A compact and interpretable convolutional neural network for cross-subject driver drowsiness detection from single-channel EEG
•An interpretable CNN model that can reveal important parts of input single-channel EEG signals for classification with the Class Activation Map (CAM) method.•The model can discover biologically explainable features from a diversity of EEG data of different subjects.•An average accuracy of 73.22% is...
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| Published in: | Methods (San Diego, Calif.) Vol. 202; pp. 173 - 184 |
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| Main Authors: | , , , , , , |
| Format: | Journal Article |
| Language: | English |
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01.06.2022
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| ISSN: | 1046-2023, 1095-9130, 1095-9130 |
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| Abstract | •An interpretable CNN model that can reveal important parts of input single-channel EEG signals for classification with the Class Activation Map (CAM) method.•The model can discover biologically explainable features from a diversity of EEG data of different subjects.•An average accuracy of 73.22% is achieved by the model on 11 subjects for 2-class cross-subject EEG signal classification.•We use the model to discover interesting features from EEG signals that can be indicators of alert or drowsy states.
Driver drowsiness is one of the main factors leading to road fatalities and hazards in the transportation industry. Electroencephalography (EEG) has been considered as one of the best physiological signals to detect drivers’ drowsy states, since it directly measures neurophysiological activities in the brain. However, designing a calibration-free system for driver drowsiness detection with EEG is still a challenging task, as EEG suffers from serious mental and physical drifts across different subjects. In this paper, we propose a compact and interpretable Convolutional Neural Network (CNN) to discover shared EEG features across different subjects for driver drowsiness detection. We incorporate the Global Average Pooling (GAP) layer in the model structure, allowing the Class Activation Map (CAM) method to be used for localizing regions of the input signal that contribute most for classification. Results show that the proposed model can achieve an average accuracy of 73.22% on 11 subjects for 2-class cross-subject EEG signal classification, which is higher than conventional machine learning methods and other state-of-art deep learning methods. It is revealed by the visualization technique that the model has learned biologically explainable features, e.g., Alpha spindles and Theta burst, as evidence for the drowsy state. It is also interesting to see that the model uses artifacts that usually dominate the wakeful EEG, e.g., muscle artifacts and sensor drifts, to recognize the alert state. The proposed model illustrates a potential direction to use CNN models as a powerful tool to discover shared features related to different mental states across different subjects from EEG signals. |
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| AbstractList | Driver drowsiness is one of the main factors leading to road fatalities and hazards in the transportation industry. Electroencephalography (EEG) has been considered as one of the best physiological signals to detect drivers' drowsy states, since it directly measures neurophysiological activities in the brain. However, designing a calibration-free system for driver drowsiness detection with EEG is still a challenging task, as EEG suffers from serious mental and physical drifts across different subjects. In this paper, we propose a compact and interpretable Convolutional Neural Network (CNN) to discover shared EEG features across different subjects for driver drowsiness detection. We incorporate the Global Average Pooling (GAP) layer in the model structure, allowing the Class Activation Map (CAM) method to be used for localizing regions of the input signal that contribute most for classification. Results show that the proposed model can achieve an average accuracy of 73.22% on 11 subjects for 2-class cross-subject EEG signal classification, which is higher than conventional machine learning methods and other state-of-art deep learning methods. It is revealed by the visualization technique that the model has learned biologically explainable features, e.g., Alpha spindles and Theta burst, as evidence for the drowsy state. It is also interesting to see that the model uses artifacts that usually dominate the wakeful EEG, e.g., muscle artifacts and sensor drifts, to recognize the alert state. The proposed model illustrates a potential direction to use CNN models as a powerful tool to discover shared features related to different mental states across different subjects from EEG signals. •An interpretable CNN model that can reveal important parts of input single-channel EEG signals for classification with the Class Activation Map (CAM) method.•The model can discover biologically explainable features from a diversity of EEG data of different subjects.•An average accuracy of 73.22% is achieved by the model on 11 subjects for 2-class cross-subject EEG signal classification.•We use the model to discover interesting features from EEG signals that can be indicators of alert or drowsy states. Driver drowsiness is one of the main factors leading to road fatalities and hazards in the transportation industry. Electroencephalography (EEG) has been considered as one of the best physiological signals to detect drivers’ drowsy states, since it directly measures neurophysiological activities in the brain. However, designing a calibration-free system for driver drowsiness detection with EEG is still a challenging task, as EEG suffers from serious mental and physical drifts across different subjects. In this paper, we propose a compact and interpretable Convolutional Neural Network (CNN) to discover shared EEG features across different subjects for driver drowsiness detection. We incorporate the Global Average Pooling (GAP) layer in the model structure, allowing the Class Activation Map (CAM) method to be used for localizing regions of the input signal that contribute most for classification. Results show that the proposed model can achieve an average accuracy of 73.22% on 11 subjects for 2-class cross-subject EEG signal classification, which is higher than conventional machine learning methods and other state-of-art deep learning methods. It is revealed by the visualization technique that the model has learned biologically explainable features, e.g., Alpha spindles and Theta burst, as evidence for the drowsy state. It is also interesting to see that the model uses artifacts that usually dominate the wakeful EEG, e.g., muscle artifacts and sensor drifts, to recognize the alert state. The proposed model illustrates a potential direction to use CNN models as a powerful tool to discover shared features related to different mental states across different subjects from EEG signals. Driver drowsiness is one of the main factors leading to road fatalities and hazards in the transportation industry. Electroencephalography (EEG) has been considered as one of the best physiological signals to detect drivers' drowsy states, since it directly measures neurophysiological activities in the brain. However, designing a calibration-free system for driver drowsiness detection with EEG is still a challenging task, as EEG suffers from serious mental and physical drifts across different subjects. In this paper, we propose a compact and interpretable Convolutional Neural Network (CNN) to discover shared EEG features across different subjects for driver drowsiness detection. We incorporate the Global Average Pooling (GAP) layer in the model structure, allowing the Class Activation Map (CAM) method to be used for localizing regions of the input signal that contribute most for classification. Results show that the proposed model can achieve an average accuracy of 73.22% on 11 subjects for 2-class cross-subject EEG signal classification, which is higher than conventional machine learning methods and other state-of-art deep learning methods. It is revealed by the visualization technique that the model has learned biologically explainable features, e.g., Alpha spindles and Theta burst, as evidence for the drowsy state. It is also interesting to see that the model uses artifacts that usually dominate the wakeful EEG, e.g., muscle artifacts and sensor drifts, to recognize the alert state. The proposed model illustrates a potential direction to use CNN models as a powerful tool to discover shared features related to different mental states across different subjects from EEG signals.Driver drowsiness is one of the main factors leading to road fatalities and hazards in the transportation industry. Electroencephalography (EEG) has been considered as one of the best physiological signals to detect drivers' drowsy states, since it directly measures neurophysiological activities in the brain. However, designing a calibration-free system for driver drowsiness detection with EEG is still a challenging task, as EEG suffers from serious mental and physical drifts across different subjects. In this paper, we propose a compact and interpretable Convolutional Neural Network (CNN) to discover shared EEG features across different subjects for driver drowsiness detection. We incorporate the Global Average Pooling (GAP) layer in the model structure, allowing the Class Activation Map (CAM) method to be used for localizing regions of the input signal that contribute most for classification. Results show that the proposed model can achieve an average accuracy of 73.22% on 11 subjects for 2-class cross-subject EEG signal classification, which is higher than conventional machine learning methods and other state-of-art deep learning methods. It is revealed by the visualization technique that the model has learned biologically explainable features, e.g., Alpha spindles and Theta burst, as evidence for the drowsy state. It is also interesting to see that the model uses artifacts that usually dominate the wakeful EEG, e.g., muscle artifacts and sensor drifts, to recognize the alert state. The proposed model illustrates a potential direction to use CNN models as a powerful tool to discover shared features related to different mental states across different subjects from EEG signals. |
| Author | Li, Fan Müller-Wittig, Wolfgang Lan, Zirui Liu, Yisi Li, Ruilin Sourina, Olga Cui, Jian |
| Author_xml | – sequence: 1 givenname: Jian surname: Cui fullname: Cui, Jian email: cuijian@ntu.edu.sg organization: Fraunhofer Singapore, Nanyang Technological University, Singapore – sequence: 2 givenname: Zirui surname: Lan fullname: Lan, Zirui email: lan.zirui@fraunhofer.sg organization: Fraunhofer Singapore, Singapore – sequence: 3 givenname: Yisi surname: Liu fullname: Liu, Yisi email: liu.yisi@fraunhofer.sg organization: Fraunhofer Singapore, Singapore – sequence: 4 givenname: Ruilin surname: Li fullname: Li, Ruilin email: RUILIN001@e.ntu.edu.sg organization: Nanyang Technological University, Singapore – sequence: 5 givenname: Fan surname: Li fullname: Li, Fan email: lifan@ntu.edu.sg organization: Fraunhofer Singapore, Nanyang Technological University, Singapore – sequence: 6 givenname: Olga surname: Sourina fullname: Sourina, Olga email: EOSourina@ntu.edu.sg organization: Fraunhofer Singapore, Nanyang Technological University, Singapore – sequence: 7 givenname: Wolfgang surname: Müller-Wittig fullname: Müller-Wittig, Wolfgang email: Wolfgang.Mueller-wittig@fraunhofer.sg organization: Fraunhofer Singapore, Nanyang Technological University, Singapore |
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| Keywords | Network visualization Interpretable CNN Single-channel EEG Driver drowsiness detection Convolutional neural network Class activation mapping |
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| Snippet | •An interpretable CNN model that can reveal important parts of input single-channel EEG signals for classification with the Class Activation Map (CAM)... Driver drowsiness is one of the main factors leading to road fatalities and hazards in the transportation industry. Electroencephalography (EEG) has been... |
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| Title | A compact and interpretable convolutional neural network for cross-subject driver drowsiness detection from single-channel EEG |
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