Driving Fatigue Detecting Based on EEG Signals of Forehead Area

2017 ◽  
Vol 31 (05) ◽  
pp. 1750011 ◽  
Author(s):  
Zhendong Mu ◽  
Jianfeng Hu ◽  
Jinghai Yin
Keyword(s):  
Brain Region ◽  
Classification Accuracy ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Driving Experience ◽  
Average Accuracy ◽  
Eeg Data ◽  
Driving Fatigue ◽  
Classification Tool ◽  
Fold Cross Validation

This study examined whether prefrontal brain region electroencephalography (EEG) can be used to detect driver's fatigue. The participants were 13 healthy university students with driving experience. They collected EEG experiments in a virtual driving environment, and divided the collected EEG data into normal state and fatigue state. Fuzzy entropy was used for feature extraction; SVM was used as a classification tool. FP1 and FP2 electrode EEG signal was selected from the subject's EEG signal as analysis object. When single electrode signal was used as feature, accuracy of FP1 was higher than FP2, and if mixing FP1 and FP2 as feature, the accuracy is the highest, the average accuracy is 0.85 by 10-fold cross-validation in Prefrontal brain region. Although the signal classification accuracy of the prefrontal brain region is not the highest, from a practical point, the EEG classification accuracy can be used to detect fatigue.

scholarly journals EEG data processing in ADHD diagnosis and neurofeedback

2020 ◽  
Vol 40 (3) ◽  
pp. 116-123
Author(s):  
Zoran Šverko ◽  
Ivan Markovinović ◽  
Miroslav Vrankić ◽  
Saša Vlahinić
Keyword(s):  
Data Processing ◽  
Mean Value ◽  
Adhd Group ◽  
Eeg Signal ◽  
Statistical Features ◽  
Eeg Signals ◽  
New Approach ◽  
Mean Values ◽  
Adhd Diagnosis ◽  
Eeg Data

In this paper, EEG data processing was conducted in order to define the parameters for neurofeedback. A new survey was conducted based on a brief review of previous research. Two groups of participants were chosen: ADHD (3) and nonADHD (14). The main part of this study includes EEG signal data pre-processing and processing. We have outlined statistical features of observed EEG signals such as mean value, grand-mean value and their ratios. It can be concluded that an increase in grand-mean values of power theta-low beta ratio on Cz electrode gives confirmation of previous research. The value of alpha-delta power ratio higher than 1 on C3, Cz, P3, Pz, P4 in ADHD group is proposed as a new approach to classification. Based on these conclusions we will design a neurofeedback protocol as a continuation of this work.

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 Four-classes Human Emotion Recognition Via Entropy Characteristic and Random Forest。

2020 ◽  
Vol 49 (3) ◽  
pp. 285-298
Author(s):  
Jian Zhang ◽  
Yihou Min
Keyword(s):  
Random Forest ◽  
Emotion Recognition ◽  
Classification Accuracy ◽  
Wavelet Packet ◽  
Recognition Rate ◽  
Eeg Signal ◽  
Differential Entropy ◽  
Eeg Signals ◽  
Human Emotion ◽  
Before And After

Human Emotion Recognition is of vital importance to realize human-computer interaction (HCI), while multichannel electroencephalogram (EEG) signals gradually replace other physiological signals and become the main basis of emotional recognition research with the development of brain-computer interface (BCI). However, the accuracy of emotional classification based on EEG signals under video stimulation is not stable, which may be related to the characteristics of  EEG signals before receiving stimulation. In this study, we extract the change of Differential Entropy (DE) before and after stimulation based on wavelet packet transform (WPT) to identify individual emotional state. Using the EEG emotion database DEAP, we divide the experimental EEG data in the database equally into 15 sets and extract their differential entropy on the basis of WPT. Then we calculate value of DE change of each separated EEG signal set. Finally, we divide the emotion into four categories in the two-dimensional valence-arousal emotional space by combining it with the integrated algorithm, Random Forest (RF). The simulation results show that the WPT-RF model established by this method greatly improves the recognition rate of EEG signal, with an average classification accuracy of 87.3%. In addition, we use WPT-RF model to train individual subjects, and the classification accuracy reached 97.7%.

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 Electroencephalogram of Happy Emotional Cognition Based on Complex System of Music and Image Visual and Auditory

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Lin Gan ◽  
Mu Zhang ◽  
Jiajia Jiang ◽  
Fajie Duan
Keyword(s):  
Complex System ◽  
Cognitive Neuroscience ◽  
Auditory Stimulation ◽  
External Information ◽  
Eeg Signals ◽  
Baseline Drift ◽  
Average Accuracy ◽  
Eeg Data ◽  
Electroencephalogram Eeg ◽  
The Brain

People are ingesting various information from different sense organs all the time to complete different cognitive tasks. The brain integrates and regulates this information. The two significant sensory channels for receiving external information are sight and hearing that have received extensive attention. This paper mainly studies the effect of music and visual-auditory stimulation on electroencephalogram (EEG) of happy emotion recognition based on a complex system. In the experiment, the presentation was used to prepare the experimental stimulation program, and the cognitive neuroscience experimental paradigm of EEG evoked by happy emotion pictures was established. Using 93 videos as natural stimuli, fMRI data were collected. Finally, the collected EEG signals were removed with the eye artifact and baseline drift, and the t-test was used to analyze the significant differences of different lead EEG data. Experimental data shows that, by adjusting the parameters of the convolutional neural network, the highest accuracy of the two-classification algorithm can reach 98.8%, and the average accuracy can reach 83.45%. The results show that the brain source under the combined visual and auditory stimulus is not a simple superposition of the brain source of the single visual and auditory stimulation, but a new interactive source is generated.

CLASSIFICATION OF ICTAL EEG USING MODELING BASED SPECTRAL AND TEMPORAL FEATURES ON INSTANTANEOUS AMPLITUDE-FREQUENCY COMPONENTS OF IMFs

2018 ◽  
Vol 30 (06) ◽  
pp. 1850042 ◽  
Author(s):  
K. S. Biju ◽  
M. G. Jibukumar
Keyword(s):  
Spectral Density ◽  
Power Spectral Density ◽  
Classification Accuracy ◽  
Spectral Features ◽  
Eeg Signal ◽  
Instantaneous Amplitude ◽  
Eeg Signals ◽  
Hilbert Huang Transform ◽  
Temporal Features ◽  
Power Spectral

In the present study, a method for classifying the different ictal stages in electroencephalogram (EEG) signals is proposed. The main symptoms of epilepsy are indicated by ictal activities, which trigger widespread neurological disorders other than stroke and thus affect the world population. In this work, a novel ictal classification method that combines the spectral and temporal features of twin components in Hilbert–Huang transform is proposed. Spectral features of instantaneous amplitude (IA) function are obtained based on the power spectral density of autoregressive (AR) modeling. Here four different cases of ictal activities of EEG signal are classified. In each case first and second intrinsic mode function of Hilbert–Huang transform are tabulated. The power spectral density of AR(6) and AR(10) model are done for IA1 and IA2 components of each case. Temporal features of either instantaneous frequency (IF) function or IA are computed. The feature vectors are tested in a well-known database of different classes in interictal, ictal, and normal activities of EEG signals. The discriminating power of each vector is evaluated through one-way analysis of variance, and the classification results are verified using an artificial neural network (ANN) classifier. The performance of the classifier was assessed in term of sensitivity, specificity, and total classification accuracy. The spectral features of the AR(10) of IA and the temporal features of IA yielded 100% accuracy, 100% sensitivity, and 100% specificity in the ictal classification. By contrast, these features obtained only 83.33% of the total classification accuracy in ictal and interictal EEG signal.

Discovering Emotion-Inducing Music Features Using EEG Signals

2013 ◽  
Vol 17 (3) ◽  
pp. 362-370 ◽  
Author(s):  
Rafael Cabredo ◽  
◽  
Roberto Legaspi ◽  
Paul Salvador Inventado ◽  
Masayuki Numao ◽  
...  
Keyword(s):  
Cross Validation ◽  
Absolute Error ◽  
Window Length ◽  
Analysis Method ◽  
Eeg Signals ◽  
Eeg Data ◽  
Windowing Technique ◽  
High Level ◽  
Fold Cross Validation ◽  
Emotion Models

Music induces different kinds of emotions in listeners. Previous research on music and emotions discovered that different music features can be used for classifying how certain music can induce emotions in an individual. We propose a method for collecting electroencephalograph (EEG) data from subjects listening to emotion-inducing music. The EEG data is used to continuously label high-level music features with continuous-valued emotion annotations using the emotion spectrum analysis method. The music features are extracted fromMIDI files using a windowing technique. We highlight the results of two emotion models for stress and relaxation which were constructed using C4.5. Evaluations of the models using 10-fold cross validation give promising results with an average relative absolute error of 6.54% using a window length of 38.4 seconds.

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.

scholarly journals A Densely Connected Multi-Branch 3D Convolutional Neural Network for Motor Imagery EEG Decoding

2021 ◽  
Vol 11 (2) ◽  
pp. 197
Author(s):  
Tianjun Liu ◽  
Deling Yang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Motor Imagery ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Signal Features ◽  
Average Accuracy ◽  
3D Representation ◽  
3D Cnn ◽  
The Way

Motor imagery (MI) is a classical method of brain–computer interaction (BCI), in which electroencephalogram (EEG) signal features evoked by imaginary body movements are recognized, and relevant information is extracted. Recently, various deep-learning methods are being focused on in finding an easy-to-use EEG representation method that can preserve both temporal information and spatial information. To further utilize the spatial and temporal features of EEG signals, an improved 3D representation of the EEG and a densely connected multi-branch 3D convolutional neural network (dense M3D CNN) for MI classification are introduced in this paper. Specifically, as compared to the original 3D representation, a new padding method is proposed to pad the points without electrodes with the mean of all the EEG signals. Based on this new 3D presentation, a densely connected multi-branch 3D CNN with a novel dense connectivity is proposed for extracting the EEG signal features. Experiments were carried out on the WAY-EEG-GAL and BCI competition IV 2a datasets to verify the performance of this proposed method. The experimental results show that the proposed framework achieves a state-of-the-art performance that significantly outperforms the multi-branch 3D CNN framework, with a 6.208% improvement in the average accuracy for the BCI competition IV 2a datasets and 6.281% improvement in the average accuracy for the WAY-EEG-GAL datasets, with a smaller standard deviation. The results also prove the effectiveness and robustness of the method, along with validating its use in MI-classification tasks.

scholarly journals Research on subliminal visual messages based on EEG signal and convolutional neural network

2021 ◽  
Vol 336 ◽  
pp. 05014
Author(s):  
Zhiming Chen ◽  
Yanshan Tan ◽  
Zhuo Zhang ◽  
Ming Li
Keyword(s):  
Classification Accuracy ◽  
Visual Information ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Visual Effects ◽  
Commercial Advertising ◽  
The Impact ◽  
The Brain ◽  
Stimulus Time ◽  
Stimulation Of

The visual information that can't be detected by consciousness but can affect individual's behavior and attitude under specific conditions is called subliminal visual messages. In order to better apply subliminal visual messages to commercial advertising, education and other fields, this paper studied the process of subliminal visual messages in the brain. First, this paper designed a experiment to allow the subjects to see a series of pictures stimulation of different durations and collect the EEG signals, then analyzed the impact of stimulation time on classification accuracy. The experimental results showed that when the stimulus time is short, the classification accuracy increases with the increase of time, resulting in subliminal visual effects. However, with the increase of stimulus time, the classification accuracy began to decline. We speculated that the visual information changed from subthreshold to suprathreshold. The subliminal visual effects were disturbed until disappeared.

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