  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.
