EEG-Based Spatio–Temporal Convolutional Neural Network for Driver Fatigue Evaluation

Gao, Zhong-Ke; Wang, Xinmin; Yang, Yuxuan; Mu, Chaoxu; Cai, Qing; Dang, Weidong; Zuo, Siyang

doi:10.1109/tnnls.2018.2886414

Cited by 350 publications

(183 citation statements)

References 34 publications

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“…The effects of these noises can be substantially removed by different digital filtering methods. Recently, the Blind Source Separation (BSS) techniques such as Independent Component Analysis (ICA) are wildly used for the artifact removal of the Sensors 2020, 20, 1340 5 of 13 physiological signals [32][33][34], however, our signals are single channel signals while the BSS techniques requires multi-channels signals, thus, we choose the traditional methods to remove the noise. First, we applied a notch filter to remove the effect of the power frequency interference.…”

Section: Signal Preprocessingmentioning

confidence: 99%

A Novel Classification Method for a Driver’s Cognitive Stress Level by Transferring Interbeat Intervals of the ECG Signal to Pictures

Huang¹,

Luo²,

Peng³

2020

Sensors

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In this study, a novel classification method for a driver's cognitive stress level was proposed, whereby the interbeat intervals extracted from an electrocardiogram (ECG) signal were transferred to pictures, and a convolution neural network (CNN) was used to train the pictures to classify a driver's cognitive stress level. First, we defined three levels of tasks and collected the ECG signal of the driver at different cognitive stress levels by designing and performing a driving simulation experiment. We extracted the interbeat intervals and converted them to pictures according to the number of consecutive interbeat intervals in each picture. Second, the CNN model was used to train the data set to recognize the cognitive stress levels. Classification accuracies of 100%, 91.6% and 92.8% were obtained for the training set, validation set and test set, respectively, and were compared with those the BP neural network. Last, we discussed the influence of the number of interbeat intervals in each picture on the performance of the proposed classification method. The results showed that the performance initially improved with an increase in the number of interbeat intervals. A downward trend was observed when the number exceeded 40, and when the number was 40, the model performed best with the highest accuracy (98.79%) and a relatively low relative standard deviation (0.019).

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Section: Signal Preprocessingmentioning

confidence: 99%

A Novel Classification Method for a Driver’s Cognitive Stress Level by Transferring Interbeat Intervals of the ECG Signal to Pictures

Huang¹,

Luo²,

Peng³

2020

Sensors

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“…In recent years, convolutional neural networks (CNNs) have demonstrated impressive capabilities for data classification and feature extraction. For example, CNNs have been implemented for epilepsy prediction and monitoring [9], for detection of visual-evoked responses [10], for motor imagery classification [11], fatigue driving evaluation [12], [13], and emotion recognition [14], [15]. These studies have inspired many attempts to further improve the outcomes of EEG-based tasks through CNNs.…”

Section: Introductionmentioning

confidence: 99%

A Coincidence-Filtering-Based Approach for CNNs in EEG-Based Recognition

Gao

Yang

et al. 2020

IEEE Trans. Ind. Inf.

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Electroencephalogram (EEG), obtained by wearable devices, can realize effective human health monitoring. Traditional methods based on artificially-designed features have achieved valid results in EEG-based recognition, and numerous studies start to apply deep learning techniques in this area. In this paper, we propose a coincidence filtering-based method to build a connection between artificial features-based methods and convolutional neural networks (CNNs), and design CNNs through simulating the information extraction pattern of artificial features-based methods. Based on this method, we propose a novel, simple, and effective CNNs structure for EEG-based classification. We implement two experiments to obtain EEG data, and perform experiments based on the two health monitoring tasks. The results illustrate that the proposed network can achieve a prominent average accuracy on the emotion recognition and fatigue driving detection task. Due to its generality, the proposed framework design of CNNs is expected to be useful for broader applications in health monitoring areas.

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“…Research on the brain-computer interface (BCI) was primarily motivated by supporting interaction with the environment of disabled people [3][4][5]. Moreover, examples such as detecting and classifying epileptic seizures based on EEG signals [6], controlling driver fatigue [7], sleep disturbance detection [8], recognizing different mental states [8,9], etc. are of great importance.…”

Section: Introductionmentioning

confidence: 99%

“…The practical implementation of the brain-computer interface (BCI) systems uses electroencephalographic (EEG) signals [7,[10][11][12]. In BCI systems, the recorded signal is preconditioned in order to eliminate the artifacts and interferences, among others, resulting from eye blink, eye movement, muscle activity, or signal drift due to electrode misplacement [1,[13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

“…We are aware that there exists [49,51,53,61,[66][67][68][69][70][71][72][73][74][75][76][77][78], and they could be employed, e.g., as test data or in transfer learning applied to deep learning. However, many of the cited works are also exploratory in their character [7,9,47,55,63], they include a variety of datasets, signal acquisition methods, data formats, etc., which cannot be directly compared to the outcome of the study performed by us. Therefore, we have decided to acquire our own locally acquired data, especially as the experiments also served other purposes.…”

Section: Introductionmentioning

confidence: 99%

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Analyzing the Effectiveness of the Brain–Computer Interface for Task Discerning Based on Machine Learning

Browarczyk

Kurowski

Kostek

2020

Sensors

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The aim of the study is to compare electroencephalographic (EEG) signal feature extraction methods in the context of the effectiveness of the classification of brain activities. For classification, electroencephalographic signals were obtained using an EEG device from 17 subjects in three mental states (relaxation, excitation, and solving logical task). Blind source separation employing independent component analysis (ICA) was performed on obtained signals. Welch's method, autoregressive modeling, and discrete wavelet transform were used for feature extraction. Principal component analysis (PCA) was performed in order to reduce the dimensionality of feature vectors. k-Nearest Neighbors (kNN), Support Vector Machines (SVM), and Neural Networks (NN) were employed for classification. Precision, recall, F1 score, as well as a discussion based on statistical analysis, were shown. The paper also contains code utilized in preprocessing and the main part of experiments.

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EEG-Based Spatio–Temporal Convolutional Neural Network for Driver Fatigue Evaluation

Cited by 350 publications

References 34 publications

A Novel Classification Method for a Driver’s Cognitive Stress Level by Transferring Interbeat Intervals of the ECG Signal to Pictures

A Novel Classification Method for a Driver’s Cognitive Stress Level by Transferring Interbeat Intervals of the ECG Signal to Pictures

A Coincidence-Filtering-Based Approach for CNNs in EEG-Based Recognition

Analyzing the Effectiveness of the Brain–Computer Interface for Task Discerning Based on Machine Learning

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