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

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.

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

2021 ◽  
Vol 1070 (1) ◽  
pp. 012096
Author(s):  
S Pradeep Kumar ◽  
Suganiya Murugan ◽  
Jerritta Selvaraj ◽  
Arun Sahayadhas
Keyword(s):  
Mental Fatigue ◽  
Eeg Signals ◽  
Simulated Driving ◽  
Electroencephalogram Eeg

scholarly journals EEG Based Brain Computer Interfacing for Hand Assistant System Using Wavelet Transform

2020 ◽  
Vol 32 ◽  
pp. 03035
Author(s):  
Dayanand Dhongade ◽  
Mukesh Patil
Keyword(s):  
Wavelet Transform ◽  
Analog Signal ◽  
Digital Data ◽  
Robotic Hand ◽  
Eeg Signals ◽  
Arduino Uno ◽  
Brain Computer Interfacing ◽  
Low Amplitude ◽  
Near Future ◽  
Electroencephalogram Eeg

Robots have been of great use to mankind for several years. In situation where human body fails to operate as per the need robot’s functions in those situations quite efficiently. Electroencephalogram (EEG) controlled hand assistant makes use of EEG signals and Brain Computer Interface (BCI). EEG signals are obtained from the brain using Emotiv Insight headset, after which processing and features extraction of the signals is performed and then conditioning of signals is done as it is a low amplitude signal with additive noise. Signals processing is done on the analog signal by using wavelet transform. Wavelet transform will help to extract information from the analog signal. Then the signals are assigned with the signatures to perform the dedicated task Filtered signal is given to analog pins of Arduino Uno. With the help of inbuilt ADC available on Arduino Uno, Digital Data is also made available on the digital pins. Then through MATLAB access Arduino board. In near future if it gets similar kind of input it will understand exactly what operation to perform. Further the Robotic hand assistant can be operated as we want.

P300 Feature Extraction of Visual and Auditory Evoked EEG Signal

2014 ◽  
Vol 490-491 ◽  
pp. 1374-1377 ◽  
Author(s):  
Xiao Yan Qiao ◽  
Jia Hui Peng
Keyword(s):  
Feature Extraction ◽  
Wavelet Transform ◽  
Evoked Potentials ◽  
Brain Computer Interface ◽  
Auditory Stimulation ◽  
Computer Interface ◽  
Eeg Signal ◽  
Significant Issue ◽  
Eeg Signals ◽  
Electroencephalogram Eeg

It is a significant issue to accurately and quickly extract brain evoked potentials under strong noise in the research of brain-computer interface technology. Considering the non-stationary and nonlinearity of the electroencephalogram (EEG) signal, the method of wavelet transform is adopted to extract P300 feature from visual, auditory and visual-auditory evoked EEG signal. Firstly, the imperative pretreatment to EEG acquisition signals was performed. Secondly, respectivly obtained approximate and detail coefficients of each layer, by decomposing the pretreated signals for five layers using wavelet transform. Finally, the approximate coefficients of the fifth layer were reconstructed to extract P300 feature. The results have shown that the method can effectively extract the P300 feature under the different visual-auditory stimulation modes and lay a foundation for processing visual-auditory evoked EEG signals under the different mental tasks.

A Portable Electroencephalogram Recording System for Rats

2014 ◽  
Vol 577 ◽  
pp. 1236-1240
Author(s):  
Dian Zhang ◽  
Bo Wang ◽  
Qing Liang Qin
Keyword(s):  
Recording System ◽  
Band Pass Filter ◽  
Test Results ◽  
Electronic Components ◽  
Eeg Signals ◽  
Pass Filter ◽  
Wireless Transmitter ◽  
Eeg Data ◽  
Band Pass ◽  
Electroencephalogram Eeg

A wireless portable electroencephalogram (EEG) recording system for animals was designed, manufactured and then tested in rats. The system basically consisted of four modules: 1) EEG collecting module with the wireless transmitter and receiver (designed by NRF24LE1), 2) filter bank consisting of pre-amplifier, band pass filter and 50Hz trapper, 3) power management module and 4) display interface for showing EEG signals. The EEG data were modulated firstly and emitted by the wireless transmitter after being amplified and filtered. The receiver demodulated and displayed the signals in voltage through serial port. The system was designed as surface mount devices (SMD) with small size (20mm×25mm×3mm) and light weight (4g), and was fabricated of electronic components that were commercially available. The test results indicated that in given environment the system could stably record more than 8 hours and transmit EEG signals over a distance of 20m. Our system showed the features of small size, low power consumption and high accuracy which were suitable for EEG telemetry in rats.

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.

scholarly journals Detection of Driver Braking Intention Using EEG Signals During Simulated Driving

Sensors ◽  
2019 ◽  
Vol 19 (13) ◽  
pp. 2863 ◽  
Author(s):  
Trung-Hau Nguyen ◽  
Wan-Young Chung
Keyword(s):  
Training Data ◽  
Detection Accuracy ◽  
Eeg Signals ◽  
Simulated Driving ◽  
Emergency Braking ◽  
Emergency Situations ◽  
Artificial Neural Network Classifier ◽  
Short Period ◽  
Novel Method ◽  
Electroencephalogram Eeg

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.

scholarly journals InstanceEasyTL: An Improved Transfer-Learning Method for EEG-Based Cross-Subject Fatigue Detection

Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7251
Author(s):  
Hong Zeng ◽  
Jiaming Zhang ◽  
Wael Zakaria ◽  
Fabio Babiloni ◽  
Borghini Gianluca ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Support Vector ◽  
Learning Method ◽  
Learning Models ◽  
Eeg Signals ◽  
Eeg Data ◽  
State Detection ◽  
Fatigue Detection ◽  
Electroencephalogram Eeg ◽  
Machine Learning Models

Electroencephalogram (EEG) is an effective indicator for the detection of driver fatigue. Due to the significant differences in EEG signals across subjects, and difficulty in collecting sufficient EEG samples for analysis during driving, detecting fatigue across subjects through using EEG signals remains a challenge. EasyTL is a kind of transfer-learning model, which has demonstrated better performance in the field of image recognition, but not yet been applied in cross-subject EEG-based applications. In this paper, we propose an improved EasyTL-based classifier, the InstanceEasyTL, to perform EEG-based analysis for cross-subject fatigue mental-state detection. Experimental results show that InstanceEasyTL not only requires less EEG data, but also obtains better performance in accuracy and robustness than EasyTL, as well as existing machine-learning models such as Support Vector Machine (SVM), Transfer Component Analysis (TCA), Geodesic Flow Kernel (GFK), and Domain-adversarial Neural Networks (DANN), etc.

scholarly journals Electroencephalogram Application for the Analysis of Stress Relief in the Seasonal Landscape

2021 ◽  
Vol 18 (16) ◽  
pp. 8522
Author(s):  
Yuting Wang ◽  
Ming Xu
Keyword(s):  
Human Perception ◽  
Integrated Approach ◽  
Brain Wave ◽  
Natural Landscape ◽  
Eeg Data ◽  
Significant Difference ◽  
Different Seasons ◽  
Landscape Types ◽  
Electroencephalogram Eeg ◽  
Objective Tool

This study proposes an integrated approach to assess the psychological and physiological responses of people in natural seasonal landscapes. The questionnaire of restoration outcomes scale (ROS), willingness to visit (WTV), cultural ecosystem services (CES) cognitive classification, and the neuroscientific technique based on electroencephalogram (EEG) measurements were applied. The effects of different landscapes on human perception were studied by comparing the EEG data of different landscape types and different seasons. The coupling relationship between EEG data and stress recovery was also examined. The results showed the following: First, there was a significant difference between the winter landscape and the summer natural landscape. Second, only the winter landscape showed significant gender differences. Third, the values of ROS and WTV in the summer landscape were greater than those in the winter landscape. Fourth, the number of CES in the summer landscape was significantly higher than that in the winter landscape, and the number of CES in water was higher than that in the forest and grassland. Thus, brain wave data and quantified values from questionnaires including ROS, WTV, and CES showed significant seasonality. Therefore, an EEG can be used as a new, more objective tool and method for landscape evaluation and planning in the future.

scholarly journals Investigating Nuisance Effects Induced in EEG During tACS Application

2021 ◽  
Vol 15 ◽  
Author(s):  
Romain Holzmann ◽  
Judith Koppehele-Gossel ◽  
Ursula Voss ◽  
Ansgar Klimke
Keyword(s):  
Brain Function ◽  
Human Subject ◽  
Higher Order ◽  
Frequency Range ◽  
Neuronal Oscillations ◽  
Eeg Data ◽  
Strong Stimulation ◽  
Transcranial Alternating Current Stimulation ◽  
Methodological Approaches ◽  
Electroencephalogram Eeg

Transcranial alternating-current stimulation (tACS) in the frequency range of 1–100 Hz has come to be used routinely in electroencephalogram (EEG) studies of brain function through entrainment of neuronal oscillations. It turned out, however, to be highly non-trivial to remove the strong stimulation signal, including its harmonic and non-harmonic distortions, as well as various induced higher-order artifacts from the EEG data recorded during the stimulation. In this paper, we discuss some of the problems encountered and present methodological approaches aimed at overcoming them. To illustrate the mechanisms of artifact induction and the proposed removal strategies, we use data obtained with the help of a schematic demonstrator setup as well as human-subject data.

Graph Attention Network with Focal Loss for Seizure Detection on Electroencephalography Signals

2021 ◽  
pp. 2150027
Author(s):  
Yanna Zhao ◽  
Gaobo Zhang ◽  
Changxu Dong ◽  
Qi Yuan ◽  
Fangzhou Xu ◽  
...  
Keyword(s):  
Detection Method ◽  
Seizure Detection ◽  
Vital Role ◽  
Eeg Signals ◽  
Attention Network ◽  
Data Imbalance ◽  
Imbalance Problem ◽  
Eeg Data ◽  
Epilepsy Diagnosis ◽  
Electroencephalogram Eeg

Automatic seizure detection from electroencephalogram (EEG) plays a vital role in accelerating epilepsy diagnosis. Previous researches on seizure detection mainly focused on extracting time-domain and frequency-domain features from single electrodes, while paying little attention to the positional correlations between different EEG channels of the same subject. Moreover, data imbalance is common in seizure detection scenarios where the duration of nonseizure periods is much longer than the duration of seizures. To cope with the two challenges, a novel seizure detection method based on graph attention network (GAT) is presented. The approach acts on graph-structured data and takes the raw EEG data as input. The positional relationship between different EEG signals is exploited by GAT. The loss function of the GAT model is redefined using the focal loss to tackle data imbalance problem. Experiments are conducted on the CHB-MIT dataset. The accuracy, sensitivity and specificity of the proposed method are 98.89[Formula: see text], 97.10[Formula: see text] and 99.63[Formula: see text], respectively.

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