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Mood based E-learning using EEG

The traditional E-learning system is limited with monitoring attention level of students. The online instructor cannot monitor whether the students remain focus during online autonomous learning. Along with the attention, emotions are also intrinsically related to the way that individuals interact with each other as well as machines. The behavior and emotions can be better understood by a human being to improve the communication but a machine cannot. This gives rise to lack of interest and knowledge gain. To overcome this limitation of the traditional E-learning system EEG sensors are been introduced. Using, EEG we can detect the emotion of the user, by actually looking inside the users brain to check user's mental state and signal waves are generated accordingly as the output which can be used to improve users learning experience. The proposed system is a smart E-learning system which predicts the video based on emotion. The system uses Neurosky Brainwave detector and Random forest Classification method to classify the waves to predict appropriate emotions.