Pairwise and variance based signal compression algorithm (PVBSC) in the P300 based speller systems using EEG signals

摘要

Background and objective: Brain-Computer Interfaces (BCI) are used to provide environmental interaction among individuals, especially people with disabilities. Spelling systems, one of the BCI applications, are based on the principle of detecting P300 waves from EEG signals. The aim of speller systems is to identify the P300 waves and determine the letter on a screen opposite the person. The purpose of the operating speller systems is to minimize the processing cost of the system with smaller data sizes to be obtained by compressing EEG data before the pre-processing step. In this study, a hybrid model was presented. With Pairwise and variance-based signal compression Algorithm, first of all, data is compressed and then preprocessing, and classification is performed. The proposed hybrid model is intended to be stored in offline systems and to increase the speed of operation in online systems.