scholarly journals ANALYSIS OF PEAK-WAVE DISCHARGES OF EEG WITH THE USE OF WAVELET TRANSFORMATIONS

2020 ◽  
pp. 224-234
Author(s):  
G.V. Kit
Keyword(s):  
Wavelet Transforms ◽  
Neurological Diseases ◽  
Absence Epilepsy ◽  
Eeg Analysis ◽  
Eeg Signals ◽  
Time Intervals ◽  
Signal Quantization ◽  
Traditional Approaches ◽  
Electroencephalogram Eeg ◽  
Wavelet Transformations

The method of analysis of electroencephalograms (EEG) on the basis of wavelet transformations is offered. Electroencephalogram (EEG) analysis is widely used in clinical practice for diagnosing such neurological diseases as epilepsy, Parkinson's disease and others. Traditional approaches to EEG analysis, generally accepted in the clinical diagnosis of diseases, are due to the fact that for a certain time after the stimulus, the EEG amplitudes are calculated at time intervals that depend on the frequency of signal quantization. Therefore, it is important to develop algorithms for classifying EEG signals using wavelet transforms. The analysis of peak-wave EEG discharges, which are indicators of the presence or absence of absence epilepsy, was performed. The EEG recording areas were decomposed into the main EEG frequency bands. Wavelet transform in combination with artificial neural networks makes it possible to implement a classifier based on the energy distribution of the components of the EEG signal. Determining the activity of individual components of EEG signals, as well as the materiality of the processes that take place in the sources of these waves, may be the subject of further research.

scholarly journals Special Issue on Brain Machine/Computer Interface and its Application

2020 ◽  
Vol 32 (4) ◽  
pp. 723-723
Author(s):  
Shoichiro Fujisawa ◽  
Minoru Fukumi ◽  
Jianting Cao ◽  
Yasue Mitsukura ◽  
Shin-ichi Ito
Keyword(s):  
Brain Activity ◽  
Computer Interface ◽  
Eeg Analysis ◽  
Special Issue ◽  
Eeg Signals ◽  
Auditory P300 ◽  
Environment Control ◽  
Electroencephalogram Eeg ◽  
Human Support

Brain machine/computer interface (BMI/BCI) technologies are based on analyzing brain activity to control machines and support the communication of commands and messages. To sense brain activities, a functional NIRS and electroencephalogram (EEG) that has been developed for that purpose is often employed. Analysis techniques and algorithms for the NIRS and EEG signals have also been created, and human support systems in the form of BMI/BCI applications have been developed. In the field of rehabilitation, BMI/BCI is used to control environment control systems and electric wheelchairs. In medicine, BMI/BCI is used to assist in communications for patient support. In industry, BMI/BCI is used to analyze sensibility and develop novel games. This special issue on Brain Machine/Computer Interface and its Application includes six interesting papers that cover the following topics: an EEG analysis method for human-wants detection, cognitive function using EEG analysis, auditory P300 detection, a wheelchair control BCI using SSVEP, a drone control BMI based on SSVEP that uses deep learning, and an improved CMAC model. We thank all authors and reviewers of the papers and the Editorial Board of Journal of Robotics and Mechatronics for its help with this special issue.

DECOMPOSITION OF SEIZURES IN EEG SIGNALS USING DISCRETE WAVELET TRANSFORMS (DWT)

2021 ◽  
pp. 50-52
Author(s):  
N Shweta ◽  
Nagendra H
Keyword(s):  
Neurological Disorders ◽  
Wavelet Transforms ◽  
Epileptic Seizures ◽  
Discrete Wavelet ◽  
Discrete Wavelet Transforms ◽  
Eeg Signals ◽  
Seizure Duration ◽  
Eeg Recordings ◽  
Electroencephalogram Eeg ◽  
The Brain

An electroencephalogram (EEG) is a test that records electrical activity in the brain. Epileptic seizures affect approximately 50 million people worldwide, making it one of the most serious neurological disorders. Seizures cause a loss of consciousness, but there are no specic signs associated with epileptic seizures. analysing the brain's activity during seizures and locating the seizure duration in EEG recordings is difcult and time consuming. A discrete wavelet transform (DWT), which is an effective tool for decomposing EEG signals into delta, theta, alpha, beta, and gamma ( and ) frequency bands. For research, the db4 is used, which has a morphological d,q,a,b g structure that is different to that of EEG.

EEG-EMG Signal Processing and Analysis for the Neuromuscular Activity Patterns with Three Motion Modes under Two Time Intervals

2013 ◽  
Vol 790 ◽  
pp. 615-618
Author(s):  
Nan Nan Li ◽  
Tian Chen Zhai ◽  
Shuang Liu ◽  
An Shuang Fu ◽  
Rui Xu ◽  
...  
Keyword(s):  
Activity Patterns ◽  
Eeg Signals ◽  
Time Intervals ◽  
Threshold Detection ◽  
Time Frequency ◽  
Emg Signal ◽  
Motion Modes ◽  
Finger Flexion ◽  
Flexor Digitorum ◽  
Electroencephalogram Eeg

To address the issue that how the EEG-EMG signals change according to different motion modes, an experiment was conducted on ten subjects with three tasks performing the voluntary, stimulated and imaginary finger flexion activities. The experiment was set two programs including 10s and 2s time intervals. Electroencephalogram (EEG) from C3/C4 channels and electromyogram (EMG) from flexor digitorum superficialis were recorded simultaneously. Besides the threshold detection of wave peaks between two points, morlet wavlet-based time-frequency analysis was adopted to study the independent variation mechanisms between EEG and EMG under different motion modes. The results indicated that EMG signals of 2s intervals exhibited a similar trend with 10s intervals. The EMG energy increased over 50 Hz when actions occurred. On the contrary, there were no significant changes in imaginary task. In addition, EEG signals performed obviously different. No pronounced similar changes were found in 10s and 2s intervals. Finally, the results demonstrated that EEG-EMG causality was high during 2s intervals in stimulation task.

scholarly journals Removal of ocular artifacts from EEG signals using adaptive threshold PCA and Wavelet transforms

2011 ◽  
pp. 42-46
Author(s):  
P. Ashok Babu ◽  
K. V.S.V.R. Prasad
Keyword(s):  
Principal Component Analysis ◽  
Wavelet Transforms ◽  
Principal Component ◽  
Adaptive Threshold ◽  
Eeg Signals ◽  
Threshold Method ◽  
Eye Blinks ◽  
Elapsed Time ◽  
Electroencephalogram Eeg ◽  
Ocular Artifacts

It becomes more difficult to identify and analyze the Electroencephalogram (EEG) signals when it is corrupted by eye movements and eye blinks. This paper gives the different methods how to remove the artifacts in EEG signals. In this paper we proposed wavelet based threshold method and Principal Component Analysis (PCA) based adaptive threshold method to remove the ocular artifacts. Compared to the wavelet threshold method PCA based adaptive threshold method will gives the better PSNR value and it will decreases the elapsed time.

scholarly journals Motor Imagery Classification using Wavelet-Based Features and Tensorflow

2019 ◽  
Vol 9 (1) ◽  
pp. 4650-4652
Keyword(s):  
Frequency Distribution ◽  
Motor Imagery ◽  
Wavelet Transforms ◽  
Haar Wavelet ◽  
Second Step ◽  
Eeg Signals ◽  
Accuracy Rate ◽  
Motion Imagery ◽  
Left And Right ◽  
Electroencephalogram Eeg

This paper proposes a methodology for making a decision on left and right motor imagery using Tensorflow and wavelet-based feature extraction. Wavelet coefficients are extracted by the Haar wavelet transforms from electroencephalogram (EEG) signals in the first step. In the second step, 60 wavelet-based features are extracted by the frequency distribution and the amount of variability in frequency distribution. In the final step, this paper classified left or right motion imagery using these 60 features as inputs to the Tensorflow. The proposed methodology shows that the performance result is 82.14% with 60 features in accuracy rate

scholarly journals Recognition of emotional states using EEG signals based on time-frequency analysis and SVM classifier

2019 ◽  
Vol 9 (2) ◽  
pp. 1012 ◽  
Author(s):  
Fabian Parsia George ◽  
Istiaque Mannafee Shaikat ◽  
Prommy Sultana Ferdawoos Hossain ◽  
Mohammad Zavid Parvez ◽  
Jia Uddin
Keyword(s):  
Emotion Recognition ◽  
Age Groups ◽  
Svm Classifier ◽  
Emotional States ◽  
Eeg Signals ◽  
Time Frequency ◽  
Gender And Age ◽  
Box And Whisker Plot ◽  
Traditional Approaches ◽  
Electroencephalogram Eeg

The recognition of emotions is a vast significance and a high developing field of research in the recent years. The applications of emotion recognition have left an exceptional mark in various fields including education and research. Traditional approaches used facial expressions or voice intonation to detect emotions, however, facial gestures and spoken language can lead to biased and ambiguous results. This is why, researchers have started to use electroencephalogram (EEG) technique which is well defined method for emotion recognition. Some approaches used standard and pre-defined methods of the signal processing area and some worked with either fewer channels or fewer subjects to record EEG signals for their research. This paper proposed an emotion detection method based on time-frequency domain statistical features. Box-and-whisker plot is used to select the optimal features, which are later feed to SVM classifier for training and testing the DEAP dataset, where 32 participants with different gender and age groups are considered. The experimental results show that the proposed method exhibits 92.36% accuracy for our tested dataset. In addition, the proposed method outperforms than the state-of-art methods by exhibiting higher accuracy.

Approximate Entropy of the Electroencephalogram in Healthy Awake Subjects and Absence Epilepsy Patients

2005 ◽  
Vol 36 (3) ◽  
pp. 188-193 ◽  
Author(s):  
Naoto Burioka ◽  
Germaine Cornélissen ◽  
Yoshihiro Maegaki ◽  
Franz Halberg ◽  
Daniel T. Kaplan ◽  
...  
Keyword(s):  
Healthy Subjects ◽  
Brain Activity ◽  
Epileptic Seizures ◽  
Approximate Entropy ◽  
Surrogate Data ◽  
Absence Epilepsy ◽  
Eeg Signals ◽  
Eyes Closed ◽  
The Mean ◽  
Electroencephalogram Eeg

The approximate entropy (ApEn) of signals in the electroencephalogram (EEG) was evaluated in 8 healthy volunteers and in 10 patients with absence epilepsy, both during seizure-free and seizure intervals. We estimated the nonlinearity of each 3-sec EEG segment using surrogate data methods. The mean (± SD) ApEn in EEG was 0.83 ± 0.22 in healthy subjects awake with eyes closed. It was significantly lower during epileptic seizures (0.48 ± 0.05) than during seizure-free intervals (0.80 ± 0.13) (P<0.001). Nonlinearity was clearly detected in EEG signals from epileptic patients during seizures but not during seizure-free intervals or in EEG signals from healthy subjects. The ApEn of EEG signals estimated over consecutive intervals could serve to determine pathological brain activity such as that occurring during absence epilepsy.

scholarly journals Taxonomy on EEG Artifacts Removal Methods, Issues, and Healthcare Applications

2021 ◽  
Vol 33 (1) ◽  
pp. 19-46
Author(s):  
Vandana Roy ◽  
Prashant Kumar Shukla ◽  
Amit Kumar Gupta ◽  
Vikas Goel ◽  
Piyush Kumar Shukla ◽  
...  
Keyword(s):  
Big Data ◽  
Information Quality ◽  
Neurological Diseases ◽  
Healthcare Research ◽  
Eeg Signals ◽  
Healthcare Applications ◽  
Research Papers ◽  
Artifacts Removal ◽  
Electroencephalogram Eeg ◽  
Eeg Artifacts

Electroencephalogram (EEG) signals are progressively growing data widely known as biomedical big data, which is applied in biomedical and healthcare research. The measurement and processing of EEG signal result in the probability of signal contamination through artifacts which can obstruct the important features and information quality existing in the signal. To diagnose the human neurological diseases like epilepsy, tumors, and problems associated with trauma, these artifacts must be properly pruned assuring that there is no loss of the main attributes of EEG signals. In this paper, the latest and updated information in terms of important key features are arranged and tabulated extensively by considering the 60 published technical research papers based on EEG artifact removal method. Moreover, the paper is a review vision about the works in the area of EEG applied to healthcare and summarizes the challenges, research gaps, and opportunities to improve the EEG big data artifacts removal more precisely.

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 Emotion Classification Using an Improved SincNet-Based Deep Learning Model

2019 ◽  
Vol 9 (11) ◽  
pp. 326 ◽  
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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