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Driving fatigue detection based on brain source activity and ARMA model

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Abstract

Fatigue among drivers is a significant issue in society, and according to organizational reports, it substantially contributes to accidents. So accurate fatigue detection in drivers plays a crucial role in reducing the number of people fatalities or injured resulting from accidents. Several methods are proposed for fatigue driver recognition among which electroencephalography (EEG) is one. This paper proposed a method for fatigue recognition by EEG signals with extracted features from source and sensor spaces. The proposed method starts with preprocessing by applying filtering and artifact rejection. Then source localization methods are applied to EEG signals for active source extraction. A multivariate autoregressive (MVAR) model is fitted to selected sources, and a dual Kalman filter is applied to estimate the source activity and their relationships. Then multivariate autoregressive moving average (ARMA) is fitted between EEG and source activity signals. Features are extracted from model parameters, source relationship matrix, and wavelet transform of EEG and source activity signals. The novelty of this approach is the use of ARMA model between source activities (as input) and EEG signals (as output) and feature extraction from source relations. Relevant features are selected using a combination of RelifF and neighborhood component analysis (NCA) methods. Three classifiers, namely k-nearest neighbor (KNN), support vector machine (SVM), and naive Bayesian (NB) classifiers, are employed to classify drivers. To improve performance, the final label for fatigue detection is calculated by combining these classifiers using the voting method. The results demonstrate that the proposed method accurately recognizes and classifies fatigued drivers with the ensemble classifiers in comparison with other methods.

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Authors and Affiliations

  1. Faculty of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran

    Fahimeh Nadalizadeh

  2. Department of Engineering, Mamaghan Branch, Islamic Azad University, Mamaghan, Iran

    Mehdi Rajabioun

  3. Department of Electrical and Computer Engineering, Tabriz University, Tabriz, Iran

    Amirreza Feyzi

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Correspondence to Mehdi Rajabioun.

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The authors declare that no funds, grants, or other support were received during the preparation of this manuscript, and the authors have no relevant financial or non-financial interests to disclose. This article does not contain any studies with human participants or animals performed by any of the authors. All authors contributed to the study conception and design. The first draft of the manuscript was written by authors, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. This is an observational study and no ethical approval is required.

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Nadalizadeh, F., Rajabioun, M. & Feyzi, A. Driving fatigue detection based on brain source activity and ARMA model. Med Biol Eng Comput 62, 1017–1030 (2024). https://doi.org/10.1007/s11517-023-02983-z

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