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

翻译：我们介绍了为Cybathlon 2020竞赛的脑机接口（BCI）项目开发的脑机接口系统。在该项目中，四肢瘫痪患者需通过意念指令操控计算机游戏。我们从一秒长度的脑电图（EEG）信号中提取快速傅里叶变换幅度的绝对值作为特征（FFTabs）。为获取最终特征，我们引入了两种方法：特征均值法（计算特定频段FFTabs的平均值）与特征区间法（基于对无重叠的2赫兹宽频段生成多个特征均值）。所得特征被输入支持向量机分类器。该算法在PhysioNet数据库及我们包含2名四肢瘫痪患者16次离线实验的数据集上进行了测试。在总共59次游戏试验中，有27次成功达到240秒的资格赛时限。我们将典型频段（α、β、γ、θ）的特征均值与建议的range30及range40方法进行了对比。在PhysioNet数据集上，range40方法结合集成SVM分类器在四分类任务中取得了最高准确率（0.4607），显著优于当前最先进的EEGNet模型。