Towards Subject Agnostic Affective Emotion Recognition

TL;DR

This paper introduces a meta-learning based domain adaptation framework for subject-agnostic EEG-based emotion recognition, achieving state-of-the-art results with reduced computational costs.

Contribution

It proposes a novel meta-learning augmented domain adaptation method that effectively handles distributional shifts in EEG signals without extra computational resources.

Findings

Achieves comparable performance to state-of-the-art methods.

Reduces computational resources needed for adaptation.

Effective in experiments on a public aBCIs dataset.

Abstract

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.