Developing a Deep Neural Network for Driver Fatigue Detection Using EEG Signals Based on Compressed Sensing

Sheykhivand, Sobhan; Rezaii, Tohid Yousefi; Meshgini, Saeed; Makoui, Somaye; Farzamnia, Ali

doi:10.3390/su14052941

Cited by 31 publications

(24 citation statements)

References 53 publications

(64 reference statements)

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“…DCNN learns in two stages: feed forward and reverse propagation. In general, DCNN is composed of three major layers: convolutional, pooling, and connected layers [16]. The output of a convolutional layer is referred to as feature mapping.…”

Section: Brief Description Of Dcnn Modelmentioning

confidence: 99%

“…Since the discovery of the ReLU activation function, which is presently the most often used activation unit, DNNs have come a long way. The ReLU activation function overcomes the gradient removal problem while simultaneously boosting learning performance [16]. A loss function is used in the prediction stage of DNN models to learn the error ratio.…”

Section: Brief Description Of Dcnn Modelmentioning

confidence: 99%

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Acute Leukemia Diagnosis Based on Images of Lymphocytes and Monocytes Using Type-II Fuzzy Deep Network

et al. 2023

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A cancer diagnosis is one of the most difficult medical challenges. Leukemia is a type of cancer that affects the bone marrow and/or blood and accounts for approximately 8% of all cancers. Understanding the epidemiology and trends of leukemia is critical for planning. Specialists diagnose leukemia using morphological analysis, but there is a possibility of error in diagnosis. Since leukemia is so difficult to diagnose, intelligent methods of diagnosis are required. The primary goal of this study is to develop a novel method for extracting features hierarchically and accurately, in order to diagnose various types of acute leukemia. This method distinguishes between acute leukemia types, namely Acute Lymphocytic Leukemia (ALL) and Acute Myeloid Leukemia (AML), by distinguishing lymphocytes from monocytes. The images used in this study are obtained from the Shahid Ghazi Tabatabai Oncology Center in Tabriz. A type-II fuzzy deep network is designed for this purpose. The proposed model has an accuracy of 98.8% and an F1-score of 98.9%, respectively. The results show that the proposed method has a high diagnostic performance. Furthermore, the proposed method has the ability to generalize more satisfactorily and has a stronger learning performance than other methods.

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Section: Brief Description Of Dcnn Modelmentioning

confidence: 99%

Section: Brief Description Of Dcnn Modelmentioning

confidence: 99%

Acute Leukemia Diagnosis Based on Images of Lymphocytes and Monocytes Using Type-II Fuzzy Deep Network

et al. 2023

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“…It depended on the experience and expertise of the expert, which indicated the need to design automatic detection of driver fatigue systems based on EEG signals. Two techniques were used to confirm fatigue: 1. decreasing performance, such as rising crash rates and highway deviations, and 2. the Chalder fatigue [30] and the Lee fatigue scales [31]. These questionnaires included the preceding questions: Is it necessary for you to rest?…”

Section: Acquisition Of Eeg Datamentioning

confidence: 99%

“…Optimization findings were employed to update hyper-parameters. The loss function is used in machine learning algorithms to evaluate and describe model efficiency [31,35]. Generally, CNNs employ the cross-entropy loss function, which is described as follows [35,39]:…”

Section: An Overview Of the Deep Convolutional Neural Network (Cnn)mentioning

confidence: 99%

Automatic Detection of Driver Fatigue Based on EEG Signals Using a Developed Deep Neural Network

et al. 2022

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In recent years, detecting driver fatigue has been a significant practical necessity and issue. Even though several investigations have been undertaken to examine driver fatigue, there are relatively few standard datasets on identifying driver fatigue. For earlier investigations, conventional methods relying on manual characteristics were utilized to assess driver fatigue. In any case study, such approaches need previous information for feature extraction, which could raise computing complexity. The current work proposes a driver fatigue detection system, which is a fundamental necessity to minimize road accidents. Data from 11 people are gathered for this purpose, resulting in a comprehensive dataset. The dataset is prepared in accordance with previously published criteria. A deep convolutional neural network–long short-time memory (CNN–LSTM) network is conceived and evolved to extract characteristics from raw EEG data corresponding to the six active areas A, B, C, D, E (based on a single channel), and F. The study’s findings reveal that the suggested deep CNN–LSTM network could learn features hierarchically from raw EEG data and attain a greater precision rate than previous comparative approaches for two-stage driver fatigue categorization. The suggested approach may be utilized to construct automatic fatigue detection systems because of their precision and high speed.

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“…However, they are emotionally blind and cannot understand human emotional states [1]. Reading and understanding human emotional states can maximize human-computer interaction (HCI) performance [2]. Therefore, the exchange of this information and the recognition of the user's affective states are considered necessary to increase human-computer interaction [3].…”

Section: Introductionmentioning

confidence: 99%

Customized 2D CNN Model for the Automatic Emotion Recognition Based on EEG Signals

et al. 2023

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Automatic emotion recognition from electroencephalogram (EEG) signals can be considered as the main component of brain–computer interface (BCI) systems. In the previous years, many researchers in this direction have presented various algorithms for the automatic classification of emotions from EEG signals, and they have achieved promising results; however, lack of stability, high error, and low accuracy are still considered as the central gaps in this research. For this purpose, obtaining a model with the precondition of stability, high accuracy, and low error is considered essential for the automatic classification of emotions. In this research, a model based on Deep Convolutional Neural Networks (DCNNs) is presented, which can classify three positive, negative, and neutral emotions from EEG signals based on musical stimuli with high reliability. For this purpose, a comprehensive database of EEG signals has been collected while volunteers were listening to positive and negative music in order to stimulate the emotional state. The architecture of the proposed model consists of a combination of six convolutional layers and two fully connected layers. In this research, different feature learning and hand-crafted feature selection/extraction algorithms were investigated and compared with each other in order to classify emotions. The proposed model for the classification of two classes (positive and negative) and three classes (positive, neutral, and negative) of emotions had 98% and 96% accuracy, respectively, which is very promising compared with the results of previous research. In order to evaluate more fully, the proposed model was also investigated in noisy environments; with a wide range of different SNRs, the classification accuracy was still greater than 90%. Due to the high performance of the proposed model, it can be used in brain–computer user environments.

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Developing a Deep Neural Network for Driver Fatigue Detection Using EEG Signals Based on Compressed Sensing

Cited by 31 publications

References 53 publications

Acute Leukemia Diagnosis Based on Images of Lymphocytes and Monocytes Using Type-II Fuzzy Deep Network

Acute Leukemia Diagnosis Based on Images of Lymphocytes and Monocytes Using Type-II Fuzzy Deep Network

Automatic Detection of Driver Fatigue Based on EEG Signals Using a Developed Deep Neural Network

Customized 2D CNN Model for the Automatic Emotion Recognition Based on EEG Signals

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