Biomedical Engineering Department, Semnan University, Semnan, Iran.
Biomedical Engineering Department, Semnan University, Semnan, Iran.
Med Eng Phys. 2023 Jan;111:103945. doi: 10.1016/j.medengphy.2022.103945. Epub 2022 Dec 23.
BCI systems provide a direct communication channel between the human and the machine using brain signals. Among the various methods of steady-state visual evoked potential (SSVEP) stimulation frequency detection, multiple linear regression (MLR), and multiset canonical correlation analysis (MsetCCA) methods have achieved high accurate results in recent studies. The purpose of this study is to utilize both approaches and benefit from them using a genetic algorithm (GA). This algorithm leads to high-performance optimization due to its large number of regulatory parameters. Signal analysis was performed for the windows with 0.5 to 4 s duration length and with 0.5-second incremental steps. In this paper, we were able to achieve 100% accuracy of recognition for 2-second time-windows using the genetic algorithm to optimally ensemble SSVEP stimulation frequency detection methods. The accuracy of the proposed system indicates a significant improvement in detection compared to either MLR or MsetCCA alone and indicates that the ensemble system is correctly optimized using the genetic algorithm. Genetic algorithm is one of the most widely used algorithms because of its high regulatory parameters leading to its high flexibility. The improvement in detection of the proposed system is due to the use of the strengths of both two methods, and the optimal choice of the system response to visual stimuli.
脑机接口系统通过脑信号为人类和机器提供直接的通信通道。在稳态视觉诱发电位（SSVEP）刺激频率检测的各种方法中，多元线性回归（MLR）和多集典型相关分析（MsetCCA）方法在最近的研究中取得了高精度的结果。本研究的目的是利用这两种方法，并利用遗传算法（GA）从中受益。由于其大量的调节参数，该算法可实现高性能优化。信号分析针对持续时间为 0.5 到 4 秒且以 0.5 秒为增量步长的窗口进行。在本文中，我们能够通过遗传算法以最佳方式组合 SSVEP 刺激频率检测方法，实现 2 秒时窗的 100%识别准确率。所提出系统的准确率表明，与单独使用 MLR 或 MsetCCA 相比，检测有了显著提高，这表明使用遗传算法对组合系统进行了正确的优化。遗传算法是使用最广泛的算法之一，因为其具有大量的调节参数，从而具有很高的灵活性。所提出系统检测能力的提高是由于同时使用了两种方法的优势，以及对系统对视觉刺激的响应的最佳选择。
