Driver Drowsiness Estimation From EEG Signals Using Online Weighted Adaptation Regularization for Regression (OwARR)

Wu, Dongrui; Lawhern, Vernon J.; Gordon, Stephen; Lance, Brent J.; Lin, Chin‐Teng

doi:10.1109/tfuzz.2016.2633379

Cited by 128 publications

(82 citation statements)

References 64 publications

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“…Each participant read and signed an informed consent form before the experiment began. The reaction time τ was later converted into a drowsiness index (DI) [21], [22], [42]- [44],…”

Section: A Datasetmentioning

confidence: 99%

EEG-Based Driver Drowsiness Estimation Using Feature Weighted Episodic Training

Cui

2019

IEEE Trans. Neural Syst. Rehabil. Eng.

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Drowsy driving is pervasive, and also a major cause of traffic accidents. Estimating a driver's drowsiness level by monitoring the electroencephalogram (EEG) signal and taking preventative actions accordingly may improve driving safety. However, individual differences among different drivers make this task very challenging. A calibration session is usually required to collect some subject-specific data and tune the model parameters before applying it to a new subject, which is very inconvenient and not user-friendly. Many approaches have been proposed to reduce the calibration effort, but few can completely eliminate it. This paper proposes a novel approach, feature weighted episodic training (FWET), to completely eliminate the calibration requirement. It integrates two techniques: feature weighting to learn the importance of different features, and episodic training for domain generalization. Experiments on EEG-based driver drowsiness estimation demonstrated that both feature weighting and episodic training are effective, and their integration can further improve the generalization performance. FWET does not need any labelled or unlabelled calibration data from the new subject, and hence could be very useful in plugand-play brain-computer interfaces.

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“…Each participant read and signed an informed consent form before the experiment began. The reaction time τ was later converted into a drowsiness index (DI) [21], [22], [42]- [44],…”

Section: A Datasetmentioning

confidence: 99%

EEG-Based Driver Drowsiness Estimation Using Feature Weighted Episodic Training

Cui

2019

IEEE Trans. Neural Syst. Rehabil. Eng.

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“…For example, in speech emotion estimation [1,2] in the 3-dimensional space of valance, arousal and dominance [3], it is easy to record a large number of voice pieces, but time-consuming to evaluate their emotions [4,5]. Another example is driver drowsiness estimation from physiological signals such as the electroencephalogram (EEG) [6,7,8]. It is relatively easy to collect a large number of EEG trials, but not easy to obtain their groundtruth drowsiness.…”

Section: Introductionmentioning

confidence: 99%

“…Transfer learning [13], which uses data or information from related domains/tasks to improve the regression performance. For example, labeled EEG data from other subjects could be used to improve the drowsiness estimation performance for a new subject [6,14]. 3.…”

Section: Introductionmentioning

confidence: 99%

Active learning for regression using greedy sampling

Lin

Huang

2019

Information Sciences

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Regression problems are pervasive in real-world applications. Generally a substantial amount of labeled samples are needed to build a regression model with good generalization ability. However, many times it is relatively easy to collect a large number of unlabeled samples, but time-consuming or expensive to label them. Active learning for regression (ALR) is a methodology to reduce the number of labeled samples, by selecting the most beneficial ones to label, instead of random selection. This paper proposes two new ALR approaches based on greedy sampling (GS). The first approach (GSy) selects new samples to increase the diversity in the output space, and the second (iGS) selects new samples to increase the diversity in both input and output spaces. Extensive experiments on 12 UCI and CMU StatLib datasets from various domains, and on 15 subjects on EEG-based driver drowsiness estimation, verified their effectiveness and robustness.

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“…Electroencephalogram (EEG), which measures the brain signal from the scalp, is the most widely used input signal in BCIs, due to its simplicity and low cost [25]. There are different paradigms in using EEG signals in BCIs, e.g., P300 evoked potentials [10], [33], [40], [43], motor imagery (MI) [29], steady-state visual evoked potential (SSVEP) [47], drowsiness/reaction time estimation [41], [42], [44], etc.…”

Section: Introductionmentioning

confidence: 99%

On the Vulnerability of CNN Classifiers in EEG-Based BCIs

Zhang

2019

IEEE Trans. Neural Syst. Rehabil. Eng.

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Deep learning has been successfully used in numerous applications because of its outstanding performance and the ability to avoid manual feature engineering. One such application is electroencephalogram (EEG) based brain-computer interface (BCI), where multiple convolutional neural network (CNN) models have been proposed for EEG classification. However, it has been found that deep learning models can be easily fooled with adversarial examples, which are normal examples with small deliberate perturbations. This paper proposes an unsupervised fast gradient sign method (UFGSM) to attack three popular CNN classifiers in BCIs, and demonstrates its effectiveness. We also verify the transferability of adversarial examples in BCIs, which means we can perform attacks even without knowing the architecture and parameters of the target models, or the datasets they were trained on. To our knowledge, this is the first study on the vulnerability of CNN classifiers in EEG-based BCIs, and hopefully will trigger more attention on the security of BCI systems.

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Driver Drowsiness Estimation From EEG Signals Using Online Weighted Adaptation Regularization for Regression (OwARR)

Cited by 128 publications

References 64 publications

EEG-Based Driver Drowsiness Estimation Using Feature Weighted Episodic Training

EEG-Based Driver Drowsiness Estimation Using Feature Weighted Episodic Training

Active learning for regression using greedy sampling

On the Vulnerability of CNN Classifiers in EEG-Based BCIs

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