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.

翻译：传统的电子计算解码方法在应用静态、控制良好的实验室环境中获得的数据时取得了一定的成功;然而,开放世界环境是一个更现实的环境,在这种环境中,影响电子计算方法记录的情况可能会出人意料地出现,大大削弱现有方法的稳健性。近年来,深层次学习(DL)由于在特征提取方面的超强能力,已成为解决这类问题的一个潜在办法。它克服了界定“手工制作”特征或使用浅层结构提取的特征的局限性,但通常需要大量昂贵、有专家标签的数据――有些数据并不总是可以获得。将DL与特定领域知识相结合,可能有利于制定强有力的方法,使大脑活动即使与小范围数据解码。虽然提出了各种DL方法,以应对电子计算解码方面的一些挑战,但系统化的辅导性概览,特别是用于开放世界应用的概览,目前缺乏。这一条条为未来EGO-DCU的研究提供了一种具有前景的探索性的方向。