TY  - CONF
KW  - Brain; Electroencephalography; Electrophysiology; Neural networks
KW  -  Beta Oscillation; Brain signals; Cortical areas; Data collection; Delta Oscillation; Higher order thinking skills; Neural oscillations; Quantitative indicators
KW  -  Problem solving
SN  - 9781509008452
PB  - Institute of Electrical and Electronics Engineers Inc.
A1  - Roslan, N.S.
A1  - Amin, H.U.
A1  - Izhar, L.I.
A1  - Saad, M.N.M.
A1  - Sivapalan, S.
AV  - none
N1  - cited By 4; Conference of 6th International Conference on Intelligent and Advanced Systems, ICIAS 2016 ; Conference Date: 15 August 2016 Through 17 August 2016; Conference Code:125970
TI  - Role of EEG delta and beta oscillations during problem solving tasks
Y1  - 2017///
N2  - Problem solving is one of the higher-order thinking skills that have been studied by many researchers using Electroencephalography (EEG) brain signals. This paper concentrates on specific neural oscillations that can be observed in EEG signals which are delta (1.0-4.0 Hz) and beta (12.0-25.0 Hz) during problem solving task. Our aim is to investigate the role of the delta and beta neural oscillations during problem solving task at cortical area as compared to resting state (i.e. eyes open) using EEG. Eight volunteered healthy right-handed male students were recruited in this study. EEG 128-channel Hydro-Cel Geodesic (EGI Inc.) system was used in this study for data collection, but only 19 channels were used for data analysis. EEG recordings were taken during problem solving task (i.e. Raven's Advanced Progressive Matric (RAPM)) and during resting state (eyes open). Results showed that delta was significantly higher in almost all brain region and beta was significantly higher at prefrontal region only. Since we aim to investigate the role of delta and beta oscillation during problem solving, further investigation needs to be done with greater number of subjects in order to have more significant result to support this study. Further investigation could also help in finding quantitative indicators for intelligent assessment using brain signals. © 2016 IEEE.
ID  - scholars8943
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85011994163&doi=10.1109%2fICIAS.2016.7824138&partnerID=40&md5=c3ccfaa3ca42a7a695cd8a39b4664d56
ER  -