Novel Methods for Stress Features Identification using EEG Signals
This paper introduces new methods to extract stress features from electroencephalogram (EEG) signals during two cognitive states; Closed-Eyes (CE) and Open-Eyes (OE) using Relative Energy Ratio (RER), Shannon Entropy (SE) and Spectral Centroids (SC). The group with the stress features was identified...
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United Kingdom Simulation Society
2011
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| Online Access: | http://umpir.ump.edu.my/id/eprint/16488/ http://umpir.ump.edu.my/id/eprint/16488/ http://umpir.ump.edu.my/id/eprint/16488/ http://umpir.ump.edu.my/id/eprint/16488/1/2011_IJSSST_Vol_12_No_1_Norizam.pdf |
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ump-164882018-04-11T03:21:38Z http://umpir.ump.edu.my/id/eprint/16488/ Novel Methods for Stress Features Identification using EEG Signals Norizam, Sulaiman Mohd Nasir, Taib Sahrim, Lias Zunairah, Murat Siti Armiza, Mohd Aris Noor Hayatee, Abdul Hamid TK Electrical engineering. Electronics Nuclear engineering This paper introduces new methods to extract stress features from electroencephalogram (EEG) signals during two cognitive states; Closed-Eyes (CE) and Open-Eyes (OE) using Relative Energy Ratio (RER), Shannon Entropy (SE) and Spectral Centroids (SC). The group with the stress features was identified and classified using k-Nearest Neighbor (k-NN). The RER in term of Energy Spectral Density (ESD) for each frequency band (delta, theta, alpha and beta) in four different groups consisted of 180 EEG data were calculated and analyzed. Then, the SE was used to confirm the pattern of stress features. Meanwhile, SC was applied to the RER of each group and then the results were selected as input features to k-Nearest Neighbor (k-NN) for the classification purposes. The training and testing of the classifier were evaluated at 50:50 ratios and 70:30 ratios. The proposed method showed promising results where the combination of RER, SE and SC techniques with the training and testing of k-NN set at 70:30 able to detect and classify the group with the unique stress features at 88.89% accuracy United Kingdom Simulation Society 2011-02 Article PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/16488/1/2011_IJSSST_Vol_12_No_1_Norizam.pdf Norizam, Sulaiman and Mohd Nasir, Taib and Sahrim, Lias and Zunairah, Murat and Siti Armiza, Mohd Aris and Noor Hayatee, Abdul Hamid (2011) Novel Methods for Stress Features Identification using EEG Signals. International Journal of Simulation: Systems, Science & Technology (IJSSST), 12 (1). pp. 27-33. ISSN 1473-8031 (print); 1473-804x (online) http://ijssst.info/Vol-12/No-1/paper4.pdf DOI: 10.5013/IJSSST.a.12.01.04 |
| repository_type |
Digital Repository |
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Local University |
| institution |
Universiti Malaysia Pahang |
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UMP Institutional Repository |
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Online Access |
| language |
English |
| topic |
TK Electrical engineering. Electronics Nuclear engineering |
| spellingShingle |
TK Electrical engineering. Electronics Nuclear engineering Norizam, Sulaiman Mohd Nasir, Taib Sahrim, Lias Zunairah, Murat Siti Armiza, Mohd Aris Noor Hayatee, Abdul Hamid Novel Methods for Stress Features Identification using EEG Signals |
| description |
This paper introduces new methods to extract stress features from electroencephalogram (EEG) signals during two cognitive states; Closed-Eyes (CE) and Open-Eyes (OE) using Relative Energy Ratio (RER), Shannon Entropy (SE) and Spectral Centroids (SC). The group with the stress features was identified and classified using k-Nearest Neighbor (k-NN). The RER in term of Energy Spectral Density (ESD) for each frequency band (delta, theta, alpha and beta) in four different groups consisted of 180 EEG data were calculated and analyzed. Then, the SE was used to confirm the pattern of stress features. Meanwhile, SC was applied to the RER of each group and then the results were selected as input features to k-Nearest Neighbor (k-NN) for the classification purposes. The training and testing of the classifier were evaluated at 50:50 ratios and 70:30 ratios. The proposed method showed promising results where the combination of RER, SE and SC techniques with the training and testing of k-NN set at 70:30 able to detect and classify the group with the unique stress features at 88.89% accuracy |
| format |
Article |
| author |
Norizam, Sulaiman Mohd Nasir, Taib Sahrim, Lias Zunairah, Murat Siti Armiza, Mohd Aris Noor Hayatee, Abdul Hamid |
| author_facet |
Norizam, Sulaiman Mohd Nasir, Taib Sahrim, Lias Zunairah, Murat Siti Armiza, Mohd Aris Noor Hayatee, Abdul Hamid |
| author_sort |
Norizam, Sulaiman |
| title |
Novel Methods for Stress Features Identification using EEG Signals |
| title_short |
Novel Methods for Stress Features Identification using EEG Signals |
| title_full |
Novel Methods for Stress Features Identification using EEG Signals |
| title_fullStr |
Novel Methods for Stress Features Identification using EEG Signals |
| title_full_unstemmed |
Novel Methods for Stress Features Identification using EEG Signals |
| title_sort |
novel methods for stress features identification using eeg signals |
| publisher |
United Kingdom Simulation Society |
| publishDate |
2011 |
| url |
http://umpir.ump.edu.my/id/eprint/16488/ http://umpir.ump.edu.my/id/eprint/16488/ http://umpir.ump.edu.my/id/eprint/16488/ http://umpir.ump.edu.my/id/eprint/16488/1/2011_IJSSST_Vol_12_No_1_Norizam.pdf |
| first_indexed |
2023-09-18T22:22:13Z |
| last_indexed |
2023-09-18T22:22:13Z |
| _version_ |
1777415732121829376 |