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

There are several protocols in the Electroencephalography (EEG) recording scenarios which produce various types of event-related potentials (ERP). P300 pattern is a well-known ERP which produced by auditory and visual oddball paradigm and BCI speller system. In this study, P300 and non-P300 separability are investigated in two scenarios including image recognition paradigm and BCI speller. Image recognition scenario is an experiment that examines the participants, knowledge about an image that shown to them before by analyzing the EEG signal recorded during the observing of that image as visual stimulation. To do this, three types of famous classifiers (SVM, Bayes LDA, and sparse logistic regression) were used to classify EEG recordings in six classes problem. Filtered and down-sampled (temporal samples) of EEG recording were considered as features in classification P300 pattern. Also, different sets of EEG recording including 4, 8 and 16 channels and different trial numbers were used to considering various situations in comparison. The accuracy was increased by increasing the number of trials and channels. The results prove that better accuracy is observed in the case of the image recognition scenario for 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 recognition paradigm is more separable than BCI (matrix speller).