Human Emotion Perception Based on K-Nearest Neighbors Classifier
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Abstract
Emotions are the psychological stages of feeling that can be intertwined through circumstances, temperament, relationships, motivation, dispositions, etc.
This paper investigates the effect for the emotion-discriminating precision of Different wave levels of EEG signals and a particular number of channels.
Using various sets of EEG channels, the proposal classified affective states in the equivalence and excitability dimensions. To begin, DEAP normalized the pretreated hypothetical data. Following that, discrete wavelet transduction was used to divide the EEG into four bands, The scales used were the features of the K-nearest neighbor Algorithm entropy and energy algorithm.
The Classifier accuracy for channels (10-14-18 , 32 )was according to the gamma frequency in the valence dimension being 99.5313%, 99.6094%, 99.7656%, 99.6875%, 99.4531%, and in the arousal dimension 99.5313%, 99.7656% and 99.7656%. The gamma frequency grading accuracy is greater than the beta frequency of the alpha and theta frequency, and the accuracy increases the number for channels.
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