Deep neural networks (DNNs) are observed to be successful in pattern classification. However, high classification performances of DNNs are related to their large training sets. Unfortunately, in the literature, the datasets used to classify motor imagery (MI) electroencephalogram (EEG) signals contain a small number of samples. To achieve high performances with small-sized datasets, most of the studies have employed a transformation such as common spatial patterns (CSP) before the classification process. However, CSP is dependent on subjects and introduces computational load in real-time applications. It is observed in the literature that the augmentation process is not applied for increasing the classification performance of EEG signals. In this study, we have investigated the effect of the augmentation process on the classification performance of MI EEG signals instead of using a preceding transformation such as the CSP, and we have demonstrated that by resulting in high success rates for the classification of MI EEGs, the augmentation process is able to compete with the CSP. In addition to the augmentation process, we modified the DNN structure to increase the classification performance, to decrease the number of nodes in the structure, and to be used with less number of hyper parameters. A minimum distance network (MDN) following the last layer of the convolutional neural network (CNN) was used as the classifier instead of a fully connected neural network (FCNN). By augmenting the EEG dataset and focusing solely on CNN's training, the training algorithm of the proposed structure is strengthened without applying any transformation. We tested these improvements on brain-computer interface (BCI) competitions 2005 and 2008 databases with two and four classes, and the high impact of the augmentation on the average performances are demonstrated.
翻译:深心神经网络(DNN)被认为在模式分类方面是成功的。然而,DNN的高度分类性能被认为在模式分类方面是成功的。然而,DNN的高度分类性能与它们的大量培训相联。不幸的是,在文献中,用于对运动图像(MI)电脑图(EEEG)信号进行分类的数据集包含少量样本。为了在小规模数据集中实现高性能,大多数研究在分类过程之前采用了通用空间模式(CSP)等高性能转换。然而,CSP依赖于主题,在实时应用程序中引入计算负荷。据文献中发现,DNNNN的增强性能并不用于提高EEEG信号的界面性能。在本研究中,我们调查了增强过程对MIEG信号分类性能的影响,而不是使用CSP等先前的变异性数据,我们证明,通过使MIEEG分类的高成功率,增强过程能够与CSP充分竞争。除了增强过程之外,我们还对DNN的结构进行了修改,增加了分类性能,在2005年NNEG的高级网络结构中,降低了NUR的升级值,在网络结构中的升级和升级结构中的升级的升级,在升级结构中的升级中提高了的升级,在网络中的升级和升级结构中的升级的升级,在网络中提高了数据的升级,在网络中的升级和升级结构中的升级的升级,在使用,在最后的升级和升级中提高了了数据的升级,在网络中的升级,在网络中的升级,在网络中的升级和升级和升级的升级的升级,在结构中,在结构中提高了。