Ourobjective is to find an efficient method to classify the subject's mental cognitive workload as good or bad by obtaining features that can describe the continuous and underlying temporal dynamics of electroencephalography (EEG) data during the performance of mental tasks. To develop a BCI model that can forecast mental states like good and bad, we explore the ensemble learning technique using classifiers like the random forest classifier. From the alpha, beta, and gamma bands of EEG, the features like mean, roo…