In brain computer interface (BCI), many transformation methods are used when processing electroencephalogram (EEG) signals. Thus, the EEG can be represented in different domains. However, designing an EEG-based BCI system without any transformation technique is a challenge. For this purpose, in this study, a BCI model is proposed without any transformation. The classification of cursor down and cursor up movements using the EEG signals received from the brain is aimed at in the proposed model. The EEG patterns were classified using two methods. Firstly, EEG signals were classified by classic convolutional neural network (CNN). Secondly, proposed hybrid structure obtained the EEG features, which were classified by k-NN and SVM, using CNN. Classification with CNN architecture gave a result of 68.15% while the hybrid method using k-NN and SVM classifiers yielded 97.55% and 97.61% respectively. The hybrid proposed method were more successful than the studies in the literature.

中文翻译：

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基于深度学习的混合方法对光标移动相关脑电记录进行分类

在脑机接口 (BCI) 中，处理脑电图 (EEG) 信号时使用了许多转换方法。因此，EEG 可以在不同的域中表示。然而，在没有任何转换技术的情况下设计基于 EEG 的 BCI 系统是一个挑战。为此，在本研究中，提出了一个没有任何转换的 BCI 模型。所提出的模型旨在使用从大脑接收到的 EEG 信号对光标向下和向上移动进行分类。使用两种方法对 EEG 模式进行分类。首先，通过经典的卷积神经网络（CNN）对脑电信号进行分类。其次，提出的混合结构使用CNN获得了由k-NN和SVM分类的EEG特征。使用 CNN 架构进行分类的结果为 68。15% 而使用 k-NN 和 SVM 分类器的混合方法分别产生 97.55% 和 97.61%。混合提出的方法比文献中的研究更成功。