Closed loop BCI System for Cybathlon 2020

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.