School of Information Science and Technology, Nantong University, Nantong, China.
Nantong Research Institute for Advanced Communication Technologies, Nantong University, Nantong, China.
Proc Inst Mech Eng H. 2023 Aug;237(8):1017-1028. doi: 10.1177/09544119231187287. Epub 2023 Aug 7.
The use of brain-computer interfaces (BCIs) to control intelligent devices is a current and future research direction. However, the challenges of low accuracy of real-time recognition and the need for multiple electroencephalographic channels are yet to be overcome. While a number of research teams have proposed many ways to improve offline classification accuracy, the potential problems in real-time experiments are often overlooked. In this study, we proposed a label-based channel diversion preprocessing to solve the problem of low real-time classification accuracy. The Tikhonov regularised common spatial-pattern algorithm (TRCSP) and one vs rest support vector machine (OVR-SVM) were used for feature extraction and pattern classification. High accuracy was achieved in real-time three-class classification using only three channels (average real-time accuracy of 87.46%, with a maximum of 90.33%). In addition, the stability and reliability of the system were verified through lighting control experiments in a real environment. Using the autonomy of MI and real-time feedback of light brightness, we have built a fully autonomous interactive system. The improvement in the real-time classification accuracy in this study is of great significance to the industrialisation of BCI.
脑机接口（BCI）用于控制智能设备是当前和未来的研究方向。然而，实时识别精度低和需要多个脑电图通道的挑战仍有待克服。虽然许多研究小组已经提出了许多方法来提高离线分类精度，但实时实验中的潜在问题往往被忽视。在本研究中，我们提出了一种基于标签的通道分流预处理方法，以解决实时分类精度低的问题。使用正则化共空间模式算法（TRCSP）和一对一支持向量机（OVR-SVM）进行特征提取和模式分类。仅使用三个通道即可实现实时三分类的高精度（平均实时精度为 87.46%，最高可达 90.33%）。此外，通过在真实环境中的灯光控制实验验证了系统的稳定性和可靠性。利用 MI 的自主性和光亮度的实时反馈，我们构建了一个完全自主的交互系统。本研究中实时分类精度的提高对 BCI 的产业化具有重要意义。
