Utilizing EEG and fNIRS for the detection of sleep-deprivation-induced fatigue and its inhibition using colored light stimulation

Shoaib, Zeshan; Akbar, Arbab; Kim, Eung Soo; Kamran, Muhammad Ahmad; Kim, Jun‐Hyun; Jeong, Myung Yung

doi:10.1038/s41598-023-33426-2

Cited by 10 publications

(4 citation statements)

References 75 publications

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“…Similarly, Zeshan Shoaib et al . explored brain responses during fatigued driving under different coloured light stimuli 24 . Thien Nguyen et al .…”

Section: Background and Summarymentioning

confidence: 99%

EEG and fNIRS datasets based on Stroop task during two weeks of high-altitude exposure in new immigrants

Ji,

Bao,

et al. 2024

Sci Data

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Maintaining sufficient cerebral oxygen metabolism is crucial for human survival, especially in challenging conditions such as high-altitudes. Human cognitive neural activity is sensitive to fluctuations in oxygen levels. However, there is a lack of publicly available datasets on human behavioural responses and cerebral dynamics assessments during the execution of conflicting tasks in natural hypoxic environments. We recruited 80 healthy new immigrant volunteers (males, aged 20 ± 2 years) and employed the Stroop cognitive conflict paradigm. After a two-week exposure to both high and low-altitudes, the behavioural performance, prefrontal oxygen levels, and electroencephalography (EEG) signals were recorded. Comparative analyses were conducted on the behavioural reaction times and accuracy during Stroop tasks, and statistical analyses of participants’ prefrontal oxygen levels and EEG signals were performed. We anticipate that our open-access dataset will contribute to the development of monitoring devices and algorithms, designed specifically for measuring cerebral oxygen and EEG dynamics in populations exposed to extreme environments, particularly among individuals suffering from oxygen deficiency.

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“…Similarly, Zeshan Shoaib et al . explored brain responses during fatigued driving under different coloured light stimuli 24 . Thien Nguyen et al .…”

Section: Background and Summarymentioning

confidence: 99%

EEG and fNIRS datasets based on Stroop task during two weeks of high-altitude exposure in new immigrants

Ji,

Bao,

et al. 2024

Sci Data

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“…Next, EEG signal is recorded for analysis of stress or drowsiness indicators. We wanted to address frontal areas which have been mostly associated with stress reaction [15,16]. Our study uses 8-channel EEG device, therefore we opted to place electrodes in positions in between Fp1/F3, Fp2/F4, F3/P3, F4/P4, PZ/OZ, and in CZ according to International 10-20 EEG system.…”

Section: Monitoring Physiological Signals Offers a Practical And Affo...mentioning

confidence: 99%

Evaluation of the stress reactive countermeasure effectiveness in workplace: a randomized, controlled, cross-over study

Ráčková,

Diamond,

Butin

et al. 2023

Preprint

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This protocol serves to examine the effectiveness of a workplace-based reactive stress countermeasure (e.g., an action or device intended to prevent or mitigate undesirable effects of acute stress). It’s designed specifically for the context where decrease of acute stress reaction is desired while preserving vigilance and avoiding sleepiness. The study adopts a cross-over, controlled design. The order of interventions is randomized and blinded to the data collector and data analyst. This approach ensures increased validity and reliability of research outcomes while minimizing the number of participants required. The protocol employs affordable state-of-the-art subjective indicators measured using questionnaires, as well as objective physiological indicators of arousal (heart rate, respiration rate, electrodermal activity, frontal EEG). Baseline stress levels are measured, followed by induced acute stress using the Maastricht Acute Stress Task, and subsequent sessions of either countermeasure or control. Data analysis includes descriptive, time series, and ANCOVA or general linear models approaches. The protocol is estimated to take approximately 2 to 2.5 hours for completion.

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“…Furthermore, the color, size, and other features of skin cancer types are very similar. Image processing and machine vision use for various medical imaging applications has grown tremendously in the past decade [17][18][19][20][21][22]. Using these strategies speeds up the diagnosis process and reduces human error.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Skin Lesion Detection: A Multistage Multiclass Convolutional Neural Network-Based Framework

Ali,

Khalid,

Alshanbari

et al. 2023

Bioengineering

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The early identification and treatment of various dermatological conditions depend on the detection of skin lesions. Due to advancements in computer-aided diagnosis and machine learning approaches, learning-based skin lesion analysis methods have attracted much interest recently. Employing the concept of transfer learning, this research proposes a deep convolutional neural network (CNN)-based multistage and multiclass framework to categorize seven types of skin lesions. In the first stage, a CNN model was developed to classify skin lesion images into two classes, namely benign and malignant. In the second stage, the model was then used with the transfer learning concept to further categorize benign lesions into five subcategories (melanocytic nevus, actinic keratosis, benign keratosis, dermatofibroma, and vascular) and malignant lesions into two subcategories (melanoma and basal cell carcinoma). The frozen weights of the CNN developed–trained with correlated images benefited the transfer learning using the same type of images for the subclassification of benign and malignant classes. The proposed multistage and multiclass technique improved the classification accuracy of the online ISIC2018 skin lesion dataset by up to 93.4% for benign and malignant class identification. Furthermore, a high accuracy of 96.2% was achieved for subclassification of both classes. Sensitivity, specificity, precision, and F1-score metrics further validated the effectiveness of the proposed multistage and multiclass framework. Compared to existing CNN models described in the literature, the proposed approach took less time to train and had a higher classification rate.

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Utilizing EEG and fNIRS for the detection of sleep-deprivation-induced fatigue and its inhibition using colored light stimulation

Cited by 10 publications

References 75 publications

EEG and fNIRS datasets based on Stroop task during two weeks of high-altitude exposure in new immigrants

EEG and fNIRS datasets based on Stroop task during two weeks of high-altitude exposure in new immigrants

Evaluation of the stress reactive countermeasure effectiveness in workplace: a randomized, controlled, cross-over study

Enhancing Skin Lesion Detection: A Multistage Multiclass Convolutional Neural Network-Based Framework

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