Tushar Hande, Shruti Warade, Tejashri Shingade

The leadinggoal of this paper is to discern emotion from Electroencephalogram EEG signals of human brain activity. Emotion categorization from EEG data has lately received a lot of attention, thanks to the growing development of various machine learning algorithms and numerous onto this of the brain to a computer interface for normalpeople. Researchers have only a rudimentary understanding of the bonding between various emotional states and mainly EEG parameters until now. Throughout the process, we systematically perform feature extraction. The retrieved primary features are stated,and the classification result validates their effectiveness. This study systematically uses EEG characteristics for emotion classification and provides an efficient feature classification approach for optimal results. This paper uses the support vector machine (SVM) and k-nearest neighbor (KNN) classifier for persons’ emotion recognition. Also, results are validated with performance parameters i.e., accuracy.

http://journaledcs.com/issues2_2022/paper5.html