چكيده به لاتين
In recent decades, the use of electroencephalogram (EEG) signals for making communication with the surrounding environment have led to the emergence of brain-computer interfaces (BCIs). P300 signal is a type of brain potential used in BCI systems. This potential is often evoked by visual stimulus using oddball paradigm. In the present study, first, a graphical user interface (GUI) containing seven keys with the proposed visual stimulus paradigm (GC3) was designed to evoke the P300 potential, and then, it was compared with the other three stimulus paradigms (GC2, GCI, GW). The results of offline experiments reveal a significant improvement (p-value < 0/05) in the system performance by GC3 paradigm with the achieved average AUC of 0/9274, in comparison with the other three paradigms. For single-trial classification of target and non-target, six linear methods were compared with each other in an offline manner, and finally, STDA classifier with average AUC of 0/9489 was selected for further experiments. Also, in this study, a brain switch was designed for the system to help user switch between controlled and non-controlled states whenever he/she wants to. For data classification in an asynchronous mode of operation, nine threshold-free algorithms were evaluated offline in three classification styles, i.e, single-trial, double-trial, and triple-trial. Finally, algorithm 4 was chosen to do online experiments by averaging tree trials (three rounds) data, which offered the best performance. Algorithm 4, which is a combination of STDA method and statistical F test, offered 0/0157 FPR and 0.9277 TPR. In the online experiments, P300 signal has been used with the aim of controlling a robotic arm, taking a material from the source point, transferring and leaving it in the target point. Online average double-trial classification accuracy in synchronous mode of operation was 98.56. Also, the average amount of FPR, TPR, and ITR in online experiments were equal to 0.11, 14.97 event/min and 15.36 bit/min, respectively.
Keywords: Brain-computer interface (BCI), electroencephalogram (EEG), P300, stimulus paradigm, spatial-temporal discriminant analysis (STDA), asynchronous, idle state, accuracy, AUC, TPR, FPR.
