Glasgow College, University of Electronic Science and Technology of China, 611731, China.
Center of Future Media, School of Computer Science and Engineering, University of Electronic Science and Technology of China, 611731, China.
Comput Math Methods Med. 2020 Jun 18;2020:6427305. doi: 10.1155/2020/6427305. eCollection 2020.
Since the 21st century, noninvasive brain-computer interface (BCI) has developed rapidly, and brain-computer devices have gradually moved from the laboratory to the mass market. Among them, the TGAM (ThinkGear Asic Module) and its encapsulate algorithm have been adopted by many research teams and faculty members around the world. However, due to the limited development cost, the effectiveness of the algorithm to calculate data is not satisfactory. This paper proposes an attention optimization algorithm based on the TGAM for EEG data feedback. Considering that the data output of the TGAM encapsulate algorithm fluctuates greatly, the delay is high and the accuracy is low. The experimental results demonstrated that our algorithm can optimize EEG data, so that with the same or even lower delay and without changing the encapsulate algorithm of the module itself, it can significantly improve the performance of attention data, greatly improve the stability and accuracy of data, and achieve better results in practical applications.
自 21 世纪以来，非侵入式脑机接口（BCI）发展迅速，脑机设备逐渐从实验室走向大众市场。其中，TGAM（ThinkGear Asic Module）及其封装算法被世界各地的许多研究团队和教职员工采用。然而，由于开发成本有限，算法对数据的计算效果并不尽如人意。本文提出了一种基于 TGAM 的 EEG 数据反馈注意力优化算法。考虑到 TGAM 封装算法的数据输出波动较大，延迟高，精度低。实验结果表明，我们的算法可以优化 EEG 数据，使得在相同甚至更低的延迟下，并且不改变模块本身的封装算法的情况下，显著提高注意力数据的性能，大大提高数据的稳定性和准确性，在实际应用中取得更好的效果。
