Department of Electrical Electronics Engineering, Nuh Naci Yazgan University, 38090 Kayseri, Turkey.
Department of Business Administration, Nuh Naci Yazgan University, 38090 Kayseri, Turkey.
Comput Math Methods Med. 2022 May 4;2022:8452002. doi: 10.1155/2022/8452002. eCollection 2022.
This research was aimed at presenting performance of 3-dimensional input convolutional neural networks for steady-state visual evoked potential classification in a wireless EEG-based brain-computer interface system. Overall performance of a brain-computer interface system depends on information transfer rate. Parameters such as signal classification accuracy rate, signal stimulator structure, and user task completion time affect information transfer rate. In this study, we used 3 types of signal classification methods that are 1-dimensional, 2-dimensional, and 3-dimensional input convolutional neural network. According to online experiment with using 3-dimensional input convolutional neural network, we reached average classification accuracy rate and average information transfer rate as 93.75% and 58.35 bit/min, respectively. This both results significantly higher than the other methods that we used in experiments. Moreover, user task completion time was reduced with using 3-dimensional input convolutional neural network. Our proposed method is novel and state-of-art model for steady-state visual evoked potential classification.
本研究旨在展示三维输入卷积神经网络在基于无线 EEG 的脑机接口系统中进行稳态视觉诱发电位分类的性能。脑机接口系统的整体性能取决于信息传输率。信息传输率受信号分类准确率、信号刺激器结构和用户任务完成时间等参数的影响。在这项研究中，我们使用了 3 种信号分类方法，即一维、二维和三维输入卷积神经网络。根据使用三维输入卷积神经网络的在线实验，我们分别达到了平均分类准确率和平均信息传输率 93.75%和 58.35 bit/min，均显著高于我们在实验中使用的其他方法。此外，使用三维输入卷积神经网络还减少了用户任务完成时间。我们提出的方法是一种新颖的、最先进的稳态视觉诱发电位分类模型。
