Human Emotion Detection Based on Machine Learning
Emotion is a mental and physiological state associated with a wide variety of feelings, thoughts and behaviors. Emotions are fundamental in the daily life of human beings as they play an important role in human cognition, namely in rational decision-making, perception, human interaction, and human intelligence. This paper investigates the effect for the emotion-discriminating precision of Different wave levels of EEG signals and a particular number of channels. It 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 highest classification accuracy was using the K-NN algorithm for channels (4-10-14-18, 32) in the four dimensions (valence, arousal, dominance, and liking). They are channel 18 (99.7656%, 99.7656%, 99.7656%, 99.7656%) respectively. While the highest classification accuracy for the frequency bands is the gamma frequency greater from beta and alpha and theta frequency for the four dimensions is (99.7656%)
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