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-computer interface with a short calibration 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 experiments are conducted on the DREAMER and DEAP datasets, and the results show the superiority of our proposed method.

中文翻译：

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基于原型的 SPD 矩阵网络，用于域适应 EEG 情绪识别

摘要 情绪在人类日常生活中起着至关重要的作用，脑电信号被广泛应用于情绪识别。由于个体差异，训练跨不同主题的通用情感识别模型很困难。传统方法涉及收集大量校准数据以构建特定主题的模型。最近，开发一种校准时间短的有效脑机接口已成为一项挑战。为了解决这个问题，我们提出了一种域适应 SPD 矩阵网络（daSPDnet），它可以成功地捕获不同主体之间共享的内在情感表征。我们的方法联合利用具有分布混淆的特征适应和具有质心对齐的样本适应。我们基于作为特征的协方差计算 SPD 矩阵，并尝试将原型学习与黎曼度量相结合。在 DREAMER 和 DEAP 数据集上进行了大量实验，结果显示了我们提出的方法的优越性。