DocumentCode :
589918
Title :
Brain signal detection methodology for attention training using minimal EEG channels
Author :
Yaomanee, K. ; Pan-ngum, Setha ; Ayuthaya, P.I.N.
Author_Institution :
Dept. of Comput. Eng., Chulalongkorn Univ., Bangkok, Thailand
fYear :
2012
fDate :
21-23 Nov. 2012
Firstpage :
84
Lastpage :
89
Abstract :
This paper presents the methodology to find the appropriate locations on the scalp for detecting EEG signal for attention training. The aim was to use low cost commercial EEG sensing device to select four positions which provide strong attention related signals. We aim to use the results of this work to develop a low cost attention training set. Three experiments were conducted to collect raw EEG data from 10 examiners. All 3 experiments made the examiners focus on the specific task to stimulate attention. First experiment is to read a light-content book, second is to figure out a hidden 3D image and the last one is to answer a general questionnaire on a PC. As the result, the suggested locations for detecting Alpha wave are AF3 and F7. The suggested locations for detecting Beta wave are FC6 and F8. It also shows that Alpha wave in the relaxation state higher than Alpha wave in the attention state and Beta wave in the attention state higher than Beta wave in the relaxation state.
Keywords :
brain-computer interfaces; electroencephalography; medical signal detection; Alpha wave detection; Beta wave detection; EEG signal detection; attention related signal; attention state; brain signal detection; hidden 3D image; light-content book; low cost attention training set; low cost commercial EEG sensing device; minimal EEG channels; relaxation state; scalp; Band pass filters; Electroencephalography; Head; Headphones; MATLAB; Vectors; Alpha; BCI2000; Beta; EEG; Emoiv Epoc Headset; Matlab; Simulink;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
ICT and Knowledge Engineering (ICT & Knowledge Engineering), 2012 10th International Conference on
Conference_Location :
Bangkok
ISSN :
2157-0981
Print_ISBN :
978-1-4673-2316-1
Type :
conf
DOI :
10.1109/ICTKE.2012.6408576
Filename :
6408576
Link To Document :
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