Detecting how time is subjectively perceived based on event-related potentials (ERPs): a machine learning approach.

Time perception is essential for the precise performance of many of our activities and the coordination between different modalities. But it is distorted in many diseases and disorders. Event-related potentials (ERP) have long been used to understand better how the human brain perceives time, but machine learning methods have rarely been used to detect a person's time perception from his/her ERPs. In this study, EEG signals of the individuals were recorded while performing an auditory oddball time discrimination task. After features were extracted from ERPs, data balancing, and feature selection, machine learning models were used to distinguish between the oddball durations of 400 ms and 600 ms from standard durations of 500 ms. ERP results showed that the P3 evoked by the 600 ms oddball stimuli appeared about 200 ms later than that of the 400 ms oddball tones. Classification performance results indicated that support vector machine (SVM) outperformed K-nearest neighbors (KNN), Random Forest, and Logistic regression models. The accuracy of SVM was 91.24, 92.96, and 89.9 for the three used labeling modes, respectively. Another important finding was that most features selected for classification were in the P3 component range, supporting the observed significant effect of duration on the P3. Although all N1, P2, N2, and P3 components contributed to detecting the desired durations. Therefore, results of this study suggest the P3 component as a potential candidate to detect sub-second periods in future researches on brain-computer interface (BCI) applications.