A Novel Method for Analyzing Dynamic Complexity of EEG Signals Using Symbolic Entropy Measurement

2009 ◽  
pp. 530-537
Author(s):  
Lisha Sun ◽  
Jun Yu ◽  
Patch J. Beadle
Keyword(s):  
Eeg Signals ◽  
Dynamic Complexity ◽  
Entropy Measurement ◽  
Novel Method

scholarly journals An Optimized Channel Selection Method Based on Multifrequency CSP-Rank for Motor Imagery-Based BCI System

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Jian Kui Feng ◽  
Jing Jin ◽  
Ian Daly ◽  
Jiale Zhou ◽  
Yugang Niu ◽  
...  
Keyword(s):  
Motor Imagery ◽  
Classification Accuracy ◽  
Channel Selection ◽  
Selection Method ◽  
Selection Methods ◽  
Eeg Signals ◽  
Common Spatial Pattern ◽  
Linear Discriminant ◽  
Novel Method ◽  
Electroencephalogram Eeg

Background. Due to the redundant information contained in multichannel electroencephalogram (EEG) signals, the classification accuracy of brain-computer interface (BCI) systems may deteriorate to a large extent. Channel selection methods can help to remove task-independent electroencephalogram (EEG) signals and hence improve the performance of BCI systems. However, in different frequency bands, brain areas associated with motor imagery are not exactly the same, which will result in the inability of traditional channel selection methods to extract effective EEG features. New Method. To address the above problem, this paper proposes a novel method based on common spatial pattern- (CSP-) rank channel selection for multifrequency band EEG (CSP-R-MF). It combines the multiband signal decomposition filtering and the CSP-rank channel selection methods to select significant channels, and then linear discriminant analysis (LDA) was used to calculate the classification accuracy. Results. The results showed that our proposed CSP-R-MF method could significantly improve the average classification accuracy compared with the CSP-rank channel selection method.

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.

Epileptic EEG Classification by Using Time-Frequency Images for Deep Learning

2021 ◽  
pp. 2150026
Author(s):  
Mehmet Akif Ozdemir ◽  
Ozlem Karabiber Cura ◽  
Aydin Akan
Keyword(s):  
Epileptic Seizures ◽  
Eeg Signals ◽  
Clinical Tool ◽  
Energy Distributions ◽  
Time Frequency ◽  
Synchrosqueezing Transform ◽  
Novel Method ◽  
Eeg Recordings ◽  
Accuracy Rates ◽  
Aided Diagnosis

Epilepsy is one of the most common brain disorders worldwide. The most frequently used clinical tool to detect epileptic events and monitor epilepsy patients is the EEG recordings. There have been proposed many computer-aided diagnosis systems using EEG signals for the detection and prediction of seizures. In this study, a novel method based on Fourier-based Synchrosqueezing Transform (SST), which is a high-resolution time-frequency (TF) representation, and Convolutional Neural Network (CNN) is proposed to detect and predict seizure segments. SST is based on the reassignment of signal components in the TF plane which provides highly localized TF energy distributions. Epileptic seizures cause sudden energy discharges which are well represented in the TF plane by using the SST method. The proposed SST-based CNN method is evaluated using the IKCU dataset we collected, and the publicly available CHB-MIT dataset. Experimental results demonstrate that the proposed approach yields high average segment-based seizure detection precision and accuracy rates for both datasets (IKCU: 98.99% PRE and 99.06% ACC; CHB-MIT: 99.81% PRE and 99.63% ACC). Additionally, SST-based CNN approach provides significantly higher segment-based seizure prediction performance with 98.54% PRE and 97.92% ACC than similar approaches presented in the literature using the CHB-MIT dataset.

scholarly journals A Novel Method to Perform Search Query using Brainwave Signals

2019 ◽  
Vol 8 (5S3) ◽  
pp. 23-27
Keyword(s):  
Search Engine ◽  
Clustering Algorithm ◽  
Ionic Current ◽  
Time Frame ◽  
Eeg Signals ◽  
Brain Waves ◽  
Novel Method ◽  
Text Query ◽  
The Mind ◽  
The Brain

The main aim of the proposed paper is to search the information using brain waves instead of searching using the text query. Electroencephalography (EEG) is an electrophysiological checking strategy to record the electrical activity of the mind. EEG estimates voltage changes coming about because of ionic current inside the neurons of the mind. EEG alludes to the account of the mind’s unconstrained electrical movement over a time frame, as recorded from different nodes put on the scalp. For training, When the user has read a question, the brain waves are recorded with the help of sensors like Neurosky device in the form of EEG signals values like Alpha, Beta, and Gamma and are stored in a dataset. The brainwaves of different persons are recorded for different questions and are stored. The features are reduced using PCA and the centroid of the values are calculated using the K-means clustering algorithm. For testing, when the user thinks about a question in the list, the brain waves are recorded and compared with the values available in the dataset. Using KNN Algorithm, the proposed system outputs the respective question which will be submitted to the search engine. K-Means clustering algorithm is used to calculate the cluster centroid. Once the centroid is calculated for each question, we plot each centroid in a 2-D plane. For a random question from the pool of existing questions, we use the KNN algorithm to find the nearest match. When the match is found, the question corresponding to it is submitted to the search engine.

scholarly journals Wavelet-Based Artifact Identification and Separation Technique for EEG Signals during Galvanic Vestibular Stimulation

2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
Mani Adib ◽  
Edmond Cretu
Keyword(s):  
Current Distribution ◽  
Adaptive Filters ◽  
Vestibular Stimulation ◽  
Decomposition Methods ◽  
Galvanic Vestibular Stimulation ◽  
Eeg Signals ◽  
Time Series Regression ◽  
Regression Methods ◽  
Main Challenge ◽  
Novel Method

We present a new method for removing artifacts in electroencephalography (EEG) records during Galvanic Vestibular Stimulation (GVS). The main challenge in exploiting GVS is to understand how the stimulus acts as an input to brain. We used EEG to monitor the brain and elicit the GVS reflexes. However, GVS current distribution throughout the scalp generates an artifact on EEG signals. We need to eliminate this artifact to be able to analyze the EEG signals during GVS. We propose a novel method to estimate the contribution of the GVS current in the EEG signals at each electrode by combining time-series regression methods with wavelet decomposition methods. We use wavelet transform to project the recorded EEG signal into various frequency bands and then estimate the GVS current distribution in each frequency band. The proposed method was optimized using simulated signals, and its performance was compared to well-accepted artifact removal methods such as ICA-based methods and adaptive filters. The results show that the proposed method has better performance in removing GVS artifacts, compared to the others. Using the proposed method, a higher signal to artifact ratio of −1.625 dB was achieved, which outperformed other methods such as ICA-based methods, regression methods, and adaptive filters.

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 Design of MRI structured spiking neural networks and learning algorithms for personalized modelling, analysis, and prediction of EEG signals

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Samaneh Alsadat Saeedinia ◽  
Mohammad Reza Jahed-Motlagh ◽  
Abbas Tafakhori ◽  
Nikola Kasabov
Keyword(s):  
Information Integration ◽  
Learning Algorithm ◽  
Network Models ◽  
Eeg Signals ◽  
Neural Network Models ◽  
Modeling Analysis ◽  
Modelling Analysis ◽  
Eeg Data ◽  
Novel Method ◽  
Spike Time Dependent Plasticity

AbstractThis paper proposes a novel method and algorithms for the design of MRI structured personalized 3D spiking neural network models (MRI-SNN) for a better analysis, modeling, and prediction of EEG signals. It proposes a novel gradient-descent learning algorithm integrated with a spike-time-dependent-plasticity algorithm. The models capture informative personal patterns of interaction between EEG channels, contrary to single EEG signal modeling methods or to spike-based approaches which do not use personal MRI data to pre-structure a model. The proposed models can not only learn and model accurately measured EEG data, but they can also predict signals at 3D model locations that correspond to non-monitored brain areas, e.g. other EEG channels, from where data has not been collected. This is the first study in this respect. As an illustration of the method, personalized MRI-SNN models are created and tested on EEG data from two subjects. The models result in better prediction accuracy and a better understanding of the personalized EEG signals than traditional methods due to the MRI and EEG information integration. The models are interpretable and facilitate a better understanding of related brain processes. This approach can be applied for personalized modeling, analysis, and prediction of EEG signals across brain studies such as the study and prediction of epilepsy, peri-perceptual brain activities, brain-computer interfaces, and others.

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