到二维情感模型中,分成4类.采用"一对一"的高斯核函数支持向量机对脑电特征进行多分类分析.实验结果表明:高斯核函数支持向量机的最高分类准确度达到90.9%(22号被试),平均分类准确度达到68.3%.高斯核函数支持向量机能有效地从脑电信号中识别出不同的情感状态;同时,对于相同刺激,不同的被试产生的情感状态不同;并且,在清醒状态下,脑电信号的高频子波对情感分类有更高的分类精确度.
An integrated method was proposed to achieve the objective accuracy of emotion recognition from the electroencephalograph （EEG） signal. The EEG signals from DEAP data set were decomposed into multiple intrinsic mode functions （IMFs） with the empirical mode decomposition （ EMD） method.After that, the power spectrum density was extracted as the EEG feature from the IMFs with different time windows. The emotion estimated scales of the subjects were mapped in the Valence-Arousal emotion model to be clustered into 4 classes. Gaussian kernel function support vector machine （ SVM） was adopted to classify the emotion states. ＂One versus one＂ model was employed within the SVM to deal with the multi-classification problem. Results show that the highest accuracy of the emotion classification obtained by the Gaussian kernel function SVM acquires is 90.9 % （with subject 22）, and the mean accuracy is 68. 31%. The results explain that the method can recognize different emotion from EEG signal effectively, and that different participants have different emotion experiences with the same stimulus,and that the EEG features from high frequency IMFs achieve higher emotion classification accuracy than that from low frequency IMFs.
出处
《北京工业大学学报》
CAS
CSCD
北大核心
2018年第2期234-243,共10页
Journal of Beijing University of Technology
基金
国家重点基础研究发展计划资助项目(2014CB744600)
国家自然科学基金资助项目(61420106005,61602017)
国家国际科技合作专项资助项目(2013DFA32180)
关键词
髙斯核函数
支持向量机
情感分类
脑电图(EEG)
经验模态分解(EMD)
本征模函数(IMF)
Gaussian kernel function
support vector machine （ SVM）
emotion classification
electroencephalography （ EEG）
empirical mode decomposition （EMD）
intrinsic mode function （ IMF）
