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There are several protocols in the Electroencephalography (EEG) recordingscenarios which produce various types of event-related potentials (ERP). P300pattern is a well-known ERP which produced by auditory and visual oddballparadigm and BCI speller system. In this study, P300 and non-P300 separabilityare investigated in two scenarios including image recognition paradigm and BCIspeller. Image recognition scenario is an experiment that examines theparticipants, knowledge about an image that shown to them before by analyzingthe EEG signal recorded during the observing of that image as visualstimulation. To do this, three types of famous classifiers (SVM, Bayes LDA, andsparse logistic regression) were used to classify EEG recordings in six classesproblem. Filtered and down-sampled (temporal samples) of EEG recording wereconsidered as features in classification P300 pattern. Also, different sets ofEEG recording including 4, 8 and 16 channels and different trial numbers wereused to considering various situations in comparison. The accuracy wasincreased by increasing the number of trials and channels. The results provethat better accuracy is observed in the case of the image recognition scenariofor the different sets of channels and by using the different number of trials.So it can be concluded that P300 pattern which produced in image recognitionparadigm is more separable than BCI (matrix speller).

Comparison of the P300 detection accuracy related to the BCI speller and image recognition scenarios