Department of Electrical, Computer, and Biomedical Engineering, Ryerson University, Toronto, ON M5B 2K3, Canada.
Postgraduate Program in Electrical Engineering, Federal University of Espirito Santo, Vitoria 29075-910, Brazil.
Sensors (Basel). 2020 Jul 2;20(13):3703. doi: 10.3390/s20133703.
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.
大数据采集和处理在神经工程领域的巨大进展使我们能够更好地理解患者的大脑障碍，并进行神经康复、修复、检测和诊断。压缩感知 (CS) 和神经工程的融合成为一个新的研究领域，旨在处理大量的神经学数据，以实现快速、长期和节能的目标。此外，脑电图 (EEG) 信号在脑机接口 (BCI) 中表现出非常有前途的应用，具有多样化的神经科学应用。在这篇综述中，我们专注于基于 EEG 的方法，这些方法得益于 CS 实现快速和节能的解决方案。特别是，我们研究了 CS 在不断发展的 BCI 领域中的当前实践、科学机遇和挑战。我们强调总结 CS 处理 EEG 信号中使用的主要 CS 重建算法、稀疏基和测量矩阵。本文献综述表明，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 EEG 研究的性能。在本文中，我们还旨在概述该领域中无重建 CS 方法和相关文献。最后，我们讨论了将 CS 框架推向 BCI 应用集成所带来的机遇和挑战。
