In current days, human emotional state recognition through Electroencephalogram (EEG) is considered as the up-and-coming topics which grip the concentration of researchers. For numerous real-time applications, generally, this EEG-based recognition is an effective technique, particularly for disabled persons. Regarding precise emotion recognition, numerous researchers are in advancement to create the recognition technique effectively. Nevertheless, it is not fulfilled in the accurate development, therefore, this work tries in the human emotion recognition stated or it affects via EEG signal by exploiting the classifier techniques as well as developed features. Initially, this work uses the Wavelet Transformation as well as 2501 (EMCD) in the recognition process to indicate the EEG signal in minimum dimension and expressive. The redundancy of EEG is removed using the EMCD, as well as the important information can be extracted. By exploiting the extracted features, the classification procedures are performed with the help of a classifier called Convolutional Neural Network (CNN). The developed method performance is evaluated regarding the metrics such as positive and negative measures and the results also exhibit the dominance of the developed model in emotion recognition in an accurate manner.
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