Detecting driver mental fatigue based on Electroencephalogram (EEG) signals during simulated driving

In this work, we developed a novel system to detect the braking intention of drivers in emergency situations using electroencephalogram (EEG) signals. The system acquired eight-channel EEG and motion-sensing data from a custom-designed EEG headset during simulated driving. A novel method for accurately labeling the training data during an extremely short period after the onset of an emergency stimulus was introduced. Two types of features, including EEG band power-based and autoregressive (AR)-based, were investigated. It turned out that the AR-based feature in combination with artificial neural network classifier provided better detection accuracy of the system. Experimental results for ten subjects indicated that the proposed system could detect the emergency braking intention approximately 600 ms before the onset of the executed braking event, with high accuracy of 91%. Thus, the proposed system demonstrated the feasibility of developing a brain-controlled vehicle for real-world applications.

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Study of Driving Fatigue Alleviation by Transcutaneous Acupoints Electrical Stimulations

The Scientific World JOURNAL ◽

10.1155/2014/450249 ◽

2014 ◽

Vol 2014 ◽

pp. 1-8 ◽

Cited By ~ 3

Author(s):

Fuwang Wang ◽

Hong Wang

Keyword(s):

Electrical Stimulation ◽

Human Body ◽

Wavelet Packet ◽

Mental Fatigue ◽

Wavelet Packet Decomposition ◽

Eeg Signals ◽

Stimulation Method ◽

Driving Fatigue ◽

Fatigue Detection ◽

Electroencephalogram Eeg

Driving fatigue is more likely to bring serious safety trouble to traffic. Therefore, accurately and rapidly detecting driving fatigue state and alleviating fatigue are particularly important. In the present work, the electrical stimulation method stimulating the Láogóng point (劳宫PC8) of human body is proposed, which is used to alleviate the mental fatigue of drivers. The wavelet packet decomposition (WPD) is used to extractθ,α, andβsubbands of drivers’ electroencephalogram (EEG) signals. Performances of the two algorithms (θ+α)/(α+β) andθ/βare also assessed as possible indicators for fatigue detection. Finally, the differences between the drivers with electrical stimulation and normal driving are discussed. It is shown that stimulating the Láogóng point (劳宫PC8) using electrical stimulation method can alleviate driver fatigue effectively during longtime driving.

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Characteristic Extraction of Fatigue Driver's EEG Signals Based on Wavelet Entropy

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.779-780.1019 ◽

2013 ◽

Vol 779-780 ◽

pp. 1019-1022

Author(s):

Ning Ning Zhang ◽

Qiang Zhang

Keyword(s):

Wavelet Transform ◽

Brain Function ◽

Eeg Signals ◽

Transform Method ◽

Wavelet Entropy ◽

Simulated Driving ◽

Characteristic Extraction ◽

Eeg Data ◽

Significant Difference ◽

Electroencephalogram Eeg

This study aims to develop a method to detect drivers fatigue using the EEG signals. Experiments have been designed to test the subjects under simulated driving and actual driving, and the fatigue drivers Electroencephalogram (EEG) signals were collected. Wavelet transform method was applied to de-noise the raw EEG data. The H, R (H=α/β; R= (α+θ)/β) wavelet entropy were calculated. The results show that the fatigue drivers H, R wavelet entropy decreased after rest (P<0.05). It is concluded that there are significant difference in brain function between fatigue states and recovered after rest. It is shown that H, R wavelet entropy is an effective eigenvalue to measure drivers fatigue.

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Stochastic Multichannel Ranking with Brain Dynamics Preferences

Neural Computation ◽

10.1162/neco_a_01293 ◽

2020 ◽

Vol 32 (8) ◽

pp. 1499-1530

Author(s):

Yuangang Pan ◽

Ivor W. Tsang ◽

Avinash K. Singh ◽

Chin-Teng Lin ◽

Masashi Sugiyama

Keyword(s):

Large Scale ◽

Mental Fatigue ◽

Brain Dynamics ◽

Cognitive State ◽

Eeg Signals ◽

Ranking Problem ◽

Eeg Data ◽

Noisy Measurement ◽

Electroencephalogram Eeg ◽

Single Regression

A driver's cognitive state of mental fatigue significantly affects his or her driving performance and more important, public safety. Previous studies have leveraged reaction time (RT) as the metric for mental fatigue and aim at estimating the exact value of RT using electroencephalogram (EEG) signals within a regression model. However, due to the easily corrupted and also nonsmooth properties of RTs during data collection, methods focusing on predicting the exact value of a noisy measurement, RT generally suffer from poor generalization performance. Considering that human RT is the reflection of brain dynamics preference (BDP) rather than a single regression output of EEG signals, we propose a novel channel-reliability-aware ranking (CArank) model for the multichannel ranking problem. CArank learns from BDPs using EEG data robustly and aims at preserving the ordering corresponding to RTs. In particular, we introduce a transition matrix to characterize the reliability of each channel used in the EEG data, which helps in learning with BDPs only from informative EEG channels. To handle large-scale EEG signals, we propose a stochastic-generalized expectation maximum (SGEM) algorithm to update CArank in an online fashion. Comprehensive empirical analysis on EEG signals from 40 participants shows that our CArank achieves substantial improvements in reliability while simultaneously detecting noisy or less informative EEG channels.

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EEG Emotion Classification Using an Improved SincNet-Based Deep Learning Model

Brain Sciences ◽

10.3390/brainsci9110326 ◽

2019 ◽

Vol 9 (11) ◽

pp. 326 ◽

Cited By ~ 5

Author(s):

Hong Zeng ◽

Zhenhua Wu ◽

Jiaming Zhang ◽

Chen Yang ◽

Hua Zhang ◽

...

Keyword(s):

Deep Learning ◽

Speaker Recognition ◽

Classification Accuracy ◽

Deep Neural Network ◽

Signal To Noise Ratio ◽

Single Subject ◽

Emotion Classification ◽

Eeg Signals ◽

Electroencephalogram Eeg ◽

Deep Learning Model

Deep learning (DL) methods have been used increasingly widely, such as in the fields of speech and image recognition. However, how to design an appropriate DL model to accurately and efficiently classify electroencephalogram (EEG) signals is still a challenge, mainly because EEG signals are characterized by significant differences between two different subjects or vary over time within a single subject, non-stability, strong randomness, low signal-to-noise ratio. SincNet is an efficient classifier for speaker recognition, but it has some drawbacks in dealing with EEG signals classification. In this paper, we improve and propose a SincNet-based classifier, SincNet-R, which consists of three convolutional layers, and three deep neural network (DNN) layers. We then make use of SincNet-R to test the classification accuracy and robustness by emotional EEG signals. The comparable results with original SincNet model and other traditional classifiers such as CNN, LSTM and SVM, show that our proposed SincNet-R model has higher classification accuracy and better algorithm robustness.

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AGE-BASED VARIATIONS OF FRACTAL STRUCTURE OF EEG SIGNAL IN PATIENTS WITH EPILEPSY

Fractals ◽

10.1142/s0218348x18500512 ◽

2018 ◽

Vol 26 (04) ◽

pp. 1850051 ◽

Cited By ~ 32

Author(s):

HAMIDREZA NAMAZI ◽

SAJAD JAFARI

Keyword(s):

Fractal Dimension ◽

Brain Research ◽

Age Groups ◽

Eeg Signal ◽

Brain Disorders ◽

Eeg Signals ◽

Important Challenge ◽

Patients With Epilepsy ◽

Process Complexity ◽

Electroencephalogram Eeg

It is known that aging affects neuroplasticity. On the other hand, neuroplasticity can be studied by analyzing the electroencephalogram (EEG) signal. An important challenge in brain research is to study the variations of neuroplasticity during aging for patients suffering from epilepsy. This study investigates the variations of the complexity of EEG signal during aging for patients with epilepsy. For this purpose, we employed fractal dimension as an indicator of process complexity. We classified the subjects in different age groups and computed the fractal dimension of their EEG signals. Our investigations showed that as patients get older, their EEG signal will be more complex. The method of investigation that has been used in this study can be further employed to study the variations of EEG signal in case of other brain disorders during aging.

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Time-ResNeXt for epilepsy recognition based on EEG signals in wireless networks

EURASIP Journal on Wireless Communications and Networking ◽

10.1186/s13638-020-01810-5 ◽

2020 ◽

Vol 2020 (1) ◽

Cited By ~ 1

Author(s):

Shaoqiang Wang ◽

Shudong Wang ◽

Song Zhang ◽

Yifan Wang

Keyword(s):

Deep Learning ◽

Network Structure ◽

Signal Recognition ◽

Eeg Signals ◽

Real World Data ◽

Data Set ◽

Practical Applications ◽

Epilepsy Diagnosis ◽

Single Data ◽

Electroencephalogram Eeg

Abstract To automatically detect dynamic EEG signals to reduce the time cost of epilepsy diagnosis. In the signal recognition of electroencephalogram (EEG) of epilepsy, traditional machine learning and statistical methods require manual feature labeling engineering in order to show excellent results on a single data set. And the artificially selected features may carry a bias, and cannot guarantee the validity and expansibility in real-world data. In practical applications, deep learning methods can release people from feature engineering to a certain extent. As long as the focus is on the expansion of data quality and quantity, the algorithm model can learn automatically to get better improvements. In addition, the deep learning method can also extract many features that are difficult for humans to perceive, thereby making the algorithm more robust. Based on the design idea of ResNeXt deep neural network, this paper designs a Time-ResNeXt network structure suitable for time series EEG epilepsy detection to identify EEG signals. The accuracy rate of Time-ResNeXt in the detection of EEG epilepsy can reach 91.50%. The Time-ResNeXt network structure produces extremely advanced performance on the benchmark dataset (Berne-Barcelona dataset) and has great potential for improving clinical practice.

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Information-based analysis of the coupling between brain and heart reactions to olfactory stimulation

Technology and Health Care ◽

10.3233/thc-213136 ◽

2021 ◽

pp. 1-11

Author(s):

Najmeh Pakniyat ◽

Mohammad Hossein Babini ◽

Vladimir V. Kulish ◽

Hamidreza Namazi

Keyword(s):

Time Series ◽

Biomedical Science ◽

Strongly Correlated ◽

Eeg Signals ◽

Ecg Signals ◽

Olfactory Stimuli ◽

Electrocardiogram Ecg ◽

First Time ◽

Electroencephalogram Eeg ◽

The Brain

BACKGROUND: Analysis of the heart activity is one of the important areas of research in biomedical science and engineering. For this purpose, scientists analyze the activity of the heart in various conditions. Since the brain controls the heart’s activity, a relationship should exist among their activities. OBJECTIVE: In this research, for the first time the coupling between heart and brain activities was analyzed by information-based analysis. METHODS: Considering Shannon entropy as the indicator of the information of a system, we recorded electroencephalogram (EEG) and electrocardiogram (ECG) signals of 13 participants (7 M, 6 F, 18–22 years old) in different external stimulations (using pineapple, banana, vanilla, and lemon flavors as olfactory stimuli) and evaluated how the information of EEG signals and R-R time series (as heart rate variability (HRV)) are linked. RESULTS: The results indicate that the changes in the information of the R-R time series and EEG signals are strongly correlated (ρ=-0.9566). CONCLUSION: We conclude that heart and brain activities are related.

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Context-Based Filtering for Assisted Brain-Actuated Wheelchair Driving

Computational Intelligence and Neuroscience ◽

10.1155/2007/25130 ◽

2007 ◽

Vol 2007 ◽

pp. 1-12 ◽

Cited By ~ 72

Author(s):

Gerolf Vanacker ◽

José del R. Millán ◽

Eileen Lew ◽

Pierre W. Ferrez ◽

Ferran Galán Moles ◽

...

Keyword(s):

Control System ◽

Shared Control ◽

Eeg Signals ◽

Intelligent Processing ◽

Intelligent Wheelchair ◽

Brain Signals ◽

Brain Interface ◽

The Subject ◽

Electroencephalogram Eeg ◽

The Brain

Controlling a robotic device by using human brain signals is an interesting and challenging task. The device may be complicated to control and the nonstationary nature of the brain signals provides for a rather unstable input. With the use of intelligent processing algorithms adapted to the task at hand, however, the performance can be increased. This paper introduces a shared control system that helps the subject in driving an intelligent wheelchair with a noninvasive brain interface. The subject's steering intentions are estimated from electroencephalogram (EEG) signals and passed through to the shared control system before being sent to the wheelchair motors. Experimental results show a possibility for significant improvement in the overall driving performance when using the shared control system compared to driving without it. These results have been obtained with 2 healthy subjects during their first day of training with the brain-actuated wheelchair.

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An Improved Optimization Algorithm for Epileptic Seizure Detection in EEG Signals Using Random Forest Classifier

Webology ◽

10.14704/web/v18si04/web18132 ◽

2021 ◽

Vol 18 (Special Issue 04) ◽

pp. 327-340

Author(s):

A. Phraeson Gini ◽

Dr.M.P. Flower Queen

Keyword(s):

Random Forest ◽

Epileptic Seizure ◽

Seizure Detection ◽

True Positive Rate ◽

Intrinsic Mode Functions ◽

Selection Algorithm ◽

Eeg Signals ◽

Efficient Detection ◽

Positive Rate ◽

Electroencephalogram Eeg

Epilepsy is a psychiatric condition that has serious consequences for the human brain. The Electroencephalogram (EEG) may reveal a pattern that tells physicians whether an epileptic seizure is likely to occur again. EEG testing may also help the physician exclude other conditions that mimic epilepsy as a reason for the seizure. Now-a-days the researchers are showing much interest in these seizure detection because of its significance in epileptic detection. This paper is addressing an efficient soft computing framework for seizure detection from the EEG signal. The proposed pipeline of work is having the state-of-art as the possibility of achieving the maximum accuracy. The spectral features extracted from the Intrinsic mode functions (IMF) of EEG samples and it is directing the proposed flow towards the efficient detection of seizure and also the random forest algorithm based a convulsion classification is reliable for because of its learning behavior from the huge number of known dataset. The feature selection algorithm in this proposed work is stimulating the overall work towards the maximum true positive rate. This work is implemented on MATLAB platform and dataset were downloaded from the universal database such as Bonn university database. The results obtained from the proposed approach is showing the truthfulness of the approach introduced here.

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