Classification of EEG based mental fatigue using principal component analysis and Bayesian neural network
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.
2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)