In the context of sensory processing, visual discrimination is a fundamental function that enables survival. Previous findings suggest that such discrimination function can be decoded from electroencephalographic brain responses, especially by using oscillation feature. However, how to evaluate the fast visual discrimination is still unclear. In this study, we hypothesize that brain’s oscillatory activity in a passive viewing condition can serve as a sensitive predictor of fast visual discrimination. A visual multi-feature paradigm which allowing investigation of several different change types was used to record both event-related potentials (ERPs) and behavioral responses. First, we investigated separating the behavioral hit rate as a function of reaction time (categorized from 200 ms to 1000 ms with step of 100 ms). In the subsequent step, we extract the slow theta component from ERP’s time frequency represents with time frequency principal component analysis (TF-PCA) and correlate its average power with behavioral performance. Our results showed that the significant detect window for different deviants’ level was from 400 to 600 ms, while the hit rates in such detect window showed a significant correlation with the averaged time frequency power in the slow theta band during 100-300 ms latency for the color and shape deviants. These findings suggest that the oscillation power, particularly in the slow theta range, of the brain responses is a predictor of fast visual discrimination.

Objective: Scalp electroencephalogram (EEG) provides a substantial amount of data about information processing in the human brain. In the context of conventional event-related potential (ERP analysis), it is typically assumed that individual trials share similar properties and stem from comparable neural sources, especially when employing group-level methods (including cluster analysis). However, those group analyses can miss important information about the relevant neural process due to a rough estimation of the brain activities of individual subjects while selecting a fixed time window for all the subjects. Method: We designed a multi-set consensus clustering method to examine cognitive processes at the single-trial level. The obtained clusters for the trials were processed via consensus clustering at the individual subject level. The proposed method effectively identified the time window of interest for each individual subject. Results: The proposed method was applied to real EEG data from the active visual oddball task experiment to qualify the P3 component. Our early findings disclosed that the estimated time windows for individual subjects can provide more precise ERP identification than considering a fixed time window for all subjects. Moreover, based on standardized measurement error and established bootstrap for single-trial EEG, our assessments revealed suitable stability for the calculated scores for the identified P3 component. Significance: The new method provides a more realistic and information-driven understanding of the single trials’ contribution towards identifying the ERP of interest in individual ERP potential data.