The purpose of this study is to distinguish the visual learners from visual non-learners while learning, having no background knowledge of the contents. The learners are distinguished analysing their brain patterns. EEG data were recorded during learning and memory tasks using 128 channels machine from a sample of thirty-four healthy university students in two sessions. The students were shown the animated learning content in video format for eight minutes. The brain waves were measured during leaning task. The study characterizes and distinguish between the visual learners and non-visual learners considering the extracted brain patterns. The power spectral entropy features are computed for the recorded EEG and is filtered into alpha and beta sub bands. The most suitable features are selected using principal component analysis (PCA). These features are than given as an input to the k-nearest neighbour (±bk-NN) classifier. Feature classification using k nearest neighbour has attained testing accuracy of 96 accuracy for alpha and 95 beta bands for first learning session and 97 and 94 for second learning session. The results show's that the alpha and beta power spectral entropy represent distinct and stable EEG signatures for visual learners and non-visual learners while performing the learning tasks. © 2018 IEEE.