Enhanced Drowsiness Detection Using Deep Learning: An fNIRS Study

Tanveer, M.; Khan, Muhammad Jawad; Qureshi, Mohsin; Naseer, Noman; Hong, Keum‐Shik

doi:10.1109/access.2019.2942838

Cited by 78 publications

(55 citation statements)

References 68 publications

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“…The strength of CNN lies in its self-feature extracting mechanism, which makes it not only powerful but also a preferable choice over the ML algorithms. CNN can independently be used as a full-fledged classifier (feature extraction plus classification) or as a feature extractor with ML classifiers (Tanveer et al, 2019;Zhang et al, 2019). The latter method is to use convolution layers as feature extractors, and acquired features from any fully connected layer are used by ML classifiers like SVM or k-NN for classification.…”

Section: Discussionmentioning

confidence: 99%

“…The latter method is to use convolution layers as feature extractors, and acquired features from any fully connected layer are used by ML classifiers like SVM or k-NN for classification. This approach has recently been used in fNIRS BCI study (Tanveer et al, 2019) where brain heat maps are used as datasets. In this approach, the training time and computational resources required to train the CNN model increase many folds because time-series data correspond to a single vector, and the images are 2-D and 3-D matrices (2-D in case of gray scale and 3-D in case of RGB image).…”

Section: Discussionmentioning

confidence: 99%

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Enhanced Accuracy for Multiclass Mental Workload Detection Using Long Short-Term Memory for Brain–Computer Interface

et al. 2020

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Cognitive workload is one of the widely invoked human factors in the areas of humanmachine interaction (HMI) and neuroergonomics. The precise assessment of cognitive and mental workload (MWL) is vital and requires accurate neuroimaging to monitor and evaluate the cognitive states of the brain. In this study, we have decoded four classes of MWL using long short-term memory (LSTM) with 89.31% average accuracy for brain-computer interface (BCI). The brain activity signals are acquired using functional near-infrared spectroscopy (fNIRS) from the prefrontal cortex (PFC) region of the brain. We performed a supervised MWL experimentation with four varying MWL levels on 15 participants (both male and female) and 10 trials of each MWL per participant. Realtime four-level MWL states are assessed using fNIRS system, and initial classification is performed using three strong machine learning (ML) techniques, support vector machine (SVM), k-nearest neighbor (k-NN), and artificial neural network (ANN) with obtained average accuracies of 54.33, 54.31, and 69.36%, respectively. In this study, novel deep learning (DL) frameworks are proposed, which utilizes convolutional neural network (CNN) and LSTM with 87.45 and 89.31% average accuracies, respectively, to solve high-dimensional four-level cognitive states classification problem. Statistical analysis, t-test, and one-way F-test (ANOVA) are also performed on accuracies obtained through ML and DL algorithms. Results show that the proposed DL (LSTM and CNN) algorithms significantly improve classification performance as compared with ML (SVM, ANN, and k-NN) algorithms.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Enhanced Accuracy for Multiclass Mental Workload Detection Using Long Short-Term Memory for Brain–Computer Interface

et al. 2020

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“…Deep learning (DL) has allowed significant progress in the identification and classification of image patterns and is considered a promising machine-learning methodology (Ravi et al, 2017). Convolutional neural networks (CNNs), the most broadly used DL architecture, have delivered excellent performances in computer-aided prediction for neurological disorders (Mamoshina et al, 2016;Tanveer et al, 2019). The great success of CNNs in neural-image classification and analysis, which evidences their strong image-classification ability (Cecotti and Gräser, 2011;Ieracitano et al, 2018;Lin et al, 2018;Waytowich et al, 2018;Oh et al, 2019), motivated us to develop a CNN-based classification method for early-stage AD detection.…”

Section: Introductionmentioning

confidence: 99%

Detection of Mild Cognitive Impairment Using Convolutional Neural Network: Temporal-Feature Maps of Functional Near-Infrared Spectroscopy

Yang

Huang

Yoo

et al. 2020

Front. Aging Neurosci.

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Mild cognitive impairment (MCI) is the clinical precursor of Alzheimer's disease (AD), which is considered the most common neurodegenerative disease in the elderly. Some MCI patients tend to remain stable over time and do not evolve to AD. It is essential to diagnose MCI in its early stages and provide timely treatment to the patient. In this study, we propose a neuroimaging approach to identify MCI using a deep learning method and functional near-infrared spectroscopy (fNIRS). For this purpose, fifteen MCI subjects and nine healthy controls (HCs) were asked to perform three mental tasks: N-back, Stroop, and verbal fluency (VF) tasks. Besides examining the oxygenated hemoglobin changes (HbO) in the region of interest, HbO maps at 13 specific time points (i.e.

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“…This finding is consistent with the pioneer study [ 45 ], which could achieve 100% by conducting the task with a 20 s time window. With the development of deep learning, an improved classification may be achieved by utilizing the hybrid modality (i.e., EEG and fNIRS) [ 49 , 50 ] advanced machine learning algorithms, such as long-short team memory [ 51 ] and deep neural network [ 52 ]. In addition, in this study, we applied red squares with the text indicators to guide the participants to select the corresponding control commands.…”

Section: Discussionmentioning

confidence: 99%

A Bipolar-Channel Hybrid Brain-Computer Interface System for Home Automation Control Utilizing Steady-State Visually Evoked Potential and Eye-Blink Signals

Yang

Nguyen

2020

Sensors

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The goal of this study was to develop and validate a hybrid brain-computer interface (BCI) system for home automation control. Over the past decade, BCIs represent a promising possibility in the field of medical (e.g., neuronal rehabilitation), educational, mind reading, and remote communication. However, BCI is still difficult to use in daily life because of the challenges of the unfriendly head device, lower classification accuracy, high cost, and complex operation. In this study, we propose a hybrid BCI system for home automation control with two brain signals acquiring electrodes and simple tasks, which only requires the subject to focus on the stimulus and eye blink. The stimulus is utilized to select commands by generating steady-state visually evoked potential (SSVEP). The single eye blinks (i.e., confirm the selection) and double eye blinks (i.e., deny and re-selection) are employed to calibrate the SSVEP command. Besides that, the short-time Fourier transform and convolution neural network algorithms are utilized for feature extraction and classification, respectively. The results show that the proposed system could provide 38 control commands with a 2 s time window and a good accuracy (i.e., 96.92%) using one bipolar electroencephalogram (EEG) channel. This work presents a novel BCI approach for the home automation application based on SSVEP and eye blink signals, which could be useful for the disabled. In addition, the provided strategy of this study—a friendly channel configuration (i.e., one bipolar EEG channel), high accuracy, multiple commands, and short response time—might also offer a reference for the other BCI controlled applications.

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Enhanced Drowsiness Detection Using Deep Learning: An fNIRS Study

Cited by 78 publications

References 68 publications

Enhanced Accuracy for Multiclass Mental Workload Detection Using Long Short-Term Memory for Brain–Computer Interface

Enhanced Accuracy for Multiclass Mental Workload Detection Using Long Short-Term Memory for Brain–Computer Interface

Detection of Mild Cognitive Impairment Using Convolutional Neural Network: Temporal-Feature Maps of Functional Near-Infrared Spectroscopy

A Bipolar-Channel Hybrid Brain-Computer Interface System for Home Automation Control Utilizing Steady-State Visually Evoked Potential and Eye-Blink Signals

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