The arrangement and functional distribution of an EEG signal structure related to higher cortical functions are being analyzed by both recent and substantial hypothesis experiments. This article provides a technique for analyzing the distribution pattern of EEG signals with cognitive functions utilizing the variational pattern recognition based on brain computation. The nonparametric rules of decision reveal vital EEG patterns that distinguish between several tasks. The general truth of findings is measured by the cross-validation recognition rate. This method is employed to derive signals from a group of adults performing several complex works. These cognitive signals that distinguish between assignments are keeping with visual EEG understandings and spectral intensity analysis and enhance the results. Since tasks recognized, it is clear that EEG signals can distinguish that complex behavior or sensory-motivating and performance-related factors that are related to the cognitive components of the work. The obtained results achieve high accuracy and sensitivity rate with less error rate.