论文标题
基于判别性大脑神经网络拓扑和合奏共同指控策略的分层情感识别框架
Hierarchical emotion-recognition framework based on discriminative brain neural network topology and ensemble co-decision strategy
论文作者
论文摘要
大脑神经网络是针对不同情绪状态的各种信息传播模式的特征。但是，基于传统图理论的统计特征可能会忽略空间网络差异。为了揭示这些固有的空间特征并提高情绪识别的稳定性，我们提出了一个分层框架，可以通过将监督学习与合奏共同确定策略相结合，可以通过多个与情感相关的空间网络拓扑模式（MESNP）执行多种情感识别（MESNP）。为了评估我们提出的MESNP方法的性能，我们使用两个公共数据集（即Mahnob和Deap）进行离线和模拟的在线实验。实验结果表明，MESNP可以显着提高多种情绪的分类性能。 MAHNOB-HCI和DEAP的离线实验的最高精度分别达到99.93％（3级）和83.66％（4类）。对于模拟的在线实验，我们还获得了MAHNOB的100％（3类）的最佳分类精度，而建议的MESNP则获得了DEAP的99.22％（4类）。这些结果进一步证明了MESNP在多分类情感任务中结构化特征提取的效率。
Brain neural networks characterize various information propagation patterns for different emotional states. However, the statistical features based on traditional graph theory may ignore the spacial network difference. To reveal these inherent spatial features and increase the stability of emotional recognition, we proposed a hierarchical framework that can perform the multiple emotion recognitions with the multiple emotion-related spatial network topology patterns (MESNP) by combining a supervised learning with ensemble co-decision strategy. To evaluate the performance of our proposed MESNP approach, we conduct both off-line and simulated on-line experiments with two public datasets i.e., MAHNOB and DEAP. The experiment results demonstrated that MESNP can significantly enhance the classification performance for the multiple emotions. The highest accuracies of off-line experiments for MAHNOB-HCI and DEAP achieved 99.93% (3 classes) and 83.66% (4 classes), respectively. For simulated on-line experiments, we also obtained the best classification accuracies with 100% (3 classes) for MAHNOB and 99.22% (4 classes) for DEAP by proposed MESNP. These results further proved the efficiency of MESNP for structured feature extraction in mult-classification emotional task.
