TY - GEN
T1 - Classification of EEG based-mental fatigue using principal component analysis and Bayesian neural network
AU - Chai, Rifai
AU - Tran, Yvonne
AU - Naik, Ganesh R.
AU - Nguyen, Tuan N.
AU - Ling, Sai Ho
AU - Craig, Ashley
AU - Nguyen, Hung T.
PY - 2016
Y1 - 2016
N2 - ![CDATA[This paper presents an electroencephalography (EEG) based-classification of between pre- and post-mental load tasks for mental fatigue detection from 65 healthy participants. During the data collection, eye closed and eye open tasks were collected before and after conducting the mental load tasks. For the computational intelligence, the system uses the combination of principal component analysis (PCA) as the dimension reduction method of the original 26 channels of EEG data, power spectral density (PSD) as feature extractor and Bayesian neural network (BNN) as classifier. After applying the PCA, the dimension of the data is reduced from 26 EEG channels in 6 principal components (PCs) with above 90% of information retained. Based on this reduced dimension of 6 PCs of data, during eyes open, the classification pre-task (alert) vs. post-task (fatigue) using Bayesian neural network resulted in sensitivity of 76.8 %, specificity of 75.1% and accuracy of 76% Also based on data from the 6 PCs, during eye closed, the classification between pre- and post-task resulted in a sensitivity of 76.1%, specificity of 74.5% and accuracy of 75.3%. Further, the classification results of using only 6 PCs data are comparable to the result using the original 26 EEG channels. This finding will help in reducing the computational complexity of data analysis based on 26 channels of EEG for mental fatigue detection.]]
AB - ![CDATA[This paper presents an electroencephalography (EEG) based-classification of between pre- and post-mental load tasks for mental fatigue detection from 65 healthy participants. During the data collection, eye closed and eye open tasks were collected before and after conducting the mental load tasks. For the computational intelligence, the system uses the combination of principal component analysis (PCA) as the dimension reduction method of the original 26 channels of EEG data, power spectral density (PSD) as feature extractor and Bayesian neural network (BNN) as classifier. After applying the PCA, the dimension of the data is reduced from 26 EEG channels in 6 principal components (PCs) with above 90% of information retained. Based on this reduced dimension of 6 PCs of data, during eyes open, the classification pre-task (alert) vs. post-task (fatigue) using Bayesian neural network resulted in sensitivity of 76.8 %, specificity of 75.1% and accuracy of 76% Also based on data from the 6 PCs, during eye closed, the classification between pre- and post-task resulted in a sensitivity of 76.1%, specificity of 74.5% and accuracy of 75.3%. Further, the classification results of using only 6 PCs data are comparable to the result using the original 26 EEG channels. This finding will help in reducing the computational complexity of data analysis based on 26 channels of EEG for mental fatigue detection.]]
KW - Bayesian statistical decision theory
KW - electroencephalography
KW - mental fatigue
KW - principal components analysis
KW - spectral energy distribution
UR - http://handle.westernsydney.edu.au:8081/1959.7/uws:41585
U2 - 10.1109/EMBC.2016.7591765
DO - 10.1109/EMBC.2016.7591765
M3 - Conference Paper
SN - 9781457702204
SP - 4654
EP - 4657
BT - Proceedings of the 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC '16), 16-20 August 2016, Orlando, Florida, USA
PB - IEEE
T2 - IEEE Engineering in Medicine and Biology Society. Annual International Conference
Y2 - 11 July 2022
ER -