scholarly journals SCHIZOPHRENIA DETECTION USING EEG SIGNAL PROCESSING TO SHOW THE NONLINEAR STRUCTURE OF THE BRAIN ELECTRICAL ACTIVITY

2019 ◽  
Vol 10 (3) ◽  
pp. 41-47
Author(s):  
Kabari Ledisi G. ◽  
Keyword(s):  
Signal Processing ◽  
Electrical Activity ◽  
Brain Electrical Activity ◽  
Eeg Signal ◽  
Nonlinear Structure ◽  
The Brain ◽  
Eeg Signal Processing

scholarly journals EEG-Based BCI System to Control Prosthesis's Finger Movements

2020 ◽  
Author(s):  
Sofien Gannouni ◽  
Kais Belwafi ◽  
Hatim Aboalsamh ◽  
Basel Alebdi ◽  
Yousef Almassad ◽  
...  
Keyword(s):  
Signal Processing ◽  
Learning Strategy ◽  
Binary Classification ◽  
Severe Disabilities ◽  
Assistive Technologies ◽  
Eeg Signal ◽  
Finger Movements ◽  
One Class Classifier ◽  
The Brain ◽  
Eeg Signal Processing

Abstract Background: The advances in assistive technologies will go a long way towards restoring the mobility of paralyzed and/or amputated limbs. In this paper, we propose a system that adopts the brain-computer interface ( BCI ) technology to control prosthetic fingers by thoughts. To predict the movements of each finger, a complex EEG signal processing algorithms should be applied in order to remove the outliers , to extract feature, to discriminate between the fingers and to control prosthesis's finger. The proposed method discriminates between the five human fingers. So a multi -classification problem based on ensemble of one class-classifier is applied where each classifier predicts the intention to move one finger. At the end, an adapted machine learning strategy is proposed to predict movements of multiple fingers at the same time. Results: The sensitive regions of the brain related to finger movements are identified and located. The proposed EEG signal processing chain, based on ensemble of one class-classifier, reach a classification accuracy of 81 \ % for five subjects according to the online approach. Unlike most of the existing prototypes that allow to control only one single finger and to perform only one movement at a time by the dedicated finger, our proposed system will enable multiple fingers to perform movements simultaneously. Despite that the proposed system classifies a five tasks, the obtained accuracy is too high compared to a binary classification system. Conclusion: The proposed system contributes to the advancement of a prosthetic allowing people with severe disabilities to do the daily tasks easily.

scholarly journals Demystifying signal processing techniques to extract resting-state EEG features for psychologists

2020 ◽  
Vol 6 (3) ◽  
pp. 189-209 ◽  
Author(s):  
Zhenjiang Li ◽  
Libo Zhang ◽  
Fengrui Zhang ◽  
Ruolei Gu ◽  
Weiwei Peng ◽  
...  
Keyword(s):  
Signal Processing ◽  
Resting State ◽  
Neural Dynamics ◽  
Complex Signal ◽  
Eeg Signal ◽  
Microstate Analysis ◽  
Processing Techniques ◽  
The Brain ◽  
Signal Processing Techniques ◽  
Eeg Signal Processing

Electroencephalography (EEG) is a powerful tool for investigating the brain bases of human psychological processes non‐invasively. Some important mental functions could be encoded by resting‐state EEG activity; that is, the intrinsic neural activity not elicited by a specific task or stimulus. The extraction of informative features from resting‐state EEG requires complex signal processing techniques. This review aims to demystify the widely used resting‐state EEG signal processing techniques. To this end, we first offer a preprocessing pipeline and discuss how to apply it to resting‐state EEG preprocessing. We then examine in detail spectral, connectivity, and microstate analysis, covering the oft‐used EEG measures, practical issues involved, and data visualization. Finally, we briefly touch upon advanced techniques like nonlinear neural dynamics, complex networks, and machine learning.

scholarly journals Methods for the Analysis of EEG signals: A Review

2021 ◽  
Vol 9 (9) ◽  
pp. 873-876
Author(s):  
Malika Garg
Keyword(s):  
Signal Processing ◽  
Feature Extraction ◽  
Electrical Activity ◽  
Signal Analysis ◽  
Eeg Signal ◽  
Eeg Analysis ◽  
Eeg Signals ◽  
Processing Techniques ◽  
The Brain ◽  
Eeg Signal Analysis

Abstract: Electroencephalography (EEG) helps to predict the state of the brain. It tells about the electrical activity going on in the brain. Difference of the surface potential evolved from various activities get recorded as EEG. The analysis of these EEG signals is of utmost importance to solve the problems related to the brain. Signal pre-processing, feature extraction and classification are the main steps of the EEG signal analysis. In this article we discussed various processing techniques of EEG signals. Keywords: EEG, analysis, signal processing, feature extraction, classification

scholarly journals Complexity Measures for Normal and Epileptic EEG Signals using ApEn, SampEn and SEN

2013 ◽  
pp. 95-101
Author(s):  
Yatindra Kumar ◽  
M. L. Dewal
Keyword(s):  
Time Series ◽  
Signal Processing ◽  
Time Series Data ◽  
Epileptic Seizures ◽  
Series Data ◽  
Eeg Signal ◽  
Complexity Measures ◽  
Non Stationary Signal ◽  
The Brain ◽  
Eeg Signal Processing

There are numerous applications of EEG signal processing such as monitoring alertness, coma, and brain death, controlling an aesthesia, investigating epilepsy and locating seizure origin, testing epilepsy drug effects, monitoring the brain development, and investigating mental disorders; where data size is too long and requires long time to observe the data by clinician or neurologist. EEG signal processing techniques can be used effectively in such applications. The configuration of the signal waveform may contain valuable and useful information about the different state of the brain since biological signal is highly random in both time and frequency domain. Thus computerized analysis is necessary. Being a non-stationary signal, suitable analysis is essential for EEG to differentiate the normal EEG and epileptic seizures. The importance of entropy based features to recognize the normal EEGs, and ictal as well as interictal epileptic seizures. Three features, such as, Approximate entropy, Sample entropy, and Spectral entropy are used to take out the quantitative entropy features from the given EEG time series data of various time frames of 0.88s, and 1s .Average value of entropies for epileptic time series is less than non epileptic time series.

scholarly journals EEG-Based BCI System to Detect Fingers Movements

2020 ◽  
Vol 10 (12) ◽  
pp. 965
Author(s):  
Sofien Gannouni ◽  
Kais Belwafi ◽  
Hatim Aboalsamh ◽  
Ziyad AlSamhan ◽  
Basel Alebdi ◽  
...  
Keyword(s):  
Signal Processing ◽  
Learning Strategy ◽  
Binary Classification ◽  
Classification Problem ◽  
Severe Disabilities ◽  
Assistive Technologies ◽  
Eeg Signal ◽  
Intention To Move ◽  
The Brain ◽  
Eeg Signal Processing

The advancement of assistive technologies toward the restoration of the mobility of paralyzed and/or amputated limbs will go a long way. Herein, we propose a system that adopts the brain-computer interface technology to control prosthetic fingers with the use of brain signals. To predict the movements of each finger, complex electroencephalogram (EEG) signal processing algorithms should be applied to remove the outliers, extract features, and be able to handle separately the five human fingers. The proposed method deals with a multi-class classification problem. Our machine learning strategy to solve this problem is built on an ensemble of one-class classifiers, each of which is dedicated to the prediction of the intention to move a specific finger. Regions of the brain that are sensitive to the movements of the fingers are identified and located. The average accuracy of the proposed EEG signal processing chain reached 81% for five subjects. Unlike the majority of existing prototypes that allow only one single finger to be controlled and only one movement to be performed at a time, the system proposed will enable multiple fingers to perform movements simultaneously. Although the proposed system classifies five tasks, the obtained accuracy is too high compared with a binary classification system. The proposed system contributes to the advancement of a novel prosthetic solution that allows people with severe disabilities to perform daily tasks in an easy manner.

scholarly journals Implementación de métodos de procesamiento de señales EEG para aplicaciones de comunicación y control

2018 ◽  
pp. 25-34
Keyword(s):  
Signal Processing ◽  
San Antonio ◽  
Eeg Signal ◽  
Human Function ◽  
Control Application ◽  
Signal Processing Methods ◽  
And Control ◽  
The Brain ◽  
Eeg Signal Processing ◽  
Control Implementation

Implementación de métodos de procesamiento de señales EEG para aplicaciones de comunicación y control Implementation of EEG signal processing methods for communication and control application Shirley Cordova Villar1, Willian A. Perez Oviedo1, Avid Román Gonzalez12 1 Universidad Nacional San Antonio Abad del Cusco 2 TELECOM ParisTech, 46 rue Barrault, 75013 – Paris, Francia DOI: https://doi.org/10.33017/RevECIPeru2013.0004/  Resumen La interface cerebro-computador (ICC) es un instrumento de comunicación entre la mente o la función cognitiva del ser humano y el ambiente externo, esta función mental es creada por el cerebro; las señales son capturadas, pre-procesadas y puestas en un clasificador. Este artículo tiene como objetivo la implementación y comparación de algoritmos basados en diferentes métodos de procesamiento de señales EEG para aplicaciones ICC que actualmente existen, para encontrar que método proporciona mejores resultados. Descriptores: ICC, EEG, procesamiento de señales, parámetros AAR, clasificación, comunicación y control, pensamiento Abstract   The Brain Computer Interface (BCI) is a communication instrument between mental or cognitive human function and the external environment, this mental function is created by the brain; the signals are captured, pre-processed and put into a classifier. This article aims to implement and compare the algorithms based on different methods of EEG signal processing for BCI applications that currently exist, in order to find methods whose algorithms provide better results. Keywords: BCI, EEG, signal processing, AAR parameters, classification, communication and control, thought

Fit Vs Faint: Assessing Work Causation in “Idiopathic Fall” Cases

2014 ◽  
Vol 19 (5) ◽  
pp. 3-12
Author(s):  
Lorne Direnfeld ◽  
David B. Torrey ◽  
Jim Black ◽  
LuAnn Haley ◽  
Christopher R. Brigham
Keyword(s):  
Blood Flow ◽  
Electrical Activity ◽  
Loss Of Consciousness ◽  
Brain Electrical Activity ◽  
Medical Terminology ◽  
Scientific Analysis ◽  
Medical Causation ◽  
The Individual ◽  
The Brain ◽  
Current Science

Abstract When an individual falls due to a nonwork-related episode of dizziness, hits their head and sustains injury, do workers’ compensation laws consider such injuries to be compensable? Bearing in mind that each state makes its own laws, the answer depends on what caused the loss of consciousness, and the second asks specifically what happened in the fall that caused the injury? The first question speaks to medical causation, which applies scientific analysis to determine the cause of the problem. The second question addresses legal causation: Under what factual circumstances are injuries of this type potentially covered under the law? Much nuance attends this analysis. The authors discuss idiopathic falls, which in this context means “unique to the individual” as opposed to “of unknown cause,” which is the familiar medical terminology. The article presents three detailed case studies that describe falls that had their genesis in episodes of loss of consciousness, followed by analyses by lawyer or judge authors who address the issue of compensability, including three scenarios from Arizona, California, and Pennsylvania. A medical (scientific) analysis must be thorough and must determine the facts regarding the fall and what occurred: Was the fall due to a fit (eg, a seizure with loss of consciousness attributable to anormal brain electrical activity) or a faint (eg, loss of consciousness attributable to a decrease in blood flow to the brain? The evaluator should be able to fully explain the basis for the conclusions, including references to current science.

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