Driving Fatigue Detection from EEG Using a Modified PCANet Method

Ma, Yuliang; Chen, Bin; Li, Rihui; Wang, Chushan; Wang, Jun; She, Qingshan; Luo, Zhizeng; Zhang, Yingchun

doi:10.1155/2019/4721863

Cited by 75 publications

(36 citation statements)

References 31 publications

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“…Recent conventional works have focused on the classification of user mental states with high performances using advanced machine learning algorithms and deep learning methods such as [54]- [60]. Zhang et al [54] introduced two deep learning-based frameworks with novel spatiotemporal preserving representations of raw EEG streams to identify human intentions with high performances.…”

Section: Discussionmentioning

confidence: 99%

Continuous EEG Decoding of Pilots’ Mental States Using Multiple Feature Block-Based Convolutional Neural Network

et al. 2020

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Non-invasive brain-computer interface (BCI) has been developed for recognizing and classifying human mental states with high performances. Specifically, classifying pilots' mental states accurately is a critical issue because their cognitive states, which are induced by mental fatigue, workload, and distraction, may be fundamental in catastrophic accidents. In this study, we present an electroencephalogram (EEG) classification of four mental states (fatigue, workload, distraction, and the normal state) from EEG signals in both offline and pseudo-online analyses. To the best of our knowledge, this study is the first attempt to classify pilots' mental states using only EEG signals during continuous decoding. We recorded EEG signals from seven pilots under various simulated flight conditions. We proposed a multiple feature block-based convolutional neural network (MFB-CNN) with temporal-spatio EEG filters to recognize the pilot's current mental states. We validated the proposed method for two analyses across all subjects. In the offline analysis, we confirmed the classification accuracy of 0.75 (±0.04) and, in the pseudo-online analysis, we obtained the detection accuracy of 0.72 (±0.20), 0.72 (±0.27), and 0.61 (±0.18) for fatigue, workload, and distraction, respectively. Hence, we demonstrate the feasibility of classifying various types of mental states for implementation in real-world environments. INDEX TERMS Brain-computer interface (BCI), Electroencephalogram (EEG), Mental states, Deep convolutional neural network.

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Section: Discussionmentioning

confidence: 99%

Continuous EEG Decoding of Pilots’ Mental States Using Multiple Feature Block-Based Convolutional Neural Network

et al. 2020

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“…Recent conventional works with respect to the BCI-based detection of mental states have focused on accurate classification of user mental states using advanced machine learning algorithms and deep learning architecture [60][61][62][63][64][65]. Recognizing the mental conditions of drivers or pilots is a critical issue in systems using AI technology, such as autonomous vehicles and autopilot.…”

Section: Discussionmentioning

confidence: 99%

Classification of Drowsiness Levels Based on a Deep Spatio-Temporal Convolutional Bidirectional LSTM Network Using Electroencephalography Signals

Jeong

Lee

et al. 2019

Brain Sciences

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Non-invasive brain-computer interfaces (BCI) have been developed for recognizing human mental states with high accuracy and for decoding various types of mental conditions. In particular, accurately decoding a pilot’s mental state is a critical issue as more than 70% of aviation accidents are caused by human factors, such as fatigue or drowsiness. In this study, we report the classification of not only two mental states (i.e., alert and drowsy states) but also five drowsiness levels from electroencephalogram (EEG) signals. To the best of our knowledge, this approach is the first to classify drowsiness levels in detail using only EEG signals. We acquired EEG data from ten pilots in a simulated night flight environment. For accurate detection, we proposed a deep spatio-temporal convolutional bidirectional long short-term memory network (DSTCLN) model. We evaluated the classification performance using Karolinska sleepiness scale (KSS) values for two mental states and five drowsiness levels. The grand-averaged classification accuracies were 0.87 (±0.01) and 0.69 (±0.02), respectively. Hence, we demonstrated the feasibility of classifying five drowsiness levels with high accuracy using deep learning.

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“…Hand motion recognition [9][10][11][12][13][14][15][16][17], Muscle activity recognition [18][19][20][21][22][23] ECG Heartbeat signal classification , Heart disease classification [49][50][51][52][53][54][55][56][57][58][59][60][61][62][63], Sleep-stage classification [64][65][66][67][68], Emotion classification [69], age and gender prediction [70] EEG Brain functionality classification , Brain disease classification , Emotion classification [122][123][124][125][126][127][128][129], Sleep-stage classification [130][131][132][133]…”

Section: Emgmentioning

confidence: 99%

“…Ma et al [131] designed a model to detect driving fatigue. The network model integrated the PCA and deep-learning method called PCANet for feature extraction.…”

Section: Deep Learning As Feature Extractor and Traditional Machine Lmentioning

confidence: 99%

Deep Learning in Physiological Signal Data: A Survey

Rim

Sung

Min

et al. 2020

Sensors

171

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Deep Learning (DL), a successful promising approach for discriminative and generative tasks, has recently proved its high potential in 2D medical imaging analysis; however, physiological data in the form of 1D signals have yet to be beneficially exploited from this novel approach to fulfil the desired medical tasks. Therefore, in this paper we survey the latest scientific research on deep learning in physiological signal data such as electromyogram (EMG), electrocardiogram (ECG), electroencephalogram (EEG), and electrooculogram (EOG). We found 147 papers published between January 2018 and October 2019 inclusive from various journals and publishers. The objective of this paper is to conduct a detailed study to comprehend, categorize, and compare the key parameters of the deep-learning approaches that have been used in physiological signal analysis for various medical applications. The key parameters of deep-learning approach that we review are the input data type, deep-learning task, deep-learning model, training architecture, and dataset sources. Those are the main key parameters that affect system performance. We taxonomize the research works using deep-learning method in physiological signal analysis based on: (1) physiological signal data perspective, such as data modality and medical application; and (2) deep-learning concept perspective such as training architecture and dataset sources.

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Driving Fatigue Detection from EEG Using a Modified PCANet Method

Cited by 75 publications

References 31 publications

Continuous EEG Decoding of Pilots’ Mental States Using Multiple Feature Block-Based Convolutional Neural Network

Continuous EEG Decoding of Pilots’ Mental States Using Multiple Feature Block-Based Convolutional Neural Network

Classification of Drowsiness Levels Based on a Deep Spatio-Temporal Convolutional Bidirectional LSTM Network Using Electroencephalography Signals

Deep Learning in Physiological Signal Data: A Survey

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