EEG Signature of Object Categorization from Event-related Potentials

Human visual system recognizes objects in a fast manner and the neural activity of the human brain generates signals which provide information about objects categories seen by the subjects. The brain signals can be recorded using different systems like the electroencephalogram (EEG). The EEG signals carry significant information about the stimuli that stimulate the brain. In order to translate information derived from the EEG for the object recognition mechanism, in this study, twelve various categories were selected as visual stimuli and were presented to the subjects in a controlled task and the signals were recorded through 19-channel EEG recording system. Analysis of signals was performed using two different event-related potential (ERP) computations namely the “target/rest” and “target/non-target” tasks. Comparing ERP of target with rest time indicated that the most involved electrodes in our task were F3, F4, C3, C4, Fz, Cz, among others. ERP of “target/non-target” resulted that in target stimuli two positive peaks occurred about 400 ms and 520 ms after stimulus onset; however, in non-target stimuli only one positive peak appeared about 400 ms after stimulus onset. Moreover, reaction times of subjects were computed and the results showed that the category of flower had the lowest reaction time; however, the stationery category had the maximum reaction time among others. The results provide useful information about the channels and the part of the signals that are affected by different object categories in terms of ERP brain signals. This study can be considered as the first step in the context of human-computer interface applications.