本文第一作者为博士生王奕欣，题目为：“A prototype-based SPD matrix network for domain adaptation EEG emotion recognition”发表在Pattern Recognition

ABSTRACT 

Emotion plays a vital role in human daily life, and EEG signals are widely used in emotion recognition. Due to individual variability, training a generic emotion recognition model across different subjects is difficult. The conventional method involves the collection of a large amount of calibration data to build subject-specific models. Recently, developing an effective brain-com puter interface with a short calibra-tion time has become a challenge. To solve this problem, we propose a domain adaptation SPD matrix network (daSPDnet) that can successfully capture an intrinsic emotional representation shared between different subjects. Our method jointly exploits feature adaptation with distribution confusion and sample adaptation with centroid alignment. We compute the SPD matrix based on the covariance as a feature and make a novel attempt to combine prototype learning with the Riemannian metric. Extensive exper-iments are conducted on the DREAMER and DEAP datasets, and the results show the superiority of our proposed method.

中文摘要

情绪在人类日常生活中起着至关重要的作用，脑电信号被广泛用于情绪识别中。由于个体差异性，很难在不同被试之间训练通用的情感识别模型。传统方法涉及收集大量校准数据以建立特定于被试的模型。近来，开发具有短校准时间的有效的人机界面已经成为挑战。为解决此问题，我们提出了一种域适应SPD矩阵网络（daSPDnet），该网络可以成功捕获不同主体之间共享的内在情感表示。我们的方法同时使用了通过混淆分布实现的特征自适应和通过质心对准实现的样本自适应。我们基于协方差作为特征来计算SPD矩阵，并尝试将原型学习与黎曼度量相结合。我们在DREAMER和DEAP数据集上进行了广泛的实验，结果表明了我们提出的方法的优越性。

文章链接：https://www.sciencedirect.com/science/article/abs/pii/S0031320320304295