EEG-Based Drowsiness Detection With Fuzzy Independent Phase-Locking Value Representations Using Lagrangian-Based Deep Neural Networks

Reddy, Tharun Kumar; Arora, Vipul; Gupta, Vinay K.; Biswas, Romita; Behera, Laxmidhar

doi:10.1109/tsmc.2021.3113823

Cited by 16 publications

(5 citation statements)

References 57 publications

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“…We looked at numerous descriptive dimensions to investigate this question: the number of participants, the amount of EEG data collected, and the task of the datasets. There are few studies that make use of their own collected datasets (Tang et al, 2017 ; Vilamala et al, 2017 ; Antoniades et al, 2018 ; Aznan et al, 2018 ; Behncke et al, 2018 ; El-Fiqi et al, 2018 ; Nguyen and Chung, 2018 ; Alazrai et al, 2019 ; Chen et al, 2019b ; Fahimi et al, 2019 ; Hussein et al, 2019 ; Zgallai et al, 2019 ; Gao et al, 2020b ; León et al, 2020 ; Maiorana, 2020 ; Penchina et al, 2020 ; Tortora et al, 2020 ; Atilla and Alimardani, 2021 ; Cai et al, 2021 ; Cho et al, 2021 ; Mai et al, 2021 ; Mammone et al, 2021 ; Petoku and Capi, 2021 ; Reddy et al, 2021 ; Shoeibi et al, 2021 ; Sundaresan et al, 2021 ; Ak et al, 2022 ). However, most of the deep learning studies have been conducted based on publicly available EEG datasets, such as:…”

Section: Utilizing Deep Learning In Eeg-based Bcimentioning

confidence: 99%

“…Various deep learning algorithms have been employed in EEG-based BCI applications, whereas CNN is clearly the most frequent one. For example, Arnau-González et al ( 2017 ), Tang et al ( 2017 ), Vilamala et al ( 2017 ), Antoniades et al ( 2018 ), Aznan et al ( 2018 ), Behncke et al ( 2018 ), Dose et al ( 2018 ), El-Fiqi et al ( 2018 ), Nguyen and Chung ( 2018 ), Völker et al ( 2018 ), Alazrai et al ( 2019 ), Amber et al ( 2019 ), Amin et al ( 2019b ), Chen et al ( 2019a , b ), Fahimi et al ( 2019 ), Gao et al ( 2019 ), Olivas-Padilla and Chacon-Murguia ( 2019 ), Ozdemir et al ( 2019 ), Roy et al ( 2019 ), Song et al ( 2019 ), Tayeb et al ( 2019 ), Zgallai et al ( 2019 ), Zhao et al ( 2019 ), Aldayel et al ( 2020 ), Gao et al ( 2020a , b ), Hwang et al ( 2020 ), Ko et al ( 2020 ), Li Y. et al ( 2020 ), Liu J. et al ( 2020 ), Miao et al ( 2020 ), Oh et al ( 2020 ), Polat and Özerdem ( 2020 ), Atilla and Alimardani ( 2021 ), Cai et al ( 2021 ), Dang et al ( 2021 ), Deng et al ( 2021 ), Huang et al ( 2021 ), Ieracitano et al ( 2021 ), Mai et al ( 2021 ), Mammone et al ( 2021 ), Petoku and Capi ( 2021 ), Reddy et al ( 2021 ), Tiwari et al ( 2021 ), Zhang et al ( 2021 ), Ak et al ( 2022 ), and, Huang et al ( 2022 ) have explored deep learning-based algorithms. However, more recent BCI studies have implemented other deep learning modalities including,…”

Section: Utilizing Deep Learning In Eeg-based Bcimentioning

confidence: 99%

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Status of deep learning for EEG-based brain–computer interface applications

Hossain

Islam²,

Hossain³

et al. 2023

Front. Comput. Neurosci.

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In the previous decade, breakthroughs in the central nervous system bioinformatics and computational innovation have prompted significant developments in brain–computer interface (BCI), elevating it to the forefront of applied science and research. BCI revitalization enables neurorehabilitation strategies for physically disabled patients (e.g., disabled patients and hemiplegia) and patients with brain injury (e.g., patients with stroke). Different methods have been developed for electroencephalogram (EEG)-based BCI applications. Due to the lack of a large set of EEG data, methods using matrix factorization and machine learning were the most popular. However, things have changed recently because a number of large, high-quality EEG datasets are now being made public and used in deep learning-based BCI applications. On the other hand, deep learning is demonstrating great prospects for solving complex relevant tasks such as motor imagery classification, epileptic seizure detection, and driver attention recognition using EEG data. Researchers are doing a lot of work on deep learning-based approaches in the BCI field right now. Moreover, there is a great demand for a study that emphasizes only deep learning models for EEG-based BCI applications. Therefore, we introduce this study to the recent proposed deep learning-based approaches in BCI using EEG data (from 2017 to 2022). The main differences, such as merits, drawbacks, and applications are introduced. Furthermore, we point out current challenges and the directions for future studies. We argue that this review study will help the EEG research community in their future research.

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Section: Utilizing Deep Learning In Eeg-based Bcimentioning

confidence: 99%

Section: Utilizing Deep Learning In Eeg-based Bcimentioning

confidence: 99%

Status of deep learning for EEG-based brain–computer interface applications

Hossain

Islam²,

Hossain³

et al. 2023

Front. Comput. Neurosci.

View full text Add to dashboard Cite

show abstract

“…As per the available literature, drivers' drowsiness detection can be classified into biological signal-based drowsiness detection [6][7][8][9][10][11], vehicle-based drowsiness detection [12][13][14][15][16], and vision-based drowsiness detection [17][18][19][20][21][22][23][24][25][26][27].…”

Section: B Prior Artmentioning

confidence: 99%

“…Wang et al [9] proposed a DDS, where sensorbased signals and facial expressions are captured from a driving simulator. Reddy et al [10] formulated a soft computing model for drowsiness detection, where Euler-Lagrangian formulation has been used to train a neural network model. Identification of eye blinks intervals with feature extraction and selection from prefrontal EEG signals have been addressed in [11] for drivers' drowsiness detection.…”

Section: B Prior Artmentioning

confidence: 99%

An Attention Deep Learning Framework Based Drowsiness Detection Model for Intelligent Transportation System

Ganguly¹,

Dey²,

Munshi³

2022

Preprint

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<p> Drivers’ drowsiness has been considered one of the prime reasons for accidents and road fatalities, causing a detrimental effect on human lives. This article aims to propose attention deep learning framework employing a “you only look once” version 3 (YOLOv3) network for drivers’ drowsiness detection based on a convolutional neural network module. An attention YOLOv3 framework has been modeled for detecting drivers’ eye regions followed by eye state classification and interpretation. A low-power consumer imaging system based on OV5647, 1080P Infrared-Cut camera embedded in a Raspberry Pi has been employed for capturing both day and night mode images. Feature extraction has been carried out via Darknet-53 module followed by a detection module comprising upsampling layers and multiple convolutional operations. Finally, multiscale fusion along with the non-maximum suppression method has been applied to detect and classify the eye region. Moreover, the eye region has been interpreted via a classification activation map using the proposed attention module. Experimental evaluations reveal the efficacy of the proposed framework on our acquired dataset and benchmark datasets. The proposed object detection and classification module is a generic one that can possess good potential by increasing safety in an intelligent transportation system. </p>

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“…They demonstrated that their method could accurately differentiate the fatigue state from an alert state with high stability. Reddy et al [35] proposed the spatio-spectral optimized fuzzy-independent phase-locking value representation in EEG signals for monitoring user's cognitive states. They analyzed car drivers' EEG synchronization changes as users drift between alert and drowsy states.…”

Section: Introductionmentioning

confidence: 99%

Autonomous System for EEG-Based Multiple Abnormal Mental States Classification Using Hybrid Deep Neural Networks Under Flight Environment

Lee

Jeong

et al. 2023

IEEE Trans. Syst. Man Cybern, Syst.

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Detection of the pilots' mental states is particularly critical because their abnormal mental states (AbSs) could cause catastrophic accidents. In this study, we presented the feasibility of classifying the various specific AbSs (namely, low fatigue, high fatigue, low workload, high workload, low distraction, and high distraction) by applying the deep learning method. To the best of our knowledge, this study is the first attempt to classify multiple AbSs of pilots. We proposed the hybrid deep neural networks with five convolutional blocks and two long short-term memory layers for decoding multiple AbSs. We designed the model to extract the informative features from electroencephalography signals. A total of ten pilots conducted the experiment in a simulated flight environment. Compared with five conventional models, our proposed model achieved the highest grand-average accuracy of 68.04(±5.26)% which is at least 6.55% higher than other conventional models for classifying seven mental states across all subjects. Our proposed model could distinguish and classify low and high levels for each status category and give appropriate feedback to the subjects. In addition, we found nine indicators that showed the statistically significant differences between two mental states (p < 0.05). Hence, we believe that it will contribute significantly to autonomous driving or autopilot advances based on artificial intelligence technology in the future.

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EEG-Based Drowsiness Detection With Fuzzy Independent Phase-Locking Value Representations Using Lagrangian-Based Deep Neural Networks

Cited by 16 publications

References 57 publications

Status of deep learning for EEG-based brain–computer interface applications

Status of deep learning for EEG-based brain–computer interface applications

An Attention Deep Learning Framework Based Drowsiness Detection Model for Intelligent Transportation System

Autonomous System for EEG-Based Multiple Abnormal Mental States Classification Using Hybrid Deep Neural Networks Under Flight Environment

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