本文的第一作者为博士生魏玮，题目为“Reducing Calibration Efforts in RSVP Tasks with Multi-source Adversarial Domain Adaptation”，被 IEEE Transactions on Neural Systems and Rehabilitation Engineering (TNSRE) 录用。

以下为文章摘要：

Rapid Serial Visual Presentation (RSVP)-based Brain-Computer Interface (BCI) is an efficient information detection technology by detecting event-related brain responses evoked by target visual stimuli. However, a time-consuming calibration procedure is needed before a new user can use this system. Thus, it is important to reduce calibration efforts for BCI applications. In this paper, we propose a multi-source conditional adversarial domain adaptation with the correlation metric learning (mCADA-C) framework that utilizes data from other subjects to reduce the data requirement from the new subject for training the model. This model utilizes adversarial training to enable a CNN-based feature extraction network to extract common features from different domains. A correlation metric learning (CML) loss is proposed to constrain the correlation of features based on class and domain to maximize the intra-class similarity and minimize inter-class similarity. Also, a multi-source framework with a source selection strategy is adopted to integrate the results of multiple domain adaptation. We constructed an RSVP-based dataset that includes 11 subjects each performing three RSVP experiments on three different days. The experimental results demonstrate that our proposed method can achieve 87.72% cross-subject balanced-accuracy under one block calibration. The results indicate our method can realize a higher performance with less calibration efforts.

中文摘要参考：

基于快速序列视觉呈现（RSVP）的脑-机接口（BCI）是一种有效的信息检测技术，它通过检测目标视觉刺激引起的事件相关脑响应来实现对目标的检测。然而，在新用户使用该系统之前，需要一个耗时的校准程序。因此，减少BCI应用的校准过程是很重要的。本文提出了一种基于相关度量学习的多源条件对抗域自适应框架（mCADA-C），该框架利用其他被试的数据来减少新被试对模型训练的数据需求。该模型利用对抗性训练，使基于CNN的特征提取网络能够从不同域中提取共同的特征。同时提出了一种基于类和域的相关度量学习（CML）损失来约束特征之间的相关性，使类内相似度最大化，类间相似度最小化。同时，采用多源框架和源选择策略来整合多域自适应的结果。我们构建了一个基于RSVP的数据集，其中包括11名受试者，每个受试者分别在三天内进行了三次RSVP实验。实验结果表明，在一个校准block的情况下，我们提出的方法利用跨被试数据可以达到87.72%的平衡精度。结果表明，我们的方法可以用较少的校准工作量实现更高的性能。

文章链接：https://ieeexplore.ieee.org/document/9195541