Key result
The proposed noise fraction analysis and CEPM clustering algorithm achieved a clustering accuracy above 90% for all participants, resulting in a high driver fatigue recognition rate.
Why the study?
Driver states influence driving safety, but redundant information and noise reduce the signal-to-noise ratio of EEG signals used to distinguish driver states.
Does noise fraction analysis combined with CEPM clustering improve driver fatigue recognition from EEG signals?
Does noise fraction analysis combined with CEPM clustering improve driver fatigue recognition from EEG signals?
A novel noise fraction analysis and clustering algorithm effectively removes EOG artifacts from EEG signals, achieving over 90% accuracy in recognizing driver fatigue.
Proposed EOG removal may enhance EEG driver-state detection; leaves open larger validation before any clinical use.
Driver states are reported as one of the principal factors in driving safety. Distinguishing the driving driver state based on the artifact-free electroencephalogram (EEG) signal is an effective means, but redundant information and noise will inevitably reduce the signal-to-noise ratio of the EEG signal. This study proposes a method to automatically remove electrooculography (EOG) artifacts by noise fraction analysis. Specifically, multi-channel EEG recordings are collected after the driver experiences a long time driving and after a certain period of rest respectively. Noise fraction analysis is then applied to remove EOG artifacts by separating the multichannel EEG into components by optimizing the signal-to-noise quotient. The representation of data characteristics of the EEG after denoising is found in the Fisher ratio space. Additionally, a novel clustering algorithm is designed to identify denoising EEG by combining cluster ensemble and probability mixture model (CEPM). The EEG mapping plot is used to illustrate the effectiveness and efficiency of noise fraction analysis on the denoising of EEG signals. Adjusted rand index (ARI) and accuracy (ACC) are used to demonstrate clustering performance and precision. The results showed that the noise artifacts in the EEG were removed and the clustering accuracy of all participants was above 90%, resulting in a high driver fatigue recognition rate.
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Fu et al. (2023) studied Driver fatigue. Noise fraction analysis and CEPM clustering algorithm was evaluated on Clustering accuracy. The proposed noise fraction analysis and CEPM clustering algorithm achieved a clustering accuracy above 90% for all participants, resulting in a high driver fatigue recognition rate.
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