  Electroencephalogram (EEG) has become increasingly popular in driver fatigue
monitoring systems. Several decomposition methods have been attempted to
analyze the EEG signals that are complex, nonlinear and non-stationary and
improve the EEG decoding performance in different applications. However, it
remains challenging to extract more distinguishable features from different
decomposed components for driver fatigue recognition. In this work, we propose
a novel decomposition-based hybrid ensemble convolutional neural network (CNN)
framework to enhance the capability of decoding EEG signals. Four decomposition
methods are employed to disassemble the EEG signals into components of
different complexity. Instead of handcraft features, the CNNs in this framework
directly learn from the decomposed components. In addition, a
component-specific batch normalization layer is employed to reduce subject
variability. Moreover, we employ two ensemble modes to integrate the outputs of
all CNNs, comprehensively exploiting the diverse information of the decomposed
components. Against the challenging cross-subject driver fatigue recognition
task, the models under the framework all showed superior performance to the
strong baselines. Specifically, the performance of different decomposition
methods and ensemble modes was further compared. The results indicated that
discrete wavelet transform-based ensemble CNN achieved the highest average
classification accuracy of 83.48% among the compared methods. The proposed
framework can be extended to any CNN architecture and be applied to any
EEG-related tasks, opening the possibility of extracting more beneficial
features from complex EEG data.
