Augmented Reality for Health Monitoring Laboratory (ARHeMLab), Università degli Studi di Napoli Federico II, Naples, Italy.
Department of Electrical Engineering and Information Technology (DIETI), Università degli Studi di Napoli Federico II, Naples, Italy.
J Neural Eng. 2022 Jun 17;19(3). doi: 10.1088/1741-2552/ac74e0.
Processing strategies are analyzed with respect to the classification of electroencephalographic signals related to brain-computer interfaces (BCIs) based on motor imagery (MI). A review of literature is carried out to understand the achievements in MI classification, the most promising trends, and the challenges in replicating these results. Main focus is placed on performance by means of a rigorous metrological analysis carried out in compliance with the international vocabulary of metrology. Hence, classification accuracy and its uncertainty are considered, as well as repeatability and reproducibility.The paper works included in the review concern the classification of electroencephalographic signals in motor-imagery-based BCIs. Article search was carried out in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analyses standard and 89 studies were included.Statistically-based analyses show that brain-inspired approaches are increasingly proposed, and that these are particularly successful in discriminating against multiple classes. Notably, many proposals involve convolutional neural networks. Instead, classical machine learning approaches are still effective for binary classifications. Many proposals combine common spatial pattern, least absolute shrinkage and selection operator, and support vector machines. Regarding reported classification accuracies, performance above the upper quartile is in the 85%-100% range for the binary case and in the 83%-93% range for multi-class one. Associated uncertainties are up to 6% while repeatability for a predetermined dataset is up to 8%. Reproducibility assessment was instead prevented by lack of standardization in experiments.By relying on the analyzed studies, the reader is guided towards the development of a successful processing strategy as a crucial part of a BCI. Moreover, it is suggested that future studies should extend these approaches on data from more subjects and with custom experiments, even by investigating online operation. This would also enable the quantification of the results reproducibility.
针对基于运动想象（MI）的脑机接口（BCI）的脑电图信号分类，分析了处理策略。进行了文献回顾，以了解 MI 分类方面的成就、最有前途的趋势以及复制这些结果所面临的挑战。主要重点是通过严格的计量分析来评估性能，该分析符合国际计量词汇。因此，考虑了分类准确性及其不确定性，以及重复性和再现性。综述中包含的论文涉及基于运动想象的脑电图信号在基于脑机接口中的分类。文章检索符合系统评价和荟萃分析的首选报告项目标准，共纳入 89 项研究。基于统计的分析表明，越来越多地提出了基于大脑启发的方法，这些方法在区分多个类别方面特别成功。值得注意的是，许多提议都涉及卷积神经网络。相反，经典的机器学习方法仍然适用于二进制分类。许多提议将常见空间模式、最小绝对收缩和选择算子以及支持向量机结合起来。关于报告的分类准确性，二进制情况下性能高于上四分位数的范围为 85%-100%，多类情况下的范围为 83%-93%。相关不确定性高达 6%，而预定数据集的重复性高达 8%。然而，由于实验缺乏标准化，无法进行可重复性评估。通过依赖分析的研究，读者可以在发展成功的处理策略方面得到指导，这是 BCI 的关键部分。此外，建议未来的研究应该在更多的受试者数据和定制实验上扩展这些方法，甚至可以在线操作。这也将能够量化结果的可重复性。
