Toward Open-World Electroencephalogram Decoding Via Deep Learning: A comprehensive survey
Toward Open-World Electroencephalogram Decoding Via Deep Learning: A comprehensive survey
复制标题
通过深度学习实现开放世界脑电图解码：一项综合调查
DOI：
10.1109/msp.2021.3134629
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发表时间：
2022-03-01
影响因子：
14.9
通讯作者：
Wang, Z. Jane
中科院分区：
文献类型：
--
作者：
Chen, Xun;Li, Chang;Wang, Z. Jane
脑电解码的目的是基于非侵入性测量的大脑活动来识别神经处理的感知、语义和认知内容。传统的EEG解码方法在应用于静态、良好控制的实验室环境中采集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，其中影响EEG记录的情况可能会意外出现，显着削弱现有方法的鲁棒性。近年来，深度学习（DL）由于其在特征提取方面的上级能力而成为此类问题的潜在解决方案。它克服了定义手工制作的特征或使用浅架构提取的特征的局限性，但通常需要大量昂贵的、专业标记的数据，而这些数据并不总是可以获得的。将深度学习与特定领域的知识相结合，可以开发出强大的方法，即使在小样本数据的情况下也可以解码大脑活动。虽然已经提出了各种DL技术来解决EEG解码中的一些挑战，但目前缺乏系统的教程概述，特别是对于开放世界应用。因此，本文提供了一个全面的调查开放世界EEG解码的DL方法，并确定有前途的研究方向，以启发未来的研究EEG解码在现实世界中的应用。
Electroencephalogram (EEG) decoding aims to identify the perceptual, semantic, and cognitive content of neural processing based on noninvasively 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 labeled data, something not always obtainable. Combining DL with domain-specific knowledge may allow for the development of robust approaches that decode brain activity even with small-sample data. Although various DL techniques 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.