The tremendous progress of big data acquisition and processing in the field of neural engineering has enabled a better understanding of the patient’s brain disorders with their neural rehabilitation, restoration, detection, and diagnosis. An integration of compressive sensing (CS) and neural engineering emerges as a new research area, aiming to deal with a large volume of neurological data for fast speed, long-term, and energy-saving purposes. Furthermore, electroencephalography (EEG) signals for brain–computer interfaces (BCIs) have shown to be very promising, with diverse neuroscience applications. In this review, we focused on EEG-based approaches which have benefited from CS in achieving fast and energy-saving solutions. In particular, we examine the current practices, scientific opportunities, and challenges of CS in the growing field of BCIs. We emphasized on summarizing major CS reconstruction algorithms, the sparse basis, and the measurement matrix used in CS to process the EEG signal. This literature review suggests that the selection of a suitable reconstruction algorithm, sparse basis, and measurement matrix can help to improve the performance of current CS-based EEG studies. In this paper, we also aim at providing an overview of the reconstruction free CS approach and the related literature in the field. Finally, we discuss the opportunities and challenges that arise from pushing the integration of the CS framework for BCI applications.

中文翻译：

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脑电图信号处理应用的压缩感知趋势。

大数据采集和处理在神经工程领域的巨大进步使得人们能够更好地了解患者的脑部疾病及其神经康复、恢复、检测和诊断。压缩传感（CS）和神经工程的集成成为一个新的研究领域，旨在处理大量的神经数据，以实现快速、长期和节能的目的。此外，脑机接口（BCIs）的脑电图（EEG）信号已被证明非常有前途，具有多种神经科学应用。在这篇综述中，我们重点关注基于脑电图的方法，这些方法受益于 CS 来实现快速、节能的解决方案。我们特别研究了计算机科学在不断发展的脑机接口领域的当前实践、科学机遇和挑战。我们重点总结了主要的CS重建算法、稀疏基以及CS中用于处理EEG信号的测量矩阵。该文献综述表明，选择合适的重建算法、稀疏基和测量矩阵有助于提高当前基于 CS 的脑电图研究的性能。在本文中，我们还旨在概述免费重建 CS 方法和该领域的相关文献。最后，我们讨论了推动BCI应用程序集成CS框架所带来的机遇和挑战。