EEG-Based Hypo-vigilance Detection Using Convolutional Neural Network

Hypo-vigilance detection is becoming an important active research areas in the biomedical signal processing field. For this purpose, electroencephalogram (EEG) is one of the most common modalities in drowsiness and awakeness detection. In this context, we propose a new EEG classification method for detecting fatigue state. Our method makes use of a and awakeness detection. In this context, we propose a new EEG classification method for detecting fatigue state. Our method makes use of a Convolutional Neural Network (CNN) architecture. We define an experimental protocol using the Emotiv EPOC+ headset. After that, we evaluate our proposed method on a recorded and annotated dataset. The reported results demonstrate high detection accuracy (93%) and indicate that the proposed method is an efficient alternative for hypo-vigilance detection as compared with other methods.

[1]  Osmalina Nur Rahma,et al.  Drowsiness Analysis Using Common Spatial Pattern and Extreme Learning Machine Based on Electroencephalogram Signal , 2019, Journal of medical signals and sensors.

[2]  Bo Hu,et al.  Information Distances versus Entropy Metric , 2017, Entropy.

[3]  Apichart Intarapanich,et al.  Analysis of the meditation brainwave from consumer EEG device , 2015, SoutheastCon 2015.

[4]  Sangtae Ahn,et al.  Exploring Neuro-Physiological Correlates of Drivers' Mental Fatigue Caused by Sleep Deprivation Using Simultaneous EEG, ECG, and fNIRS Data , 2016, Front. Hum. Neurosci..

[5]  Riyanarto Sarno,et al.  Real Time Fatigue-Driver Detection from Electroencephalography Using Emotiv EPOC+ , 2016 .

[6]  Feng Duan,et al.  A Novel Deep Learning Approach With Data Augmentation to Classify Motor Imagery Signals , 2019, IEEE Access.

[7]  Chris Yakopcic,et al.  A State-of-the-Art Survey on Deep Learning Theory and Architectures , 2019, Electronics.

[8]  Scott A. Shappell,et al.  Simulated Sustained Flight Operations and Performance. Part 1. Effects of Fatigue , 1992 .

[9]  Jean-Yves Tourneret,et al.  Sparse signal recovery using a Bernoulli generalized Gaussian prior , 2015, 2015 23rd European Signal Processing Conference (EUSIPCO).

[10]  Sang Wook Lee,et al.  Driver Drowsiness Detection Using Condition-Adaptive Representation Learning Framework , 2019, IEEE Transactions on Intelligent Transportation Systems.

[11]  Lisa C. Thomas,et al.  Fatigue Detection in Commercial Flight Operations: Results Using Physiological Measures☆ , 2015 .

[12]  Umar Draz,et al.  Two Classes Classification Using Different Optimizers in Convolutional Neural Network , 2018, 2018 IEEE 21st International Multi-Topic Conference (INMIC).

[13]  Jason J. Jung,et al.  Deep learning for EEG data analytics: A survey , 2019, Concurr. Comput. Pract. Exp..

[14]  Onur Avci,et al.  1-D Convolutional Neural Networks for Signal Processing Applications , 2019, ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[15]  Amit Sethi,et al.  Drowsy driver detection using representation learning , 2014, 2014 IEEE International Advance Computing Conference (IACC).

[16]  Kenneth Sundaraj,et al.  Electromyogram signal based hypovigilance detection. , 2014 .

[17]  Ping Wang,et al.  Noise Robustness Analysis of Performance for EEG-Based Driver Fatigue Detection Using Different Entropy Feature Sets , 2017, Entropy.

[18]  Nabil Benoudjit,et al.  Automatic Microemboli Characterization Using Convolutional Neural Networks and Radio Frequency Signals , 2018, 2018 International Conference on Communications and Electrical Engineering (ICCEE).

[19]  Hong Wang,et al.  Real-Time ECG-Based Detection of Fatigue Driving Using Sample Entropy , 2018, Entropy.

[20]  Jean-Yves Tourneret,et al.  Hybrid sparse regularization for magnetic resonance spectroscopy , 2013, 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[21]  K. Thammi Reddy,et al.  Optimization of Deep Learning using Various Optimizers , Loss Functions and Dropout , 2019 .

[22]  Martin Strmiska,et al.  Analysis of Performance Metrics Using Emotiv EPOC , 2018 .

[23]  Dimas Anton Asfani,et al.  Classification of driver fatigue state based on EEG using Emotiv EPOC , 2016 .

[24]  A B Shahriman,et al.  Muscle Fatigue Detections During Arm Movement using EMG Signal , 2019, IOP Conference Series: Materials Science and Engineering.

[25]  L. Chaâri,et al.  Covid-19 pandemic by the “real-time” monitoring: the Tunisian case and lessons for global epidemics in the context of 3PM strategies , 2020, EPMA Journal.