scholarly journals Application of music in relief of driving fatigue based on EEG signals

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Qingjun Wang ◽  
Zhendong Mu
Keyword(s):  
Traffic Accidents ◽  
Signal Acquisition ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Driving Time ◽  
Mode Decomposition ◽  
Depth Analysis ◽  
Driving Fatigue ◽  
Highly Correlated ◽  
Fatigue Driving

AbstractIn order to solve the problem of traffic accidents caused by fatigue driving, the research of EEG signals is particularly important, which can timely and accurately determine the fatigue state and take corresponding measures. Effective fatigue improvement measures are an important research topic in the current scientific field. The purpose of this article is to use EEG signals to analyze fatigue driving and prevent the dangers and injuries caused by fatigue driving. We designed the electroencephalogram (EEG) signal acquisition model to collect the EEG signal of the experimenter, and then removed the noise through the algorithm of Variational Mode Decomposition (VMD) and independent component analysis (ICA). On the basis of in-depth analysis and full understanding, we learned about the EEG signal of the driver at different driving times and different landscape roads, and provided some references for the study of music in relieving driving fatigue. The results of the study show that in the presence of music, the driver can keep the EEG signal active for more than 2 h, while in the absence of music, the driver’s EEG signal is active for about 1.5 h. Under different road conditions, the driver’s EEG signal activity is not consistent. The β wave and (α + θ)/β ratio of the driver in mountainous roads and grassland road landscape environments are highly correlated with driving time, and β wave is negatively correlated with driving time, and (α + θ)/β is positively correlated with driving time. In addition, the accumulation of changes in the two indicators is also strongly correlated with driving time.

scholarly journals Brain Complex Network Characteristic Analysis of Fatigue during Simulated Driving Based on Electroencephalogram Signals

Entropy ◽  
2019 ◽  
Vol 21 (4) ◽  
pp. 353 ◽  
Author(s):  
Chunxiao Han ◽  
Xiaozhou Sun ◽  
Yaru Yang ◽  
Yanqiu Che ◽  
Yingmei Qin
Keyword(s):  
Complex Network ◽  
Traffic Accidents ◽  
Brain Regions ◽  
Eeg Signals ◽  
Characteristic Analysis ◽  
Simulated Driving ◽  
Complex Network Theory ◽  
Driving Fatigue ◽  
Delta Rhythm ◽  
Fatigue Driving

Fatigued driving is one of the major causes of traffic accidents. Frequent repetition of driving behavior for a long time may lead to driver fatigue, which is closely related to the central nervous system. In the present work, we designed a fatigue driving simulation experiment and collected the electroencephalogram (EEG) signals. Complex network theory was introduced to study the evolution of brain dynamics under different rhythms of EEG signals during several periods of the simulated driving. The results show that as the fatigue degree deepened, the functional connectivity and the clustering coefficients increased while the average shortest path length decreased for the delta rhythm. In addition, there was a significant increase of the degree centrality in partial channels on the right side of the brain for the delta rhythm. Therefore, it can be concluded that driving fatigue can cause brain complex network characteristics to change significantly for certain brain regions and certain rhythms. This exploration may provide a theoretical basis for further finding objective and effective indicators to evaluate the degree of driving fatigue and to help avoid fatigue driving.

scholarly journals Fatigue Driving Prediction on Commercial Dangerous Goods Truck Using Location Data: The Relationship between Fatigue Driving and Driving Environment

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Shifeng Niu ◽  
Guiqiang Li
Keyword(s):  
Random Forest ◽  
Traffic Accidents ◽  
Ground Truth ◽  
Predictive Performance ◽  
Location Data ◽  
Dangerous Goods ◽  
Driving Time ◽  
Road Type ◽  
Fatigue Driving ◽  
The Relationship

The approaches monitoring fatigue driving are studied because of the fact that traffic accidents caused by fatigue driving often have fatal consequences. This paper proposes a new approach to predict driving fatigue using location data of commercial dangerous goods truck (CDT) and driver’s yawn data. The proposed location data are from an existing dataset of a transportation company that was collected from 166 vehicles and drivers in an actual driving environment. Six different categories of the predictor set are considered as fatigue-related indexes including travel time, day of week, road type, continuous driving time, average velocity, and overall mileage. The driver’s yawn data are used as a proxy for ground truth for the classification algorithm. From the six different categories of the predictor set, we obtain a set of 17 predictor variables to train logistic regression, neural network, and random forest classifiers. Then, we evaluate the predictive performance of the classifiers based on three indexes: accuracy, F1-measure, and area under the ROC curve (AUROC). The results show that the random forest is more suitable for predicting fatigue driving using location data according to its best accuracy (74.18%), F1-measure (62.02%), and AUROC (0.8059). Finally, we analyze the relationship between fatigue driving and driving environment according to variable importance described by random forest. In summary, our results obviously exhibit the potential of location data for reducing the accident rate caused by fatigue driving in practice.

scholarly journals Design and Verification of a Dry Sensor-Based Multi-Channel Digital Active Circuit for Human Brain Electroencephalography Signal Acquisition Systems

Micromachines ◽  
2019 ◽  
Vol 10 (11) ◽  
pp. 720
Author(s):  
Chin-Teng Lin ◽  
Chi-Hsien Liu ◽  
Po-Sheng Wang ◽  
Jung-Tai King ◽  
Lun-De Liao
Keyword(s):  
Daily Life ◽  
Event Related Potentials ◽  
Computer Interface ◽  
Signal Acquisition ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Preparation Time ◽  
Active Circuit ◽  
Related Potentials ◽  
Common Application

A brain–computer interface (BCI) is a type of interface/communication system that can help users interact with their environments. Electroencephalography (EEG) has become the most common application of BCIs and provides a way for disabled individuals to communicate. While wet sensors are the most commonly used sensors for traditional EEG measurements, they require considerable preparation time, including the time needed to prepare the skin and to use the conductive gel. Additionally, the conductive gel dries over time, leading to degraded performance. Furthermore, requiring patients to wear wet sensors to record EEG signals is considered highly inconvenient. Here, we report a wireless 8-channel digital active-circuit EEG signal acquisition system that uses dry sensors. Active-circuit systems for EEG measurement allow people to engage in daily life while using these systems, and the advantages of these systems can be further improved by utilizing dry sensors. Moreover, the use of dry sensors can help both disabled and healthy people enjoy the convenience of BCIs in daily life. To verify the reliability of the proposed system, we designed three experiments in which we evaluated eye blinking and teeth gritting, measured alpha waves, and recorded event-related potentials (ERPs) to compare our developed system with a standard Neuroscan EEG system.

A portable real-time EEG signal acquisition and tele-medicine system

2020 ◽  
pp. 1-11
Author(s):  
Qiang Zhang ◽  
Peng Wang ◽  
Shanshan Li ◽  
Yonghao Jing
Keyword(s):  
Real Time ◽  
Sql Server ◽  
Signal Acquisition ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Eeg Data ◽  
Sql Server Database ◽  
High Power Consumption ◽  
Machine Interface ◽  
Sd Card

Since electroencephalogram (EEG) signals contain a variety of physiological and pathological information, they are widely used in medical diagnosis, brain machine interface and other fields. The existing EEG apparatus are not perfect due to big size, high power consumption and using cables to transmit data. In this paper, a portable real-time EEG signal acquisition and tele-medicine system is developed in order to improve performance of EEG apparatus. The weak EEG signals are induced to the pre-processing circuits via a noninvasive method with bipolar leads. After multi-level amplifying and filtering, these signals are transmitted to DSP (TMS320C5509) to conduct digital filtering. Then, the EEG signals are displayed on the LCD screen and stored in the SD card so that they can be uploaded to the server through the internet. The server employs SQL Server database to manage patients’ information and to store data in disk. Doctors can download, look up and analyze patients’ EEG data using the doctor client. Experimental results demonstrate that the system can acquire weak EEG signals in real time, display the processed results, save data and carry out tele-medicine. The system can meet the requirement of the EEG signals’ quality, and are easy to use and carry.

scholarly journals Epileptic Detection from the Eeg Signal using the Anti colony Optimization Technique with Deep Neural Network

2020 ◽  
Vol 9 (1) ◽  
pp. 2726-2733
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Time Series Data ◽  
Optimization Technique ◽  
Signal Decomposition ◽  
Ant Colony ◽  
Series Data ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Mode Decomposition

Extensively used technique to diagnose the epilepsy is EEG. The research objective is to check the variations of frequency found in the epileptic EEG signals.. The EEG dataset were acquired from online database of the Bonn University (BU). Then, butterworth type two filter was implemented to remove the unwanted artifacts from the acquired EEG signals. Further, Multivariate Variational Mode Decomposition (MVMD) methodology was applied to decompose the denoised EEG signals. The signal decomposition helps in finding the necessary information, which required to model the complex time series data. Then, the features were extracted from decomposed signals by using fifteen entropy, linear and statistical features. In addition, ant colony optimization technique was proposed for optimizing the extracted features. The optimized feature vectors were classified by Deep Neural Network (DNN) that includes two circumstances (seizure and healthy), and (Interictal, ictal, and normal). The accuracy attained using the ant colony with deep neural network is 98.12% using the BU EEG dataset, respectively related to the existing models.

EEG Signal Processing Based on Multivariate Empirical Mode Decomposition and Common Spatial Pattern Hybrid Algorithm

2019 ◽  
Vol 33 (09) ◽  
pp. 1959030 ◽  
Author(s):  
Linyan Wu ◽  
Tao Wang ◽  
Qi Wang ◽  
Qing Zhu ◽  
Jinhuan Chen
Keyword(s):  
Empirical Mode Decomposition ◽  
Nearest Neighbor ◽  
Classification Model ◽  
Eeg Signal ◽  
Intrinsic Mode Functions ◽  
K Nearest Neighbor ◽  
Eeg Signals ◽  
Common Spatial Pattern ◽  
Multivariate Empirical Mode Decomposition ◽  
Mode Decomposition

The high accuracy of electroencephalogram (EEG) signal classification is the premise for the wide application of brain computer interface (BCI). In this paper, a hybrid method consisting of multivariate empirical mode decomposition (MEMD) and common space pattern (CSP) is proposed to recognize left-hand and right-hand hypothetical motion from EEG signals. Experiments were carried out using the BCI competition II imagery database. EEG signals were decomposed into multiple intrinsic mode functions (IMFs) by MEMD. The IMF functions with high correlation were processed by CSP, and AR coefficients and entropy values were extracted as features. After genetic algorithm optimization, classification is carried out. Our research results show that the K nearest neighbor (KNN) as an optimal classification model produces 85.36% accuracy. We also compare the proposed algorithm with the existing algorithms. The experimental results show that the performance of the proposed algorithm is comparable to or better than that of many existing algorithms.

scholarly journals A Review of Methods of Diagnosis and Complexity Analysis of Alzheimer’s Disease Using EEG Signals

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Mahshad Ouchani ◽  
Shahriar Gharibzadeh ◽  
Mahdieh Jamshidi ◽  
Morteza Amini
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Complexity Analysis ◽  
Future Research ◽  
Signal Acquisition ◽  
Eeg Signal ◽  
Classification Methods ◽  
Clinical Markers ◽  
Eeg Signals ◽  
Key Steps

This study will concentrate on recent research on EEG signals for Alzheimer’s diagnosis, identifying and comparing key steps of EEG-based Alzheimer’s disease (AD) detection, such as EEG signal acquisition, preprocessing function extraction, and classification methods. Furthermore, highlighting general approaches, variations, and agreement in the use of EEG identified shortcomings and guidelines for multiple experimental stages ranging from demographic characteristics to outcomes monitoring for future research. Two main targets have been defined based on the article’s purpose: (1) discriminative (or detection), i.e., look for differences in EEG-based features across groups, such as MCI, moderate Alzheimer’s disease, extreme Alzheimer’s disease, other forms of dementia, and stable normal elderly controls; and (2) progression determination, i.e., look for correlations between EEG-based features and clinical markers linked to MCI-to-AD conversion and Alzheimer’s disease intensity progression. Limitations mentioned in the reviewed papers were also gathered and explored in this study, with the goal of gaining a better understanding of the problems that need to be addressed in order to advance the use of EEG in Alzheimer’s disease science.

scholarly journals Epileptic Seizures Detection based on Empirical Mode Decomposition and Hilbert-Huang transform of EEG Signal

2015 ◽  
Vol 1 (2) ◽  
pp. 295
Author(s):  
Mokhtar Mohammadi ◽  
Aso M. Darwesh
Keyword(s):  
Empirical Mode Decomposition ◽  
Epileptic Seizure ◽  
Spectral Energy ◽  
Support Vector ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Mode Decomposition

The electrical activities of brain fluctuate frequently and can be analyzed using electroencephalogram (EEG) signals. We present a new method for classification of ictal and seizure-free intracranial EEG recordings. The proposed method uses the application of multivariate empirical mode decomposition (MEMD) algorithm combines with the Hilbert transform as the Hilbert-Huang transform (HHT) and analyzing spectral energy of the intrinsic mode function of the signal. EMD uses the characteristics of signals to adaptively decompose them to several intrinsic mode functions (IMFs). Hilbert transforms (HTs) are then used to transform the IMFs into instantaneous frequencies (IFs), to obtain the signals time-frequency-energy distributions. Classification of the EEG signal that is epileptic seizure exists or not has been done using support vector machine. The algorithm was tested in 6 intracranial channels EEG records acquired in 9 patients with refractory epilepsy and validated by the Epilepsy Center of the University Hospital of Freiburg. The experimental results show that the proposed method efficiently detects the presence of epileptic seizure in EEG signals and also showed a reasonable accuracy in detection.

Analysis of Feature Fatigue EEG Signals Based on Wavelet Entropy

2018 ◽  
Vol 32 (08) ◽  
pp. 1854023 ◽  
Author(s):  
Qingjun Wang ◽  
Yibo Li ◽  
Xueping Liu
Keyword(s):  
Classification Accuracy ◽  
Fatigue Testing ◽  
Spectral Entropy ◽  
Eeg Signals ◽  
State Monitoring ◽  
Wavelet Entropy ◽  
Feature Extraction Method ◽  
Driving Fatigue ◽  
Fatigue Driving ◽  
Feature Calculation

Fatigue driving is bringing more and more serious harm, but there are various reasons for fatigue driving, it is still difficult to test the driver’s fatigue. This paper defines a method to test driver’s fatigue based on the EEG, and different from other researches into fatigue driving, this paper mainly takes the fatigue features of EEG signals in fatigue state and uses wavelet entropy as the feature extraction method to analyze the features of wavelet entropy and spectral entropy features as well as the classification accuracy under the same classifier. The SVM is used to show the classifier’s results. The accuracy of the driver fatigue state monitoring using the wavelet entropy is 90.7%, which is higher than the use of spectral entropy as the characteristic accuracy rate of 81.3%. The results show that the frequency characteristics of EEG can be well applied to driving fatigue testing, but different frequency feature calculation methods will affect the classification accuracy.

Clinical Diagnosis of Psychiatry Based on Electroencephalography

2021 ◽  
Vol 11 (3) ◽  
pp. 955-963
Author(s):  
Lixue Yuan ◽  
Yinyan Fan ◽  
Quanxi Gan ◽  
Huibin Feng
Keyword(s):  
Environmental Control ◽  
Low Cost ◽  
Training System ◽  
Brain Machine Interface ◽  
Signal Acquisition ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Feedback Information ◽  
Machine Interface ◽  
Neural Feedback

At present, neurophysiological signals used for neuro feedback are EEG (Electroencephalogram), functional magnetic resonance imaging. Among them, the acquisition of EEG signals has the advantages of non-invasive way with low cost. It has been widely used in brain-machine interface technology in recent years. Important progress has been made in rehabilitation and environmental control. However, neural feedback and brainmachine interface technology are completely similar in signal acquisition, signal feature extraction, and pattern classification. Therefore, the related research results of brain-machine interface can be used to closely cooperate with clinical needs to research and develop neural feedback technology based on EEG. Based on neurophysiology and brain-machine interface technology, this paper develops a neural feedback training system based on the acquisition and analysis of human EEG signals. Aiming at the autonomous rhythm components in the EEG signal, such as sensorimotor rhythm and alpha rhythm, the characteristic parameters are extracted through real-time EEG signal processing to generate feedback information, and the subject is self-regulated and trained from a physiological-psychological perspective by providing adjuvant treatment, a practical and stable treatment platform for the clinic.

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