Freer Daniel, Yang Guang-Zhong
Hamlyn Centre, Imperial College London, 1 Exhibition Road, London, SW7 2AZ, United Kingdom. Author to whom any correspondence should be addressed.
J Neural Eng. 2020 Jan 31;17(1):016041. doi: 10.1088/1741-2552/ab57c0.
Brain-computer interfaces (BCI) are becoming important tools for assistive technology, particularly through the use of motor imagery (MI) for aiding task completion. However, most existing methods of MI classification have been applied in a trial-wise fashion, with window sizes of approximately 2 s or more. Application of this type of classifier could cause a delay when switching between MI events.
In this study, state-of-the-art classification methods for motor imagery are assessed offline with considerations for real-time and self-paced control, and a convolutional long-short term memory (C-LSTM) network based on filter bank common spatial patterns (FBCSP) is proposed. In addition, the effects of several methods of data augmentation on different classifiers are explored.
The results of this study show that the proposed network achieves adequate results in distinguishing between different control classes, but both considered deep learning models are still less reliable than a Riemannian MDM classifier. In addition, controlled skewing of the data and the explored data augmentation methods improved the average overall accuracy of the classifiers by 14.0% and 5.3%, respectively.
This manuscript is among the first to attempt combining convolutional and recurrent neural network layers for the purpose of MI classification, and is also one of the first to provide an in-depth comparison of various data augmentation methods for MI classification. In addition, all of these methods are applied on smaller windows of data and with consideration to ambient data, which provides a more realistic test bed for real-time and self-paced control.
脑机接口（BCI）正成为辅助技术的重要工具，特别是通过使用运动想象（MI）来辅助任务完成。然而，大多数现有的MI分类方法都是以逐次试验的方式应用的，窗口大小约为2秒或更长。这种类型的分类器在MI事件之间切换时可能会导致延迟。
在本研究中，对用于运动想象的先进分类方法进行离线评估，同时考虑实时和自定节奏控制，并提出了一种基于滤波器组公共空间模式（FBCSP）的卷积长短期记忆（C-LSTM）网络。此外，还探讨了几种数据增强方法对不同分类器的影响。
本研究结果表明，所提出的网络在区分不同控制类别方面取得了足够的结果，但所考虑的两种深度学习模型仍然不如黎曼多变量判别（MDM）分类器可靠。此外，数据的受控偏斜和所探索的数据增强方法分别将分类器的平均总体准确率提高了14.0%和5.3%。
本手稿是最早尝试将卷积神经网络层和循环神经网络层结合用于MI分类的研究之一，也是最早对MI分类的各种数据增强方法进行深入比较的研究之一。此外，所有这些方法都应用于较小的数据窗口，并考虑了环境数据，这为实时和自定节奏控制提供了更现实的测试平台。
