Abstract:
In this article, a comparative study was implemented to determine how physical or mental factors affect the performance of a BCI system based on SSVEP. For this, a public...Show MoreMetadata
Abstract:
In this article, a comparative study was implemented to determine how physical or mental factors affect the performance of a BCI system based on SSVEP. For this, a public EEG dataset was used that consists of four flickering rectangles on a led screen (5.45, 6.67, 8.57, and 12 Hz). During the experimental design, subjects answered a questionnaire to determine their physical and mental conditions in the acquisition of EEG signals. The factors of comfort (high and low), concentration (high and low), and eye fatigue (high and low) were evaluated by selecting 2 groups each one with 8 different subjects to each factor evaluated. In order to identify target frequency components using EEG signals, it was implemented the Filter Bank Canonical Correlation Analysis (FBCCA) algorithm by using five occipital lobe EEG channels. The performance was quantified by means of accuracy (Acc) and Information Transfer Rate (ITR) metrics. Furthermore, a statistical analysis was performed to determine whether better results are presented when a subject presents high comfort, high concentration, or low eye fatigue. According to obtained results, it was found changes for Acc and ITR according to the factor of comfort, concentration, and eye fatigue of the subjects. However, statistically, there are no significant differences (p > 0.05) in the physical or mental factors analyzed for 4 different time windows implemented.
Published in: 2021 IEEE 2nd International Congress of Biomedical Engineering and Bioengineering (CI-IB&BI)
Date of Conference: 13-15 October 2021
Date Added to IEEE Xplore: 01 December 2021
ISBN Information: