Wang Pengpai, Cao Xuhao, Zhou Yueying, Gong Peiliang, Yousefnezhad Muhammad, Shao Wei, Zhang Daoqiang
Key Laboratory of Brain-Machine Intelligence Technology, Ministry of Education, College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, Nanjing, China.
Front Neurosci. 2023 Jun 2;17:1086472. doi: 10.3389/fnins.2023.1086472. eCollection 2023.
The advance in neuroscience and computer technology over the past decades have made brain-computer interface (BCI) a most promising area of neurorehabilitation and neurophysiology research. Limb motion decoding has gradually become a hot topic in the field of BCI. Decoding neural activity related to limb movement trajectory is considered to be of great help to the development of assistive and rehabilitation strategies for motor-impaired users. Although a variety of decoding methods have been proposed for limb trajectory reconstruction, there does not yet exist a review that covers the performance evaluation of these decoding methods. To alleviate this vacancy, in this paper, we evaluate EEG-based limb trajectory decoding methods regarding their advantages and disadvantages from a variety of perspectives. Specifically, we first introduce the differences in motor execution and motor imagery in limb trajectory reconstruction with different spaces (2D and 3D). Then, we discuss the limb motion trajectory reconstruction methods including experiment paradigm, EEG pre-processing, feature extraction and selection, decoding methods, and result evaluation. Finally, we expound on the open problem and future outlooks.
在过去几十年中，神经科学和计算机技术的进步使脑机接口（BCI）成为神经康复和神经生理学研究中最具前景的领域。肢体运动解码逐渐成为BCI领域的一个热门话题。解码与肢体运动轨迹相关的神经活动被认为对运动受损用户的辅助和康复策略的发展有很大帮助。尽管已经提出了多种用于肢体轨迹重建的解码方法，但尚未有一篇综述涵盖这些解码方法的性能评估。为了填补这一空白，在本文中，我们从多个角度评估基于脑电图的肢体轨迹解码方法的优缺点。具体来说，我们首先介绍在不同空间（二维和三维）中肢体轨迹重建中运动执行和运动想象的差异。然后，我们讨论肢体运动轨迹重建方法，包括实验范式、脑电图预处理、特征提取与选择、解码方法以及结果评估。最后，我们阐述了开放问题和未来展望。
