A subject-transfer framework for obviating inter- and intra-subject variability in EEG-based drowsiness detection

Wei, Chun-Shu; Lin, Yuan-Pin; Wang, Yu Te; Lin, Chin‐Tsai; Jung, Tzyy-Ping

doi:10.1016/j.neuroimage.2018.03.032

Cited by 93 publications

(82 citation statements)

References 42 publications

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“…These studies show that DBN can predict around 85% of the variation in cognitive state. A subject-transfer framework for detecting drowsiness during simulated driving task based on EEG was also recently developed (Wei et al, 2018). In that study, response time was measured from the onset of a lane deviation to the onset of the response, which served as a behavioral assessment of drowsiness during the lane-keeping task.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Pilot's Reaction Time Based on EEG Signals

Binias

Myszor

Palus

et al. 2020

Front. Neuroinform.

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The main hypothesis of this work is that the time of delay in reaction to an unexpected event can be predicted on the basis of the brain activity recorded prior to that event. Such mental activity can be represented by electroencephalographic data. To test this hypothesis, we conducted a novel experiment involving 19 participants that took part in a 2-h long session of simulated aircraft flights. An EEG signal processing pipeline is proposed that consists of signal preprocessing, extracting bandpass features, and using regression to predict the reaction times. The prediction algorithms that are used in this study are the Least Absolute Shrinkage Operator and its Least Angle Regression modification, as well as Kernel Ridge and Radial Basis Support Vector Machine regression. The average Mean Absolute Error obtained across the 19 subjects was 114 ms. The present study demonstrates, for the first time, that it is possible to predict reaction times on the basis of EEG data. The presented solution can serve as a foundation for a system that can, in the future, increase the safety of air traffic.

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Section: Introductionmentioning

confidence: 99%

Prediction of Pilot's Reaction Time Based on EEG Signals

Binias

Myszor

Palus

et al. 2020

Front. Neuroinform.

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“…Anatomic and environmental factors are attributed as the main causes of the typical difference of neural responses across individuals subjected to the same stimulus [5], [6], [7]. As a consequence, EEG data collected from different subjects commonly present shifts in the conditional distribution.…”

Section: Introductionmentioning

confidence: 99%

Cross-Subject Statistical Shift Estimation for Generalized Electroencephalography-based Mental Workload Assessment

Albuquerque

Monteiro

Rosanne

et al. 2019

2019 IEEE International Conference on Systems, Man and Cybernetics (SMC)

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Assessment of mental workload in real world conditions is key to ensure the performance of workers executing tasks which demand sustained attention. Previous literature has employed electroencephalography (EEG) to this end. However, EEG correlates of mental workload vary across subjects and physical strain, thus making it difficult to devise models capable of simultaneously presenting reliable performance across users. The field of domain adaptation (DA) aims at developing methods that allow for generalization across different domains by learning domain-invariant representations. Such DA methods, however, rely on the so-called covariate shift assumption, which typically does not hold for EEG-based applications. As such, in this paper we propose a way to measure the statistical (marginal and conditional) shift observed on data obtained from different users and use this measure to quantitatively assess the effectiveness of different adaptation strategies. In particular, we use EEG data collected from individuals performing a mental task while running in a treadmill and explore the effects of different normalization strategies commonly used to mitigate cross-subject variability. We show the effects that different normalization schemes have on statistical shifts and their relationship with the accuracy of mental workload prediction as assessed on unseen participants at train time.

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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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A subject-transfer framework for obviating inter- and intra-subject variability in EEG-based drowsiness detection

Cited by 93 publications

References 42 publications

Prediction of Pilot's Reaction Time Based on EEG Signals

Prediction of Pilot's Reaction Time Based on EEG Signals

Cross-Subject Statistical Shift Estimation for Generalized Electroencephalography-based Mental Workload Assessment

EEG-Based Driver Drowsiness Estimation Using Feature Weighted Episodic Training

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