This paper focuses on affective emotion recognition, aiming to perform in the subject-agnostic paradigm based on EEG signals. However, EEG signals manifest subject instability in subject-agnostic affective Brain-computer interfaces (aBCIs), which led to the problem of distributional shift. Furthermore, this problem is alleviated by approaches such as domain generalisation and domain adaptation. Typically, methods based on domain adaptation confer comparatively better results than the domain generalisation methods but demand more computational resources given new subjects. We propose a novel framework, meta-learning based augmented domain adaptation for subject-agnostic aBCIs. Our domain adaptation approach is augmented through meta-learning, which consists of a recurrent neural network, a classifier, and a distributional shift controller based on a sum-decomposable function. Also, we present that a neural network explicating a sum-decomposable function can effectively estimate the divergence between varied domains. The network setting for augmented domain adaptation follows meta-learning and adversarial learning, where the controller promptly adapts to new domains employing the target data via a few self-adaptation steps in the test phase. Our proposed approach is shown to be effective in experiments on a public aBICs dataset and achieves similar performance to state-of-the-art domain adaptation methods while avoiding the use of additional computational resources.

翻译：本文聚焦于情感情绪识别，旨在基于EEG信号在主体无关范式下进行识别。然而，EEG信号在主体无关的情感脑机接口（aBCIs）中表现出主体不稳定性，从而引发分布偏移问题。此外，该问题可通过域泛化与域适应等方法得到缓解。通常，基于域适应的方法比域泛化方法能获得相对更优的结果，但面对新主体时需消耗更多计算资源。我们提出一种新框架——基于元学习的增强域适应法，用于主体无关的aBCIs。我们的域适应方法通过元学习进行增强，该方法包含递归神经网络、分类器以及基于可分解函数的分布偏移控制器。同时，我们证明采用可分解函数的神经网络可有效估计不同域之间的散度。增强域适应的网络设置遵循元学习和对抗学习范式，其中控制器在测试阶段通过少量自适应步骤利用目标数据快速适应新域。实验表明，所提方法在公开aBCIs数据集上效果显著，能在避免额外计算资源消耗的前提下，达到与最先进域适应方法相近的性能。