  Electroencephalogram (EEG) decoding aims to identify the perceptual,
semantic, and cognitive content of neural processing based on non-invasively
measured brain activity. Traditional EEG decoding methods have achieved
moderate success when applied to data acquired in static, well-controlled lab
environments. However, an open-world environment is a more realistic setting,
where situations affecting EEG recordings can emerge unexpectedly,
significantly weakening the robustness of existing methods. In recent years,
deep learning (DL) has emerged as a potential solution for such problems due to
its superior capacity in feature extraction. It overcomes the limitations of
defining `handcrafted' features or features extracted using shallow
architectures, but typically requires large amounts of costly,
expertly-labelled data - something not always obtainable. Combining DL with
domain-specific knowledge may allow for development of robust approaches to
decode brain activity even with small-sample data. Although various DL methods
have been proposed to tackle some of the challenges in EEG decoding, a
systematic tutorial overview, particularly for open-world applications, is
currently lacking. This article therefore provides a comprehensive survey of DL
methods for open-world EEG decoding, and identifies promising research
directions to inspire future studies for EEG decoding in real-world
applications.

Signal Processing;Human-Computer Interaction;Machine Learning