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Electroencephalogram (EEG) activity in Male human during pleasant and unpleasant emotions by different events was investigated. When the subjects were listening to pleasant music and noise, the EEG data comparing pleasant and unpleasant states were obtained. Related emotional theta band was studied by using Principal Component Analysis of EEG data, meanwhile extracting the first principal component,...
This paper introduces a method for discussing the association between human personality based on egogram scores and the results of classifying the electroencephalogram (EEG) patterns while listening to the music. The egogram based on psychological testing is used for analyzing his/her personality. The frequencies of the EEG analyzed are the components that contain significant and immaterial information...
This study explores the electroencephalographic (EEG) correlates of emotions during music listening. Principal component analysis (PCA) is used to correlate EEG features with complex music appreciation. This study also applies machine-learning algorithms to demonstrate the feasibility of classifying EEG dynamics in four subjectively-reported emotional states. The high classification accuracy (81.58plusmn3...
This paper introduces a method for classifying humans by analyzing prefrontal cortex electroencephalogram (EEG) activity to extract and confirm distinct response features on listening to music that the user feels matches his/her mood, does not match his/her mood, or otherwise. The proposed method constitutes analyzing EEG signals obtained from monitoring human response features and classifying the...
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