TY - GEN
T1 - Channels selection using independent component analysis and scalp map projection for EEG-based driver fatigue classification
AU - Chai, R.
AU - Naik, G. R.
AU - Ling, S. H.
AU - Tran, Y.
AU - Craig, A.
AU - Nguyen, H. T.
PY - 2017
Y1 - 2017
N2 - ![CDATA[This paper presents a classification of driver fatigue with electroencephalography (EEG) channels selection analysis. The system employs independent component analysis (ICA) with scalp map back projection to select the dominant of EEG channels. After channel selection, the features of the selected EEG channels were extracted based on power spectral density (PSD), and then classified using a Bayesian neural network. The results of the ICA decomposition with the back-projected scalp map and a threshold showed that the EEG channels can be reduced from 32 channels into 16 dominants channels involved in fatigue assessment as chosen channels, which included AF3, F3, FC1, FC5, T7, CP5, P3, O1, P4, P8, CP6, T8, FC2, F8, AF4, FP2. The result of fatigue vs. alert classification of the selected 16 channels yielded a sensitivity of 76.8%, specificity of 74.3% and an accuracy of 75.5%. Also, the classification results of the selected 16 channels are comparable to those using the original 32 channels. So, the selected 16 channels is preferable for ergonomics improvement of EEG-based fatigue classification system.]]
AB - ![CDATA[This paper presents a classification of driver fatigue with electroencephalography (EEG) channels selection analysis. The system employs independent component analysis (ICA) with scalp map back projection to select the dominant of EEG channels. After channel selection, the features of the selected EEG channels were extracted based on power spectral density (PSD), and then classified using a Bayesian neural network. The results of the ICA decomposition with the back-projected scalp map and a threshold showed that the EEG channels can be reduced from 32 channels into 16 dominants channels involved in fatigue assessment as chosen channels, which included AF3, F3, FC1, FC5, T7, CP5, P3, O1, P4, P8, CP6, T8, FC2, F8, AF4, FP2. The result of fatigue vs. alert classification of the selected 16 channels yielded a sensitivity of 76.8%, specificity of 74.3% and an accuracy of 75.5%. Also, the classification results of the selected 16 channels are comparable to those using the original 32 channels. So, the selected 16 channels is preferable for ergonomics improvement of EEG-based fatigue classification system.]]
UR - https://hdl.handle.net/1959.7/uws:66976
U2 - 10.1109/EMBC.2017.8037196
DO - 10.1109/EMBC.2017.8037196
M3 - Conference Paper
SN - 9781509028092
SP - 1808
EP - 1811
BT - Proceedings EMBC 2017: 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Smarter Technology for a Healthier World, July 11-15 2017, International Convention Centre, Jeju Island, South Korea
PB - IEEE
T2 - IEEE Engineering in Medicine and Biology Society. Annual International Conference
Y2 - 11 July 2022
ER -
