Biomedical Engineering Department, Semnan University, Semnan, Iran.
Biomedical Engineering Department, Semnan University, Semnan, Iran.
J Neurosci Methods. 2020 May 15;338:108686. doi: 10.1016/j.jneumeth.2020.108686. Epub 2020 Mar 12.
BCI systems based on steady-state visual evoked potentials (SSVEP) have formed an immense contribution to practical applications, due to their high recognition accuracy and ease of use. The MLR method has a better frequency recognition accuracy for short-term windows, and the MsetCCA method works more accurately in long-term windows.
The proposed fuzzy ensemble system can analyze the relevant SSVEP signals of each subject from 0.5 to 4 s windows with 0.5 s incremental steps. It is capable of taking decisions to improve the accuracy of SSVEP stimulation frequency recognition using the MLR and MsetCCA methods.
Our fuzzy system provides high-accuracy results for the stimulation frequency recognition in signals with the length of 1 s and more. Specifically, the average accuracy of 2 s windows has improved to 100 percent.
The recognition accuracy of the presented system is always better than both MLR and MsetCCA methods.
One of the capabilities of fuzzy systems is that they can use human information and knowledge to build engineering systems. The fuzzy ensemble system can utilize various methods or classifiers simultaneously. The new system has proposed to combine multiple methods using the fuzzy ensemble, which encompasses the benefits of all the subsystems.
基于稳态视觉诱发电位 (SSVEP) 的 BCI 系统由于其识别精度高、使用方便，已经为实际应用做出了巨大贡献。MLR 方法在短期窗口中具有更好的频率识别精度，而 MsetCCA 方法在长期窗口中工作更准确。
所提出的模糊集成系统可以分析每个受试者的相关 SSVEP 信号，窗口长度从 0.5 秒到 4 秒，步长为 0.5 秒。它能够使用 MLR 和 MsetCCA 方法做出决策，以提高 SSVEP 刺激频率识别的准确性。
我们的模糊系统为 1 秒及以上长度的信号提供了高精度的刺激频率识别结果。具体来说，2 秒窗口的平均准确率提高到了 100%。
所提出系统的识别精度始终优于 MLR 和 MsetCCA 方法。
模糊系统的一个能力是它们可以利用人类的信息和知识来构建工程系统。模糊集成系统可以同时使用多种方法或分类器。新系统提出使用模糊集成来组合多种方法，从而包含所有子系统的优点。
