Evaluation of students’ mental performance level based on EEG signal analysis

The article presents the results of studies on using non-invasive brain-computer interfaces (BCI) for analyzing the degree of mental fatigue of students. It is proposed to use electroencephalographic (EEG) signals, allowing to determine the potentials caused by events. A set of algorithms for preprocessing EEG signals and recognizing the evoked potential of P300 arising 300 ms after a visual stimulus is described in detail. The main focus is on the P300 wave recognition experiment from information captured by a Muse headset. Preliminary results on the accuracy of P300 wave recognition in different people using various types of classifiers are given. A methodology has been developed for using P300 to assess the students’ mental fatigue. A number of experiments have been carried out confirming the possibility of such assessment using the developed methodology.