Open AccessStudy of Classification of Human Emotion Features from EEG Signals at Minimum Free Energy Operated Effortlessly
Author(s) -
Hong Yu,
Sean-Paul Tomer,
Marcus Perla,
Yvett Yu
Publication year - 2021
Publication title -
european journal of engineering and technology research
Language(s) - English
Resource type - Journals
ISSN - 2736-576X
DOI - 10.24018/ejeng.2021.6.6.2604
Subject(s) - electroencephalography , happiness , computer science , discrete wavelet transform , energy (signal processing) , wavelet , artificial intelligence , brain–computer interface , loneliness , focus (optics) , action (physics) , speech recognition , wavelet transform , pattern recognition (psychology) , psychology , neuroscience , mathematics , social psychology , statistics , physics , psychiatry , optics , quantum mechanics
With the rapid development of various EEG-based features, PC machine robots can apply their fast massive parallel electronic computing AI capability to BCI systems. However, the machines do not have the human-equivalent chemical macromolecular hormone signals to understand human emotions. Machines cannot test people who are home alone with their loneliness, depression, drowsiness, happiness or remark the real hints through human being communication. But a minimum free energy of EEG discrete wavelet transform can indicate human emotion features and human being trend action. In this paper, we focus on EEG discrete wavelet transform at minimum free energy compared to operating effortlessly in visualization algorithm. In addition, we discuss EEG-based brain activities with the discrete wavelet energy spectrum for clinic treatment.