In recent year, Authors had been attempting to find or detect the feeling of human by recorded brain signal for example, EEG (electroencephalogram) alerts. Because of the unnecessary degrees of unwanted signal from EEG recording, a solitary feature alone can't accomplish great execution. Distinct feature is key for automatic feeling identification. Right now, we present an AI based scheme utilizing various features extricated from EEG recordings. The plan joins these particular highlights in feature space utilizing both managed and unaided component choice procedures. To re-request the joined highlights to max-importance with the names and min-repetition of each feature by applying Maximum Relevance Minimum Redundancy (MRMR). The produced highlights are additionally diminished with principal component analysis(PCA) for removing essential segments. Test report will be generated to show that the proposed work should outperform the condition of-workmanship techniques utilizing similar settings in real time dataset.

For detecting the feelings or emotions of the human being by using brain signals and its classification has been attempt by many researchers. The EEG headset is used for collecting the brain signal of the subject. Because of lots of noise in the input signal taken by EEG headset, various features need to be used as a single feature that cannot give accurate output. The Number of feature used is the key for identifying the emotion of a person automatically. So, we identify various features using an AI based scheme from EEG recorded signals. This various features are saved in the database. Features include mean, maximum, minimum, std. deviation, variance, corr. Coefficient, cov. Coefficient, Median, Kurtosis, Energy, Zero crossing rate. By using Maximum Relevance Minimum Redundancy (MRMR), as per the name we arrange the features to minimum-relevance and maximum-importance of every feature. For removing essential segments PCA is used to diminish the produce feature. The proposed system will outperform and improve the accuracy of emotion detection by using brain wave and Adaptive PSO.