An Auto-Weighting Incremental Random Vector Functional Link Network for EEG-Based Driving Fatigue Detection

Recently, electroencephalogram (EEG) has been receiving increasing attention in driving fatigue attention because it is generated by the neural activities of central nervous system and has been regarded as the gold standard to measure fatigue. However, most existing studies for the EEG-based driving fatigue detection have some common limitations such as: 1) using the batch learning mode and no incremental updating ability; 2) converting continuous fatigue indices into discrete levels which deviates far from the essence of fatigue detection; and 3) neglecting considering the different contributions of EEG feature dimensions in fatigue expression. To handle these problems, we propose an auto-weighting incremental random vector functional link (AWIRVFL) network model for EEG-based driving fatigue detection, which simultaneously implements online regression prediction and incremental learning. Moreover, an auto-weighting variable is introduced to adaptively and quantitatively explore the importance of different feature dimensions. A novel optimization algorithm is proposed to solve the AWIRVFL objective function. Experiments were conducted on the SEED-VIG and sustained-attention driving task (SADT) datasets to validate the performance of AWIRVFL, and the results demonstrated that AWIRVFL greatly outperforms the state-of-the-arts in terms of the two regression evaluation metrics, root mean square error (RMSE) and mean absolute percentage error (MAPE). Moreover, the quantitative feature importance values are obtained.

Automated summary

This study proposes a new model AWIRVFL for EEG-based driving fatigue detection, which addresses the limitations of existing methods. The AWIRVFL model incorporates an auto-weighting variable to consider the importance of different feature dimensions. Experimental results demonstrate that AWIRVFL outperforms existing techniques in driving fatigue detection.