论文标题
通过多种模态生理信号识别的合奏情绪
Ensemble emotion recognizing with multiple modal physiological signals
论文作者
论文摘要
在情绪识别领域中，越来越多的人的注意力引起了人们对人类情感状态的客观抑制的生理信号。但是，单个信号很难完全准确地描述情绪。多个生理信号融合模型，通过一致和互补的信息从不同情绪中构建统一的分类模型，以提高识别性能。原始融合模型通常选择识别特定的分类方法，该方法忽略了多个信号的不同分布。针对上述问题，在这项工作中，我们通过多种模态生理信号提出了一种情感分类模型，以实现不同的情绪。特征是从脑电图，EMG，EOG信号中提取的，用于表征在价和唤醒水平上的情绪状态。为了进行表征，采用了四个频带过滤Theta，beta，alpha，伽玛进行信号预处理的伽马，并且将三个Hjorth参数计算为特征。为了提高分类性能，建立了合奏分类器。实验是在基准DEAP数据集上进行的。对于两类任务，唤醒的最佳结果是94.42 \％，价值的最佳结果分别为94.02 \％。对于四级任务，最高的平均分类精度为90.74，并且显示出良好的稳定性。本文还分析了不同外围生理信号对结果的影响。
Physiological signals that provide the objective repression of human affective states are attracted increasing attention in the emotion recognition field. However, the single signal is difficult to obtain completely and accurately description for emotion. Multiple physiological signals fusing models, building the uniform classification model by means of consistent and complementary information from different emotions to improve recognition performance. Original fusing models usually choose the particular classification method to recognition, which is ignoring different distribution of multiple signals. Aiming above problems, in this work, we propose an emotion classification model through multiple modal physiological signals for different emotions. Features are extracted from EEG, EMG, EOG signals for characterizing emotional state on valence and arousal levels. For characterization, four bands filtering theta, beta, alpha, gamma for signal preprocessing are adopted and three Hjorth parameters are computing as features. To improve classification performance, an ensemble classifier is built. Experiments are conducted on the benchmark DEAP datasets. For the two-class task, the best result on arousal is 94.42\%, the best result on valence is 94.02\%, respectively. For the four-class task, the highest average classification accuracy is 90.74, and it shows good stability. The influence of different peripheral physiological signals for results is also analyzed in this paper.
