IEEE Trans Neural Syst Rehabil Eng. 2022;30:2107-2115. doi: 10.1109/TNSRE.2022.3192413. Epub 2022 Aug 4.
Brain-computer interface (BCI) systems can be used to communicate and express desires from people with severe nervous system damage. Among BCI systems based on evoked responses, steady state visual evoked potential (SSVEP) responses are the most widely used. Canonical correlation analysis (CCA)-based methods have been widely used in SSVEP-based online BCIs due to their low computation and high speed, and many methods have been introduced to improve the results. In this research, a method for constructing reference signals used in CCA based on the amplified evoked response of brain is introduced. In the proposed method, after removing the latency in the training signals, to construct reference signals, multilayer perceptron neural networks of the fitting type are used instead of the usual sine/cosine signals. The results show the success of this method in boosting the evoked responses of brain. The detection accuracy in 100-second time windows was 100%, and the information transfer rate in the same period was 240 bits per minute. Making reference signals similar to the recorded electroencephalogram allowed us to make more similarities in the CCA between the signals under consideration, and the reference signals, and to dramatically improve the results.
脑机接口（BCI）系统可用于与严重神经系统损伤的人进行交流和表达意愿。在基于诱发反应的 BCI 系统中，稳态视觉诱发电位（SSVEP）反应是应用最广泛的。基于典型相关分析（CCA）的方法由于其计算量低、速度快而被广泛应用于基于 SSVEP 的在线 BCI 中，并且已经引入了许多方法来提高结果。在这项研究中，提出了一种基于大脑放大诱发电位的 CCA 参考信号构建方法。在提出的方法中，在去除训练信号中的潜伏期后，为了构建参考信号，使用拟合类型的多层感知机神经网络代替常用的正弦/余弦信号。结果表明，该方法成功地增强了大脑的诱发电位。在 100 秒的时间窗口中，检测准确率达到 100%，同一时期的信息传输率达到 240 位/分钟。使参考信号与记录的脑电图相似，使我们能够在考虑中的信号和参考信号之间的 CCA 中建立更多的相似性，并显著提高结果。
