scholarly journals A High-Performance Lossless Compression Scheme for EEG Signals Using Wavelet Transform and Neural Network Predictors

2012 ◽  
Vol 2012 ◽  
pp. 1-8 ◽  
Author(s):  
N. Sriraam
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
High Performance ◽  
Digital Health ◽  
Error Modeling ◽  
Eeg Signals ◽  
Compression Scheme ◽  
Diagnostic Reliability ◽  
Compression Efficiency ◽  
Eeg Data

Developments of new classes of efficient compression algorithms, software systems, and hardware for data intensive applications in today's digital health care systems provide timely and meaningful solutions in response to exponentially growing patient information data complexity and associated analysis requirements. Of the different 1D medical signals, electroencephalography (EEG) data is of great importance to the neurologist for detecting brain-related disorders. The volume of digitized EEG data generated and preserved for future reference exceeds the capacity of recent developments in digital storage and communication media and hence there is a need for an efficient compression system. This paper presents a new and efficient high performance lossless EEG compression using wavelet transform and neural network predictors. The coefficients generated from the EEG signal by integer wavelet transform are used to train the neural network predictors. The error residues are further encoded using a combinational entropy encoder, Lempel-Ziv-arithmetic encoder. Also a new context-based error modeling is also investigated to improve the compression efficiency. A compression ratio of 2.99 (with compression efficiency of 67%) is achieved with the proposed scheme with less encoding time thereby providing diagnostic reliability for lossless transmission as well as recovery of EEG signals for telemedicine applications.

scholarly journals Left and Right Hand Movements EEG Signals Classification Using Wavelet Transform and Probabilistic Neural Network

2015 ◽  
Vol 5 (1) ◽  
pp. 92 ◽  
Author(s):  
A. B. M. Aowlad Hossain ◽  
Md. Wasiur Rahman ◽  
Manjurul Ahsan Riheen
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Probabilistic Neural Network ◽  
Hand Movement ◽  
Hand Movements ◽  
Discrete Wavelet ◽  
Eeg Signals ◽  
Beta Band ◽  
Right Hand ◽  
Left And Right

Electroencephalogram (EEG) signals have great importance in the area of brain-computer interface (BCI) which has diverse applications ranging from medicine to entertainment. BCI acquires brain signals, extracts informative features and generates control signals from the knowledge of these features for functioning of external devices. The objective of this work is twofold. Firstly, to extract suitable features related to hand movements and secondly, to discriminate the left and right hand movements signals finding effective classifier. This work is a continuation of our previous study where beta band was found compatible for hand movement analysis. The discrete wavelet transform (DWT) has been used to separate beta band of the EEG signal in order to extract features.  The performance of a probabilistic neural network (PNN) is investigated to find better classifier of left and right hand movements EEG signals and compared with classical back propagation based neural network. The obtained results shows that PNN (99.1%) has better classification rate than the BP (88.9%). The results of this study are expected to be helpful in brain computer interfacing for hand movements related bio-rehabilitation applications.

scholarly journals Sleep State Classification Using Power Spectral Density and Residual Neural Network with Multichannel EEG Signals

2020 ◽  
Vol 10 (21) ◽  
pp. 7639
Author(s):  
Md Junayed Hasan ◽  
Dongkoo Shon ◽  
Kichang Im ◽  
Hyun-Kyun Choi ◽  
Dae-Seung Yoo ◽  
...  
Keyword(s):  
Neural Network ◽  
Spectral Density ◽  
Power Spectral Density ◽  
Sleep Stage ◽  
Human Subjects ◽  
Sleep Stages ◽  
Eeg Signals ◽  
Healthy Human ◽  
Power Spectral ◽  
Eeg Data

This paper proposes a classification framework for automatic sleep stage detection in both male and female human subjects by analyzing the electroencephalogram (EEG) data of polysomnography (PSG) recorded for three regions of the human brain, i.e., the pre-frontal, central, and occipital lobes. Without considering any artifact removal approach, the residual neural network (ResNet) architecture is used to automatically learn the distinctive features of different sleep stages from the power spectral density (PSD) of the raw EEG data. The residual block of the ResNet learns the intrinsic features of different sleep stages from the EEG data while avoiding the vanishing gradient problem. The proposed approach is validated using the sleep dataset of the Dreams database, which comprises of EEG signals for 20 healthy human subjects, 16 female and 4 male. Our experimental results demonstrate the effectiveness of the ResNet based approach in identifying different sleep stages in both female and male subjects compared to state-of-the-art methods with classification accuracies of 87.8% and 83.7%, respectively.

scholarly journals A Novel Convolutional Neural Network Classification Approach of Motor-Imagery EEG Recording Based on Deep Learning

2021 ◽  
Vol 11 (21) ◽  
pp. 9948
Author(s):  
Amira Echtioui ◽  
Ayoub Mlaouah ◽  
Wassim Zouch ◽  
Mohamed Ghorbel ◽  
Chokri Mhiri ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Motor Imagery ◽  
Signal To Noise Ratio ◽  
Eeg Signals ◽  
Learning Techniques ◽  
Eeg Data ◽  
Spatial And Temporal Characteristics

Recently, Electroencephalography (EEG) motor imagery (MI) signals have received increasing attention because it became possible to use these signals to encode a person’s intention to perform an action. Researchers have used MI signals to help people with partial or total paralysis, control devices such as exoskeletons, wheelchairs, prostheses, and even independent driving. Therefore, classifying the motor imagery tasks of these signals is important for a Brain-Computer Interface (BCI) system. Classifying the MI tasks from EEG signals is difficult to offer a good decoder due to the dynamic nature of the signal, its low signal-to-noise ratio, complexity, and dependence on the sensor positions. In this paper, we investigate five multilayer methods for classifying MI tasks: proposed methods based on Artificial Neural Network, Convolutional Neural Network 1 (CNN1), CNN2, CNN1 with CNN2 merged, and the modified CNN1 with CNN2 merged. These proposed methods use different spatial and temporal characteristics extracted from raw EEG data. We demonstrate that our proposed CNN1-based method outperforms state-of-the-art machine/deep learning techniques for EEG classification by an accuracy value of 68.77% and use spatial and frequency characteristics on the BCI Competition IV-2a dataset, which includes nine subjects performing four MI tasks (left/right hand, feet, and tongue). The experimental results demonstrate the feasibility of this proposed method for the classification of MI-EEG signals and can be applied successfully to BCI systems where the amount of data is large due to daily recording.

scholarly journals Convolutional Neural Network Transfer Learning Applied to the Affective Auditory P300-Based BCI

2020 ◽  
Vol 32 (4) ◽  
pp. 731-737
Author(s):  
Akinari Onishi ◽  
◽  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Training Data ◽  
Learning Method ◽  
Eeg Signals ◽  
Recording Time ◽  
Auditory P300 ◽  
Eeg Data ◽  
Time Required

Brain-computer interface (BCI) enables us to interact with the external world via electroencephalography (EEG) signals. Recently, deep learning methods have been applied to the BCI to reduce the time required for recording training data. However, more evidence is required due to lack of comparison. To reveal more evidence, this study proposed a deep learning method named time-wise convolutional neural network (TWCNN), which was applied to a BCI dataset. In the evaluation, EEG data from a subject was classified utilizing previously recorded EEG data from other subjects. As a result, TWCNN showed the highest accuracy, which was significantly higher than the typically used classifier. The results suggest that the deep learning method may be useful to reduce the recording time of training data.

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 Identification of Anesthesia Stages from EEG Signals using Wavelet Entropy and Backpropagation Neural Network

2019 ◽  
pp. 17-20
Author(s):  
Ahmed Abdal Shafi Rasel
Keyword(s):  
Neural Network ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Training Data ◽  
Eeg Signals ◽  
The Neural Network ◽  
Power Spectral ◽  
Eeg Data ◽  
Density Analysis ◽  
Key Features

This study focuses on entropy based analysis of EEG signals for extracting features for a neural network based solution for identifying anesthetic levels. The process involves an optimized back propagation neural network with a supervised learning method. We provided the extracted features from EEG signals as training data for the neural network. The target outputs provided are levels of anesthesia stages. Wavelet analysis provides more effective extraction of key features from EEG data than power spectral density analysis using Fourier transform. The key features are used to train the Back Propagation Neural Network (BPNN) for pattern classification network. The final result shows that entropy-based feature extraction is an effective procedure for classifying EEG data.

scholarly journals Sample Entropy on Multidistance Signal Level Difference for Epileptic EEG Classification

2018 ◽  
Vol 2018 ◽  
pp. 1-6 ◽  
Author(s):  
Achmad Rizal ◽  
Sugondo Hadiyoso
Keyword(s):  
High Performance ◽  
Cell Activity ◽  
Sample Entropy ◽  
Brain Cell ◽  
Support Vector ◽  
Eeg Signals ◽  
Eeg Classification ◽  
Level Difference ◽  
Eeg Data

Epilepsy is a disorder of the brain’s nerves as a result of excessive brain cell activity. It is generally characterized by the recurrent unprovoked seizures. This neurological abnormality can be detected and evaluated using Electroencephalogram (EEG) signal. Many algorithms have been applied to achieve high performance for the EEG classification of epileptic. However, the complexity and randomness of EEG signals become a challenge to researchers in applying the appropriate algorithms. In this research, sample entropy on Multidistance Signal Level Difference (MSLD) was applied to obtain the characteristic of EEG signals, especially towards the epilepsy patients. The test was performed on three classes of EEG data: EEG signals of epilepsy patient in ictal (seizure), interictal conditions (occurring between seizures) and normal EEG signals from healthy subjects with a closed eye condition. In this study, classification and verification were done using the Support Vector Machine (SVM) method. Through the 5-fold cross-validation, experimental results showed the highest accuracy of 97.7%.

A Novel Method for Sleep-Stage Classification Based on Sonification of Sleep Electroencephalogram Signals Using Wavelet Transform and Recurrent Neural Network

2020 ◽  
Vol 83 (5) ◽  
pp. 468-486
Author(s):  
Foad Moradi ◽  
Hiwa Mohammadi ◽  
Mohammad Rezaei ◽  
Payam Sariaslani ◽  
Nazanin Razazian ◽  
...  
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Recurrent Neural Network ◽  
Short Term Memory ◽  
Sleep Stage ◽  
Sleep Stages ◽  
Discrete Wavelet ◽  
Eeg Signals ◽  
Proposed Model ◽  
Novel Method

<b><i>Introduction:</i></b> Visual sleep-stage scoring is a time-consuming technique that cannot extract the nonlinear characteristics of electroencephalogram (EEG). This article presents a novel method for sleep-stage differentiation based on sonification of sleep-EEG signals using wavelet transform and recurrent neural network (RNN). <b><i>Methods:</i></b> Two RNNs were designed and trained separately based on a database of classical guitar pieces and Kurdish tanbur Makams using a long short-term memory model. Moreover, discrete wavelet transform and wavelet packet decomposition were used to determine the association between the EEG signals and musical pitches. Continuous wavelet transform was applied to extract musical beat-based features from the EEG. Then, the pretrained RNN was used to generate music. To test the proposed model, 11 sleep EEGs were mapped onto the guitar and tanbur frequency intervals and presented to the pretrained RNN. Next, the generated music was randomly presented to 2 neurologists. <b><i>Results:</i></b> The proposed model classified the sleep stages with an accuracy of &#x3e;81% for tanbur and more than 93% for guitar musical pieces. The inter-rater reliability measured by Cohen’s kappa coefficient (<i>κ</i>) revealed good reliability for both tanbur (<i>κ</i> = 0.64, <i>p</i> &#x3c; 0.001) and guitar musical pieces (<i>κ</i> = 0.85, <i>p</i> &#x3c; 0.001). <b><i>Conclusion:</i></b> The present EEG sonification method leads to valid sleep staging by clinicians. The method could be used on various EEG databases for classification, differentiation, diagnosis, and treatment purposes. Real-time EEG sonification can be used as a feedback tool for replanning of neurophysiological functions for the management of many neurological and psychiatric disorders in the future.

scholarly journals EEG Multipurpose Eye Blink Detector using convolutional neural network

2021 ◽  
Vol 10 (15) ◽  
pp. e335101522712
Author(s):  
Amanda Ferrari Iaquinta ◽  
Ana Carolina de Sousa Silva ◽  
Aldrumont Ferraz Júnior ◽  
Jessica Monique de Toledo ◽  
Gustavo Voltani von Atzingen
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Electrical Signal ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Eye Blink ◽  
Multiple Strategies ◽  
Processing Data ◽  
Eeg Data ◽  
Close Proximity

The electrical signal emitted by the eyes movement produces a very strong artifact on EEG signal due to its close proximity to the sensors and abundance of occurrence. In the context of detecting eye blink artifacts in EEG waveforms for further removal and signal purification, multiple strategies where proposed in the literature. Most commonly applied methods require the use of a large number of electrodes, complex equipment for sampling and processing data. The goal of this work is to create a reliable and user independent algorithm for detecting and removing eye blink in EEG signals using CNN (convolutional neural network). For training and validation, three sets of public EEG data were used. All three sets contain samples obtained while the recruited subjects performed assigned tasks that included blink voluntarily in specific moments, watch a video and read an article. The model used in this study was able to have an embracing understanding of all the features that distinguish a trivial EEG signal from a signal contaminated with eye blink artifacts without being overfitted by specific features that only occurred in the situations when the signals were registered.

Sign in / Sign up

Export Citation Format

Share Document