We present our Brain-Computer Interface (BCI) system, developed for the BCI discipline of Cybathlon 2020 competition. In the BCI discipline, tetraplegic subjects are required to control a computer game with mental commands. The absolute of the Fast-Fourier Transformation amplitude was calculated as a Source Feature (SF) from one-second-long electroencephalographic (EEG) signals. To extract the final features, we introduced two methods, namely the SF Average where the average of the SF for a specific frequency band was calculated, and the SF Range which was based on generating multiple SF Average features for non-overlapping 2 Hz wide frequency bins. The resulting features were fed to a Support Vector Machine classifier. The algorithms were tested both on the PhysioNet database and on our dataset, which contains 16 offline experiments, recorded with 2 tetraplegic subjects. 27 real-time experiments (out of 59) with our tetraplegic subjects, reached the 240-second qualification time limit. The SF Average of canonical frequency bands (alpha, beta, gamma, theta) were compared with our suggested range30 and range40 method. On the PhysioNet dataset, the range40 method significantly reached the highest accuracy level (0.4607), with 4 class classification, and outperformed the state-of-the-art EEGNet.

翻译：我们展示了我们为Cybathlon 2020 竞争BCI学科开发的大脑-计算机界面(BCI)系统。在 BCI 学科中,四肢瘫痪对象必须控制带有精神指令的计算机游戏。快速四肢变形振幅的绝对值是从一秒长电子脑图信号中得出的源特征(SF)。为了提取最后特征,我们采用了两种方法,即SF平均数,其中计算了一个特定频率波段的SF平均数,以及SF范围,它基于为不超拍2赫兹宽频箱生成多SF平均值功能。由此产生的特性被反馈给一个支持性矢量机器分类器。算法的绝对值是在PhysioNet数据库和我们的数据集中进行测试的,该数据集包含16个离线实验,记录了两个四肢特征。27个实时实验(在59个中),涉及我们一个特定频带的SFS平均数达到了240秒的资格时限。SF平均频率波段(al-40)超过2 Hz宽的2个Hybles bass bass bass(al-al-al-EG-EGIS30 范围),与建议的最高级的Phyal-real-roadal-rodal 4 范围数据将达。与最高级的频段距为40和最远达。建议的方法与最高级的频率为40-eg-eg-eg-eg-eg-eg-egs。与最近达。与最高的频率达。建议的数据系距为40)。