Abstract:

The paper presents results of the student research project dedicated to applying Brain-Computer Interface in concentration study. The applied Brain-Computer Interface is based on electroencephalographic P300 paradigm. It was constructed in order to check students ability to concentrate on a specific task. What is more, the research procedure was implement to adjust the set of parameters applied in the scenario.

Tests were performed in Laboratory of Motion Analysis and Interface Ergonomics is located at Lublin University of Technology in Poland. Construction of the interface was performed using 21 channel EEG amplifier - Mitsar EEG 201. OpenVibe environment was used to design the experiment.

P300 paradigm is based on Event-related potential phenomenon. It is a potential correlated with the event detected during multiple presence of oddball stimuli. Obtained signal must be averaged to reduce the noise and to enhance the evoked potential component. P300 signal is a positive potential detected at time between 300 and 600 ms after the expected stimulus. The important aspect of P300 calculation is the need of multiple repetitions of displayed event. P300 potential might be detected only after signal averaging.

The BCI was dedicated to focus on one of twelve cards to selected it only with brain waves, without using muscles. The BCI is composed of three parts: acquisition scenario, offline classifier training scenario and online scenario. In the classification process Linear Discriminant Analysis classifier was applied. Features were extracted based on eight channel electroencephalographic signal subjected to a filtration and epoching process. Each tested person was calibrated and analyzed individually. It is necessary because of high variability in individuals.

The paper presents the Brain-Computer Interface construction and the process of data gathering and analyzing. Several users were tested and results obtained for different set of settings were compared.

Keywords:

Student IT projects, electroencephalography, EEG, P300 paradigm, Brain-Computer Interface.