A Decomposition-Based Hybrid Ensemble CNN Framework for Driver Fatigue Recognition

TL;DR

This paper introduces a novel hybrid ensemble CNN framework utilizing multiple EEG decomposition methods to improve driver fatigue recognition, achieving higher accuracy by directly learning from decomposed signals and reducing subject variability.

Contribution

The work proposes a new decomposition-based hybrid ensemble CNN framework with component-specific normalization and multiple ensemble modes for enhanced EEG feature extraction in driver fatigue detection.

Findings

Discrete wavelet transform-based ensemble CNN achieved 83.48% accuracy.

The framework outperformed strong baseline models.

It is adaptable to various EEG-related tasks.

Abstract

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.