DESIGN AND IMPLEMENTATION OF WIRELESS MULTI-CHANNEL EEG RECORDING SYSTEM AND STUDY OF EEG CLUSTERING METHOD

A multi-channel wireless EEG (electroencephalogram) acquisition and recording system is developed in this work. The system includes an EEG sensing and transmission unit and a digital processing circuit. The former is composed of pre-amplifiers, filters, and gain amplifiers. The kernel of the later digital processing circuit is a micro-controller unit (MCU, TI-MSP430), which is utilized to convert the EEG signals into digital signals and fulfill the digital filtering. By means of Bluetooth communication module, the digitized signals are sent to the back-end such as PC or PDA. Thus, the patient's EEG signal can be observed and stored without any long cables such that the analogue distortion caused by long distance transmission can be reduced significantly. Furthermore, an integrated classification method, consisting of non-linear energy operator (NLEO), autoregressive (AR) model, and bisecting k-means algorithm, is also proposed to perform EEG off-line clustering at the back-end. First, the NLEO algorithm is utilized to divide the EEG signals into many small signal segments according to the features of the amplitude and frequency of EEG signals. The AR model is then applied to extract two characteristic values, i.e., frequency and amplitude (peak to peak value), of each segment and to form characteristic matrix for each segment of EEG signal. Finally, the improved modified k-means algorithm is utilized to assort similar EEG segments into better data classification, which allows accessing the long-term EEG signals more quickly.

Download Full-text

Complexity Measures of Brain Electrophysiological Activity

Journal of Psychophysiology ◽

10.1027/0269-8803/a000024 ◽

2010 ◽

Vol 24 (2) ◽

pp. 131-135 ◽

Cited By ~ 5

Author(s):

Włodzimierz Klonowski ◽

Pawel Stepien ◽

Robert Stepien

Keyword(s):

Brain Activity ◽

Deterministic Chaos ◽

Epileptic Seizures ◽

Eeg Signal ◽

Eeg Signals ◽

Complexity Measures ◽

Electrophysiological Activity ◽

Signal Complexity ◽

Physiological Sleep ◽

The Brain

Over 20 years ago, Watt and Hameroff (1987 ) suggested that consciousness may be described as a manifestation of deterministic chaos in the brain/mind. To analyze EEG-signal complexity, we used Higuchi’s fractal dimension in time domain and symbolic analysis methods. Our results of analysis of EEG-signals under anesthesia, during physiological sleep, and during epileptic seizures lead to a conclusion similar to that of Watt and Hameroff: Brain activity, measured by complexity of the EEG-signal, diminishes (becomes less chaotic) when consciousness is being “switched off”. So, consciousness may be described as a manifestation of deterministic chaos in the brain/mind.

Download Full-text

Eye-blink artifact removal from single channel EEG with k-means and SSA

Scientific Reports ◽

10.1038/s41598-021-90437-7 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Ajay Kumar Maddirala ◽

Kalyana C Veluvolu

Keyword(s):

Machine Learning ◽

Single Channel ◽

Learning Algorithm ◽

Singular Spectrum Analysis ◽

Machine Learning Algorithm ◽

Eeg Signal ◽

Eeg Signals ◽

Unsupervised Machine Learning ◽

Eye Blink ◽

Blink Artifact

AbstractIn recent years, the usage of portable electroencephalogram (EEG) devices are becoming popular for both clinical and non-clinical applications. In order to provide more comfort to the subject and measure the EEG signals for several hours, these devices usually consists of fewer EEG channels or even with a single EEG channel. However, electrooculogram (EOG) signal, also known as eye-blink artifact, produced by involuntary movement of eyelids, always contaminate the EEG signals. Very few techniques are available to remove these artifacts from single channel EEG and most of these techniques modify the uncontaminated regions of the EEG signal. In this paper, we developed a new framework that combines unsupervised machine learning algorithm (k-means) and singular spectrum analysis (SSA) technique to remove eye blink artifact without modifying actual EEG signal. The novelty of the work lies in the extraction of the eye-blink artifact based on the time-domain features of the EEG signal and the unsupervised machine learning algorithm. The extracted eye-blink artifact is further processed by the SSA method and finally subtracted from the contaminated single channel EEG signal to obtain the corrected EEG signal. Results with synthetic and real EEG signals demonstrate the superiority of the proposed method over the existing methods. Moreover, the frequency based measures [the power spectrum ratio ($$\Gamma $$ Γ ) and the mean absolute error (MAE)] also show that the proposed method does not modify the uncontaminated regions of the EEG signal while removing the eye-blink artifact.

Download Full-text

A Single-Terminal Fault Location Method for HVDC Transmission Lines Based on a Hybrid Deep Network

Electronics ◽

10.3390/electronics10030255 ◽

2021 ◽

Vol 10 (3) ◽

pp. 255

Author(s):

Lei Wang ◽

Yigang He ◽

Lie Li

Keyword(s):

Deep Learning ◽

Transmission Lines ◽

Fault Location ◽

Short Term Memory ◽

Engineering Application ◽

Energy Operator ◽

Sampling Frequency ◽

Learning Technology ◽

Long Distance ◽

Hvdc Transmission

High voltage direct current (HVDC) transmission systems play an increasingly important role in long-distance power transmission. Realizing accurate and timely fault location of transmission lines is extremely important for the safe operation of power systems. With the development of modern data acquisition and deep learning technology, deep learning methods have the feasibility of engineering application in fault location. The traditional single-terminal traveling wave method is used for fault location in HVDC systems. However, many challenges exist when a high impedance fault occurs including high sampling frequency dependence and difficulty to determine wave velocity and identify wave heads. In order to resolve these problems, this work proposed a deep hybrid convolutional neural network (CNN) and long short-term memory (LSTM) network model for single-terminal fault location of an HVDC system containing mixed cables and overhead line segments. Simultaneously, a variational mode decomposition–Teager energy operator is used in feature engineering to improve the effect of model training. 2D-CNN was employed as a classifier to identify fault segments, and LSTM as a regressor integrated the fault segment information of the classifier to achieve precise fault location. The experimental results demonstrate that the proposed method has high accuracy of fault location, with the effects of fault types, noise, sampling frequency, and different HVDC topologies in consideration.

Download Full-text

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.

Download Full-text

An Automated Approach for Epilepsy Detection Based on Tunable Q-Wavelet and Firefly Feature Selection Algorithm

International Journal of Biomedical Imaging ◽

10.1155/2018/5812872 ◽

2018 ◽

Vol 2018 ◽

pp. 1-12 ◽

Cited By ~ 6

Author(s):

Ahmed I. Sharaf ◽

Mohamed Abu El-Soud ◽

Ibrahim M. El-Henawy

Keyword(s):

Feature Selection ◽

Correlation Energy ◽

Epileptic Seizures ◽

Compact Set ◽

Eeg Signal ◽

Selection Algorithm ◽

Feature Selection Algorithm ◽

Eeg Signals ◽

Computer Aided ◽

Firefly Optimization

Detection of epileptic seizures using an electroencephalogram (EEG) signals is a challenging task that requires a high level of skilled neurophysiologists. Therefore, computer-aided detection provides an asset to the neurophysiologist in interpreting the EEG. This paper introduces a novel approach to recognize and classify the epileptic seizure and seizure-free EEG signals automatically by an intelligent computer-aided method. Moreover, the prediction of the preictal phase of the epilepsy is proposed to assist the neurophysiologist in the clinic. The proposed method presents two perspectives for the EEG signal processing to detect and classify the seizures and seizure-free signals. The first perspectives consider the EEG signal as a nonlinear time series. A tunable Q-wavelet is applied to decompose the signal into smaller segments called subbands. Then a chaotic, statistical, and power spectrum features sets are extracted from each subband. The second perspectives process the EEG signal as an image; hence the gray-level co-occurrence matrix is determined from the image to obtain the textures of contrast, correlation, energy, and homogeneity. Due to a large number of features obtained, a feature selection algorithm based on firefly optimization was applied. The firefly optimization reduces the original set of features and generates a reduced compact set. A random forest classifier is trained for the classification and prediction of the seizures and seizure-free signals. Afterward, a dataset from the University of Bonn, Germany, is used for benchmarking and evaluation. The proposed approach provided a significant result compared with other recent work regarding accuracy, recall, specificity, F-measure, and Matthew’s correlation coefficient.

Download Full-text

Neural Mass Model-Based Different EEG Signal Generation and Analysis in Simulink Sheikh Md. Rabiul Islam1, Md. Shakibul Islam2

Indian Journal of Signal Processing ◽

10.54105/ijsp.c1008.081321 ◽

2021 ◽

pp. 1-7

Author(s):

Sheikh Md. Rabiul Islam ◽

◽

Md. Shakibul Islam ◽

Keyword(s):

Signal Generation ◽

Neural Mass Model ◽

Pathophysiological Mechanism ◽

Eeg Signal ◽

Eeg Signals ◽

Mass Model ◽

Detailed Model ◽

Simulation Based ◽

Neural Mass ◽

The Brain

The electroencephalogram (EEG) is an electrophysiological monitoring strategy that records the spontaneous electrical movement of the brain coming about from ionic current inside the neurons of the brain. The importance of the EEG signal is mainly the diagnosis of different mental and brain neurodegenerative diseases and different abnormalities like seizure disorder, encephalopathy, dementia, memory problem, sleep disorder, stroke, etc. The EEG signal is very useful for someone in case of a coma to determine the level of brain activity. So, it is very important to study EEG generation and analysis. To reduce the complexity of understanding the pathophysiological mechanism of EEG signal generation and their changes, different simulation-based EEG modeling has been developed which are based on anatomical equivalent data. In this paper, Instead of a detailed model a neural mass model has been used to implement different simulation-based EEG models for EEG signal generation which refers to the simplified and straightforward method. This paper aims to introduce obtained EEG signals of own implementation of the Lopes da Silva model, Jansen-Rit model, and Wendling model in Simulink and to compare characteristic features with real EEG signals and better understanding the EEG abnormalities especially the seizure-like signal pattern.

Download Full-text

An Automatic Epilepsy Detection Method Based on Improved Inductive Transfer Learning

Computational and Mathematical Methods in Medicine ◽

10.1155/2020/5046315 ◽

2020 ◽

Vol 2020 ◽

pp. 1-13

Author(s):

Yufeng Yao ◽

Zhiming Cui

Keyword(s):

Transfer Learning ◽

Real Life ◽

Eeg Signal ◽

Signal Recognition ◽

Least Squares Regression ◽

Eeg Signals ◽

Strong Basis ◽

Inductive Transfer ◽

Inductive Transfer Learning ◽

The Impact

Epilepsy is a chronic disease caused by sudden abnormal discharge of brain neurons, causing transient brain dysfunction. The seizures of epilepsy have the characteristics of being sudden and repetitive, which has seriously endangered patients’ health, cognition, etc. In the current condition, EEG plays a vital role in the diagnosis, judgment, and qualitative location of epilepsy among the clinical diagnosis of various epileptic seizures and is an indispensable means of detection. The study of the EEG signals of patients with epilepsy can provide a strong basis and useful information for in-depth understanding of its pathogenesis. Although, intelligent classification technologies based on machine learning have been widely used to the classification of epilepsy EEG signals and show the effectiveness. In fact, it is difficult to ensure that there is always enough EEG data available for training the model in real life, which will affect the performance of the algorithms. In view of this, to reduce the impact of insufficient data on the detection performance of the algorithms, a novel discriminate least squares regression- (DLSR-) based inductive transfer learning method was introduced which is on the basis of DLSR and the inductive transfer learning. And, it is applied to promote the adaptability and accuracy of the epilepsy EEG signal recognition. The proposed method inherits the advantages of DLSR; it can be more suitable for classification scenarios by expanding the interval between different classes. Meanwhile, it can simultaneously use the data of the target domain and the knowledge of the source domain, which is helpful for getting better performance. The results show that the improved method has more advantages in EEG signal recognition comparing to several other representative methods.

Download Full-text

EPILEPTIC EEG IDENTIFICATION BASED ON HYBRID FEATURE EXTRACTION

Journal of Mechanics in Medicine and Biology ◽

10.1142/s0219519420500256 ◽

2020 ◽

Vol 20 (06) ◽

pp. 2050025 ◽

Cited By ~ 1

Author(s):

XIAOCHEN LIU ◽

JIZHONG SHEN ◽

WUFENG ZHAO

Keyword(s):

Feature Extraction ◽

Classification Accuracy ◽

Energy Operator ◽

Typical Feature ◽

Frequency Domain Analysis ◽

Identification System ◽

Eeg Signals ◽

Hybrid Features ◽

Interictal Eeg ◽

Hybrid Feature Extraction

Electroencephalogram (EEG) signals are widely used as an effective method for epilepsy analysis and diagnosis. For the establishment of an accurate and efficient epilepsy EEG identification system, it is very important to properly extract the features of EEG signals and select appropriate combination features. This paper proposes an automatic epileptic EEG identification method based on hybrid feature extraction. It uses temporal and frequency domain analysis, nonlinear analysis and one-dimensional local pattern recognition method to extract epileptic EEG features. Gradient energy operator and local speed pattern are proposed to better reflect typical feature in the active EEG signals measured during seizure-free intervals. The genetic algorithm is used to select the obtained hybrid features; then the AdaBoost classifier is used to classify epileptic EEG under various classification conditions. Classification results on the dataset developed by University of Bonn show that the proposed method can be used to classify normal EEG, interictal EEG and seizure activity with only a few features. Compared with related researches using the same dataset, the proposed method can obtain an equally satisfactory classification accuracy while the feature amount is reduced by 61–95%. In particular, the classification accuracy of the interictal and normal EEG can reach 99%.

Download Full-text

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

Micromachines ◽

10.3390/mi10110720 ◽

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.

Download Full-text

A Fault Diagnosis Method of Bogie Axle Box Bearing Based on Spectrum Whitening Demodulation

Sensors ◽

10.3390/s20247155 ◽

2020 ◽

Vol 20 (24) ◽

pp. 7155

Author(s):

Zejun Zheng ◽

Dongli Song ◽

Xiao Xu ◽

Lei Lei

Keyword(s):

Feature Extraction ◽

Health Status ◽

Fault Diagnosis ◽

High Speed ◽

Energy Operator ◽

Ar Model ◽

Diagnostic Efficiency ◽

Heavy Load ◽

Feature Extraction Method ◽

Vibration Data

The axle box bearing of bogie is one of the key components of the rail transit train, which can ensure the rotary motion of wheelsets and make the wheelsets adapt to the conditions of uneven railways. At the same time, the axle box bearing also exposes most of the load of the car body. Long-time high-speed rotation and heavy load make the axle box bearing prone to failure. If the bearing failure occurs, it will greatly affect the safety of the train. Therefore, it is extremely important to monitor the health status of the axle box bearing. At present, the health status of the axle box bearing is mainly monitored by vibration information and temperature information. Compared with the temperature data, the vibration data can more easily detect the early fault of the bearing, and early warning of the bearing state can avoid the occurrence of serious fault in time. Therefore, this paper is based on the vibration data of the axle box bearing to carry out adaptive fault diagnosis of bearing. First, the AR model predictive filter is used to denoise the vibration signal of the bearing, and then the signal is whitened in the frequency domain. Finally, the characteristic value of vibration data is extracted by energy operator demodulation, and the fault type is determined by comparing with the theoretical value. Through the analysis of the constructed simulation signal data, the characteristic parameters of the data can be effectively extracted. The experimental data collected from the bearing testbed of high-speed train are analyzed and verified, which further proves the effectiveness of the feature extraction method proposed in this paper. Compared with other axle box bearing fault diagnosis methods, the innovation of the proposed method is that the signal is denoised twice by using AR filter and spectrum whitening, and the adaptive extraction of fault features is realized by using energy operator. At the same time, the steps of setting parameters in the process of feature extraction are avoided in other feature extraction methods, which improves the diagnostic efficiency and is conducive to use in online monitoring system.

Download Full-text