A novel convolutional neural network method for subject-independent driver drowsiness detection based on single-channel data and EEG alpha spindles

2021 ◽  
pp. 095441192110178
Author(s):  
Sara Houshmand ◽  
Reza Kazemi ◽  
Hamed Salmanzadeh
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Driving Simulator ◽  
Single Channel ◽  
Reference Method ◽  
Eeg Signals ◽  
Drowsiness Detection ◽  
Driver Drowsiness ◽  
Driving Scenario ◽  
Eeg Alpha

A significant number of fatal accidents are caused by drowsy drivers worldwide. Driver drowsiness detection based on electroencephalography (EEG) signals has high accuracy and is known as a reference method for evaluating drowsiness. Among brain waves, EEG alpha spindle activity is a silent feature of decreasing alertness levels. In this paper, based on the detection of EEG alpha spindles, a novel driver drowsiness detection method is presented. The EEG spindles were detected using Continuous Wavelet Transform (CWT) analysis and the Morlet function. To do so, the signal is divided into 30-s epochs, and the observer rating of drowsiness determines the drowsiness level in each epoch. Tests were conducted on 17 healthy males in a driving simulator with a monotonous driving scenario. The Convolutional Neural Network (CNN) is used for classifying EEG signals and automatically learns features of the early drowsy state. The subject-independent classification results for single-channel P4 show 94% accuracy.

scholarly journals Convolutional Neural Network for Drowsiness Detection Using EEG Signals

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1734
Author(s):  
Siwar Chaabene ◽  
Bassem Bouaziz ◽  
Amal Boudaya ◽  
Anita Hökelmann ◽  
Achraf Ammar ◽  
...  
Keyword(s):  
Neural Network ◽  
Data Acquisition ◽  
Convolutional Neural Network ◽  
Data Augmentation ◽  
Model Analysis ◽  
Biomedical Signal Processing ◽  
Eeg Signals ◽  
Drowsiness Detection ◽  
Active Research ◽  
Key Steps

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.

scholarly journals A Multi-scale Residual Convolutional Neural Network for Sleep Staging Based on Single Channel Electroencephalography Signal

2021 ◽  
Author(s):  
QINGHUA ZHONG ◽  
Haibo Lei ◽  
Qianru Chen ◽  
Guofu Zhou
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Sleep Disorder ◽  
Eye Movement ◽  
Cross Validation ◽  
Single Channel ◽  
Rapid Eye Movement Sleep ◽  
Sleep Staging ◽  
Eeg Signals ◽  
Multi Scale

Abstract Sleep disorder is a serious public health problem. Non hospital sleep monitoring system for monitoring sleep quality can effectively support the screening of sleep disorder related diseases. A new algorithm of multi-scale residual convolutional neural network (MS-RESCNN) was proposed to discover the feature of electroencephalography (EEG) signals detected with wearable system and staging the sleep stage. EEG signals were analyzed by this algorithm every 30 seconds, and then sleep staging results of wake-up (W), rapid eye movement sleep (REM) and non-rapid eye movement sleep (NREM) were outputed. NREM can also be subdivided into N1, N2 and N3 stages. 5-fold cross validation and independent subject cross validation were performed on the dataset with Kappa cofficients 0.7360 and 0.7001, respectively. The accuracy rates of those methods were 92.06% and 91.13%, respectively. Compared with the other methods, our proposed method can obtain the information of sleep stages from single channel EEG signals without special feature extraction. It has a good performance and can provide support for clinical application based on automatic sleep staging.

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