EEG-based Driving Fatigue Detection during Operating the Steering Wheel Data Section

Zou, Bing; Shen, Mu; Li, Xinhang; Zheng, Yubo; Zhang, Lin

doi:10.1109/embc44109.2020.9175962

Cited by 3 publications

(7 citation statements)

References 10 publications

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“…As a comparison, we also present results from works using the same dataset with a cross-subject approach [37], [34], [35], whose authors used a reduced group of subjects from the dataset, as shown in Table II. When put alongside our results, shown in Table II, a superior performance was achieved against those approaches, particularly when considering the use of a more robust metric, balanced accuracy, and the use of more test subjects, 25.…”

Section: Resultsmentioning

confidence: 99%

“…In [23] a combination of a deep neural network with support vector machine (SVM) classifier at the last layer was also proposed. In [34], [35], the authors used graph convolutional networks and multi-layer perceptron, respectively, to address fatigue detection in pre-event and during steering wheel operation on the same dataset of our work.…”

Section: Related Workmentioning

confidence: 99%

“…Other works perform cross-session instead of cross-subject [36], but this is only useful for the same subject in future trials. To the best of our knowledge, it could only be found 4 preliminary works which performed cross-subject validation, considering the driving context [37], [38], [34], [35]. In [37], the authors perform domain adaptation, a branch of transfer learning, to adapt the data distributions of source and target so that the classification could be more efficient in a crosssubject scenario.…”

Section: Related Workmentioning

confidence: 99%

“…In [38], EEG features, statistics, higher order crossing, fractal dimension, hjorth, signal energy, and spectral power were extracted and combined with several classifiers, such as logistic regression, linear discriminant analysis, 1nearest neighbor, linear SVM, and naïve Bayes. While in [34], [35], as described above, the authors pursue driver's drowsiness detection using the same dataset as in this paper, relying neural network-based methodologies.…”

Section: Related Workmentioning

confidence: 99%

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Cross-Subject Zero Calibration Driver’s Drowsiness Detection: Exploring Spatiotemporal Image Encoding of EEG Signals for Convolutional Neural Network Classification

Paulo

Pires

Nunes

2021

IEEE Trans. Neural Syst. Rehabil. Eng.

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This paper explores two methodologies for drowsiness detection using EEG signals in a sustained-attention driving task considering pre-event time windows, and focusing on crosssubject zero calibration. Driving accidents are a major cause of injuries and deaths on the road. A considerable portion of those are due to fatigue and drowsiness. Advanced driver assistance systems that could detect mental states which are associated with hazardous situations, such as drowsiness, are of critical importance. EEG signals are used widely for brain-computer interfaces, as well as mental state recognition. However, these systems are still difficult to design due to very low signal-tonoise ratios and cross-subject disparities, requiring individual calibration cycles. To tackle this research domain, here, we explore drowsiness detection based on EEG signals' spatiotemporal image encoding representations in the form of either recurrence plots or gramian angular fields for deep convolutional neural network (CNN) classification. Results comparing both techniques using a public dataset of 27 subjects show a superior balanced accuracy of up to 75.87% for leave-one-out cross-validation, using both techniques, against works in the literature, demonstrating the possibility to pursue cross-subject zero calibration design.

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Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Cross-Subject Zero Calibration Driver’s Drowsiness Detection: Exploring Spatiotemporal Image Encoding of EEG Signals for Convolutional Neural Network Classification

Paulo

Pires

Nunes

2021

IEEE Trans. Neural Syst. Rehabil. Eng.

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“…The purposes of these researches are wide and could help in various fields: medical, entertainment, road safety or marketing. The proposed approaches consider the use of ML models that have already shown outperforming results in other fields: fullyconnected neural networks [7], convolutional neural networks (CNN) [6], recurrent neural networks (RNN) [3] or combinations of these last. Moreover, the models can consider extracted features from EEG [5] or preprocessed EEG directly [6].…”

Section: Introductionmentioning

confidence: 99%

Spatio-Temporal Analysis of Transformer based Architecture for Attention Estimation from EEG

Delvigne¹,

Wannous²,

Vandeborre³

et al. 2022

Preprint

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For many years now, understanding the brain mechanism has been a great research subject in many different fields. Brain signal processing and especially electroencephalogram (EEG) has recently known a growing interest both in academia and industry. One of the main examples is the increasing number of Brain-Computer Interfaces (BCI) aiming to link brains and computers. In this paper, we present a novel framework allowing us to retrieve the attention state, i.e degree of attention given to a specific task, from EEG signals. While previous methods often consider the spatial relationship in EEG through electrodes and process them in recurrent or convolutional based architecture, we propose here to also exploit the spatial and temporal information with a transformer-based network that has already shown its supremacy in many machine-learning (ML) related studies, e.g. machine translation. In addition to this novel architecture, an extensive study on the feature extraction methods, frequential bands and temporal windows length has also been carried out. The proposed network has been trained and validated on two public datasets and achieves higher results compared to state-of-the-art models. As well as proposing better results, the framework could be used in real applications, e.g. Attention Deficit Hyperactivity Disorder (ADHD) symptoms or vigilance during a driving assessment.

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Spatio-Temporal Analysis of Transformer based Architecture for Attention Estimation from EEG

Delvigne

Wannous²,

Vandeborre³

et al. 2022

2022 26th International Conference on Pattern Recognition (ICPR)

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EEG-based Driving Fatigue Detection during Operating the Steering Wheel Data Section

Cited by 3 publications

References 10 publications

Cross-Subject Zero Calibration Driver’s Drowsiness Detection: Exploring Spatiotemporal Image Encoding of EEG Signals for Convolutional Neural Network Classification

Cross-Subject Zero Calibration Driver’s Drowsiness Detection: Exploring Spatiotemporal Image Encoding of EEG Signals for Convolutional Neural Network Classification

Spatio-Temporal Analysis of Transformer based Architecture for Attention Estimation from EEG

Spatio-Temporal Analysis of Transformer based Architecture for Attention Estimation from EEG

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