IEEE Trans Biomed Eng. 2019 Oct;66(10):2869-2881. doi: 10.1109/TBME.2019.2897651. Epub 2019 Feb 5.
Spectral power analysis plays a predominant role in electroencephalogram-based emotional recognition. It can reflect activity differences among multiple brain regions. In addition to activation difference, different emotions also involve different large-scale network during related information processing. In this paper, both information propagation patterns and activation difference in the brain were fused to improve the performance of emotional recognition.
We constructed emotion-related brain networks with phase locking value and adopted a multiple feature fusion approach to combine the compensative activation and connection information for emotion recognition.
Recognition results on three public emotional databases demonstrated that the combined features are superior to either single feature based on power distribution or network character. Furthermore, the conducted feature fusion analysis revealed the common characters between activation and connection patterns involved in the positive, neutral, and negative emotions for information processing.
The proposed feasible combination of both information propagation patterns and activation difference in the brain is meaningful for developing the effective human-computer interaction systems by adapting to human emotions in the real world applications.
在基于脑电图的情感识别中，频谱功率分析起着主要作用。它可以反映多个脑区的活动差异。除了激活差异外，不同的情绪在相关信息处理过程中还涉及不同的大规模网络。在本文中，我们融合了信息传播模式和大脑中的激活差异，以提高情感识别的性能。
我们使用锁相值构建了与情绪相关的脑网络，并采用了一种多特征融合方法，将补偿激活和连接信息结合起来进行情感识别。
在三个公开的情感数据库上的识别结果表明，与基于功率分布或网络特征的单一特征相比，组合特征具有优越性。此外，进行的特征融合分析揭示了在正性、中性和负性情绪的信息处理中涉及的激活和连接模式之间的共同特征。
该研究提出了一种在大脑中信息传播模式和激活差异之间的可行组合，对于通过适应现实世界应用中的人类情感来开发有效的人机交互系统具有重要意义。
