Brain–computer interfaces (BCIs) employ neurophysiological signals derived from the brain to control computers or external devices. By enhancing or replacing human peripheral functioning capacity, BCIs offer supplementary degrees of freedom, significantly improving individuals’ quality of life, particularly offering hope for those with locked-in syndrome (LIS). Moreover, BCI applications have expanded across medical and nonmedical domains, including rehabilitation, clinical diagnosis, cognitive and affective computing, and gaming. Over the past decades, with a wealth of brain signals captured invasively or noninvasively, BCI has made spectacular progress. However, this also poses new challenges for signal processing techniques, such as characterization and classification. In this review, we first introduce signal enhancement and characterization methods to mine inherent patterns of nonstationary and time-varying brain signals. Then, we highlight widely adopted classification methods in BCI and the challenges they face. This article aims to comprehensively overview crucial signal processing techniques in BCI and provide suggestions for future directions.

中文翻译：

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脑机接口信号处理：回顾和当前观点

脑机接口（BCI）利用来自大脑的神经生理信号来控制计算机或外部设备。通过增强或替代人类外周功能能力，脑机接口提供了补充的自由度，显着改善了个人的生活质量，特别是为那些患有锁定综合症（LIS）的人带来了希望。此外，BCI 应用已扩展到医疗和非医疗领域，包括康复、临床诊断、认知和情感计算以及游戏。在过去的几十年里，随着侵入性或非侵入性捕获大量大脑信号，BCI 取得了惊人的进步。然而，这也给信号处理技术（例如表征和分类）带来了新的挑战。在这篇评论中，我们首先引入信号增强和表征方法来挖掘非平稳和时变大脑信号的固有模式。然后，我们重点介绍 BCI 中广泛采用的分类方法及其面临的挑战。本文旨在全面概述 BCI 中的关键信号处理技术，并为未来的方向提供建议。