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Exploration of Prominent Frequency Wave in EEG Signals from Brain Sensors Network
Author(s) -
Raja Majid Mehmood,
Hyo Jong Lee
Publication year - 2015
Publication title -
international journal of distributed sensor networks
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.324
H-Index - 53
eISSN - 1550-1477
pISSN - 1550-1329
DOI - 10.1155/2015/386057
Subject(s) - electroencephalography , computer science , frequency domain , signal (programming language) , artifact (error) , frequency band , energy (signal processing) , brain waves , time–frequency analysis , radio spectrum , time domain , artificial intelligence , pattern recognition (psychology) , acoustics , speech recognition , physics , telecommunications , statistics , psychology , computer vision , mathematics , neuroscience , filter (signal processing) , bandwidth (computing) , programming language
We investigated the signals regularity of electroencephalography (EEG) channels separately and determined the energy of selected frequency waves, such as, δ, θ, α, β, and γ. The goal of this research is to identify the prominent frequency band from selected frequencies. We recorded the EEG signal data of 30 controlled subjects with 18 EEG channels. These subjects are all males with an average age of 24 years. Emotional stimuli related to different emotions were presented to each of selected candidates. EEG data were extracted and further processed for artifact removal, filtering, epoch selection and averaging of the signals. We designed and tested our method for exploring the frequency waves of all EEG channels. We also employed the Hjorth parameters to measure the signal regularity in time and frequency domain. The detailed physiological response of human subjects is also presented in this paper. Our results showed that the energy level of delta wave is mostly high in all cases.

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