Driver Fatigue Detection with Single EEG Channel Using Transfer Learning

Shalash, Wafaa M.

doi:10.1109/ist48021.2019.9010483

Cited by 27 publications

(14 citation statements)

References 29 publications

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“…The experimental results showed an average accuracy of around 100%. In [ 51 ], the authors suggest the detection of driver fatigue using a single EEG signal with the AlexNet CNN model. The achieved accuracy is respectively equal to 90% and 91%.…”

Section: Introductionmentioning

confidence: 99%

Convolutional Neural Network for Drowsiness Detection Using EEG Signals

Chaabene

Bouaziz

Boudaya

et al. 2021

Sensors

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Drowsiness detection (DD) has become a relevant area of active research in biomedical signal processing. Recently, various deep learning (DL) researches based on the EEG signals have been proposed to detect fatigue conditions. The research presented in this paper proposes an EEG classification system for DD based on DL networks. However, the proposed DD system is mainly realized into two procedures; (i) data acquisition and (ii) model analysis. For the data acquisition procedure, two key steps are considered, which are the signal collection using a wearable Emotiv EPOC+ headset to record 14 channels of EEG, and the signal annotation. Furthermore, a data augmentation (DA) step has been added to the proposed system to overcome the problem of over-fitting and to improve accuracy. As regards the model analysis, a comparative study is also introduced in this paper to argue the choice of DL architecture and frameworks used in our DD system. In this sense, The proposed DD protocol makes use of a convolutional neural network (CNN) architecture implemented using the Keras library. The results showed a high accuracy value (90.42%) in drowsy/awake discrimination and revealed the efficiency of the proposed DD system compared to other research works.

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Section: Introductionmentioning

confidence: 99%

Convolutional Neural Network for Drowsiness Detection Using EEG Signals

Chaabene

Bouaziz

Boudaya

et al. 2021

Sensors

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“…Over the last decade, active research has been carried out on the various feature extraction and classification techniques for EEG signals ( Wang et al, 2015 ; Salgado Patrón & Barrera, 2016 ; Schwarz et al, 2018 ; Chronopoulou, Baziotis & Potamianos, 2019 ; Rodrigues, Jutten & Congedo, 2019 ). A pre-trained convolution neural networks (CNN) (a variation of the Transfer Learning model) was investigated to improve the BCI-system usability of a driving system that utilises EEG signals ( Shalash, 2019 ). Online datasets were used in the research which was collected in a controlled lab environment through Neuro-scan data acquisition equipment with 30 effective channels and two reference electrodes.…”

Section: Introductionmentioning

confidence: 99%

“…A driver fatigue classification system through the use of TL models and single-channel EEG signals was investigated by Shalash (2019) . The proposed pipeline was evaluated on the online dataset obtained from Min, Wang & Hu (2017) that was downsampled from 1,000 to 500 samples.…”

Section: Introductionmentioning

confidence: 99%

The classification of EEG-based winking signals: a transfer learning and random forest pipeline

Kumar

Rashid

Musa

et al. 2021

PeerJ

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Brain Computer-Interface (BCI) technology plays a considerable role in the control of rehabilitation or peripheral devices for stroke patients. This is particularly due to their inability to control such devices from their inherent physical limitations after such an attack. More often than not, the control of such devices exploits electroencephalogram (EEG) signals. Nonetheless, it is worth noting that the extraction of the features and the classification of the signals is non-trivial for a successful BCI system. The use of Transfer Learning (TL) has been demonstrated to be a powerful tool in the extraction of essential features. However, the employment of such a method towards BCI applications, particularly in regard to EEG signals, are somewhat limited. The present study aims to evaluate the effectiveness of different TL models in extracting features for the classification of wink-based EEG signals. The extracted features are classified by means of fine-tuned Random Forest (RF) classifier. The raw EEG signals are transformed into a scalogram image via Continuous Wavelet Transform (CWT) before it was fed into the TL models, namely InceptionV3, Inception ResNetV2, Xception and MobileNet. The dataset was divided into training, validation, and test datasets, respectively, via a stratified ratio of 60:20:20. The hyperparameters of the RF models were optimised through the grid search approach, in which the five-fold cross-validation technique was adopted. The optimised RF classifier performance was compared with the conventional TL-based CNN classifier performance. It was demonstrated from the study that the best TL model identified is the Inception ResNetV2 along with an optimised RF pipeline, as it was able to yield a classification accuracy of 100% on both the training and validation dataset. Therefore, it could be established from the study that a comparable classification efficacy is attainable via the Inception ResNetV2 with an optimised RF pipeline. It is envisaged that the implementation of the proposed architecture to a BCI system would potentially facilitate post-stroke patients to lead a better life quality.

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“…Transfer learning is a commonly used technique with deep learning as it can overcome many problems associated with deep neural networks. Using transfer learning can reduce the training time and tuning efforts for many hyperparameters [ 60 ]. It transfers the knowledge from a pretrained network that was trained on large training data to a target network in which limited training data are available [ 11 ].…”

Section: Methodsmentioning

confidence: 99%

Diabetic Retinopathy Fundus Image Classification and Lesions Localization System Using Deep Learning

Alyoubi

Abulkhair

Shalash

2021

Sensors

174

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Diabetic retinopathy (DR) is a disease resulting from diabetes complications, causing non-reversible damage to retina blood vessels. DR is a leading cause of blindness if not detected early. The currently available DR treatments are limited to stopping or delaying the deterioration of sight, highlighting the importance of regular scanning using high-efficiency computer-based systems to diagnose cases early. The current work presented fully automatic diagnosis systems that exceed manual techniques to avoid misdiagnosis, reducing time, effort and cost. The proposed system classifies DR images into five stages—no-DR, mild, moderate, severe and proliferative DR—as well as localizing the affected lesions on retain surface. The system comprises two deep learning-based models. The first model (CNN512) used the whole image as an input to the CNN model to classify it into one of the five DR stages. It achieved an accuracy of 88.6% and 84.1% on the DDR and the APTOS Kaggle 2019 public datasets, respectively, compared to the state-of-the-art results. Simultaneously, the second model used an adopted YOLOv3 model to detect and localize the DR lesions, achieving a 0.216 mAP in lesion localization on the DDR dataset, which improves the current state-of-the-art results. Finally, both of the proposed structures, CNN512 and YOLOv3, were fused to classify DR images and localize DR lesions, obtaining an accuracy of 89% with 89% sensitivity, 97.3 specificity and that exceeds the current state-of-the-art results.

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Driver Fatigue Detection with Single EEG Channel Using Transfer Learning

Cited by 27 publications

References 29 publications

Convolutional Neural Network for Drowsiness Detection Using EEG Signals

Convolutional Neural Network for Drowsiness Detection Using EEG Signals

The classification of EEG-based winking signals: a transfer learning and random forest pipeline

Diabetic Retinopathy Fundus Image Classification and Lesions Localization System Using Deep Learning

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