论文标题
Cybathlon的闭环BCI系统2020
Closed loop BCI System for Cybathlon 2020
论文作者
论文摘要
我们介绍为Cybathlon 2020竞赛的BCI学科开发的大脑计算机界面（BCI）系统。在BCI纪律中，需要四肢瘫痪的受试者才能控制具有心理命令的计算机游戏。快速转化幅度的绝对性是根据一秒长的脑电图（EEG）信号计算出的特征（FFTAB）。为了提取最终功能，我们介绍了两种方法，即特征平均值，其中计算了特定频带的FFTABS的平均值，而该特征范围基于为非重叠的2 Hz宽频率箱生成多个特征平均值。最终的功能被馈送到支持向量机分类器中。在Physionet数据库和我们的数据集上测试了该算法，其中包含16个带有2个四边形受试者的离线实验。 27个游戏试验（在59个中），我们的四脑受试者达到了240秒的资格时间限制。将规范频带（Alpha，Beta，Gamma和Theta）的特征平均值与我们建议的Range30和Range40方法进行了比较。在Physionet数据集上，与集合SVM分类器相结合的Range40方法显着达到了最高的精度水平（0.4607），具有4级分类，并且表现优于先进的EEGNET。
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.
