Influence of a BCI neurofeedback videogame in children with ADHD. Quantifying the brain activity through an EEG signal processing dedicated toolbox

2016 ◽  
Author(s):  
Diego Zamora Blandon ◽  
John Edison Munoz ◽  
David Sebastian Lopez ◽  
Oscar Henao Gallo
Keyword(s):  
Signal Processing ◽  
Brain Activity ◽  
Eeg Signal ◽  
Children With Adhd ◽  
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.

Application of the Hilbert_Huang Transform and Wavelet Transform in the EEG Processing

2013 ◽  
Vol 756-759 ◽  
pp. 1753-1757
Author(s):  
Gui Xin Zhang ◽  
Ping Dong Wu ◽  
Man Ling Huang
Keyword(s):  
Wavelet Transform ◽  
Brain Activity ◽  
Eeg Signal ◽  
Hilbert Huang Transform ◽  
Signal Processing Method ◽  
Communication Method ◽  
Machine Interface ◽  
And Control ◽  
The Brain ◽  
Eeg Signal Processing

Brain-Machine Interface (BMI) could make people control machine through EEG which is produced by the brain activity, and it provide a new communication method between human and machine. The research for BMI will extend the ability of communication and control the environment and machine. The key point of the BMI is how to abstract and distinguish different EEG characters. Therefore, EEG signal processing method is the emphasis of BMI. Wavelet Transform and Hilbert-Huang Transform are used to analyze the EEG signal in this paper. The results indicate that these two methods could abstract the main characters of the EEG, but the Hilbert-Huang Transform could express the distributing status in the time and frequency aspect of the EEG more accurately, because it produces the self-adaptive basis according the data, and obtain the local and instantaneous frequency of the EEG.

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.

Tensor analysis of electroencephalogram signal for localization of event-related potentials

2021 ◽  
Author(s):  
Saeed Pouryazdian
Keyword(s):  
Signal Processing ◽  
Brain Activity ◽  
Tensor Analysis ◽  
Order Selection ◽  
Eeg Signal ◽  
Model Order Selection ◽  
Model Order ◽  
Time Frequency ◽  
Related Potentials ◽  
Eeg Signal Processing

Electroencephalogram (EEG) is widely used for monitoring, diagnosis purposes and also for study of brains physiological, mental and functional abnormalities. EEG is known to be a high-dimensional signal in which processing of information by the brain is reected in dynamical changes of the electrical activity in time, frequency, and space. EEG signal processing tends to describe and quantify these variations into functions with known spatio-temporal-spectral properties or at least easier to characterize. Multi-channel EEG recordings naturally include multiple modes. Matrix analysis, via stacking or concatenating other modes with the retained two modes, has been extensively used to represent and analyze the EEG data. On the other hand, Multi-way (tensor) analysis techniques keep the structure of the data, and by analyzing more dimensions simultaneously, summarize the data into more interpretable components. This work presents a generalized multi-way array analysis methodology in pattern classification systems as related to source separation and discriminant feature selection in EEG signal processing problems. Analysis of ERPs, as one of the main categories of EEG signals, requires systems that can exploit the variation of the signals in different contextual domains in order to reveal the hidden structures in the data. Temporal, spectral, spatial, and subjects/experimental conditions of multi-channel ERP signals are exploited here to generate three-way and four-way ERP tensors. Two key elements of this framework are the Time-Frequency representation (TFR) and CANDECOMP/PARAFAC model order selection techniques we incorporate for analysis. Here, we propose a fully data-driven TFR scheme, via combining the Empirical Mode Decomposition and Reassignment method, which yields a high resolution and cross-term free TFR. Furthermore, we develop a robust and effective model order selection scheme that outperforms conventional techniques in mid and low SNRs (i.e. 0􀀀10 dB) with a better Probability of Detection (PoD) and almost no extra computational overhead after the CANDECOMP/PARAFAC decomposition. ERP tensor can be regarded as a mixture that includes different kinds of brain activity, artifacts, interference, and noise. Using this framework, the desired brain activity could be extracted out from the mixture. The extracted signatures are then translated for different applications in brain-computer interface and cognitive neuroscience.

scholarly journals Tensor analysis of electroencephalogram signal for localization of event-related potentials

2021 ◽  
Author(s):  
Saeed Pouryazdian
Keyword(s):  
Signal Processing ◽  
Brain Activity ◽  
Tensor Analysis ◽  
Order Selection ◽  
Eeg Signal ◽  
Model Order Selection ◽  
Model Order ◽  
Time Frequency ◽  
Related Potentials ◽  
Eeg Signal Processing

Electroencephalogram (EEG) is widely used for monitoring, diagnosis purposes and also for study of brains physiological, mental and functional abnormalities. EEG is known to be a high-dimensional signal in which processing of information by the brain is reected in dynamical changes of the electrical activity in time, frequency, and space. EEG signal processing tends to describe and quantify these variations into functions with known spatio-temporal-spectral properties or at least easier to characterize. Multi-channel EEG recordings naturally include multiple modes. Matrix analysis, via stacking or concatenating other modes with the retained two modes, has been extensively used to represent and analyze the EEG data. On the other hand, Multi-way (tensor) analysis techniques keep the structure of the data, and by analyzing more dimensions simultaneously, summarize the data into more interpretable components. This work presents a generalized multi-way array analysis methodology in pattern classification systems as related to source separation and discriminant feature selection in EEG signal processing problems. Analysis of ERPs, as one of the main categories of EEG signals, requires systems that can exploit the variation of the signals in different contextual domains in order to reveal the hidden structures in the data. Temporal, spectral, spatial, and subjects/experimental conditions of multi-channel ERP signals are exploited here to generate three-way and four-way ERP tensors. Two key elements of this framework are the Time-Frequency representation (TFR) and CANDECOMP/PARAFAC model order selection techniques we incorporate for analysis. Here, we propose a fully data-driven TFR scheme, via combining the Empirical Mode Decomposition and Reassignment method, which yields a high resolution and cross-term free TFR. Furthermore, we develop a robust and effective model order selection scheme that outperforms conventional techniques in mid and low SNRs (i.e. 0􀀀10 dB) with a better Probability of Detection (PoD) and almost no extra computational overhead after the CANDECOMP/PARAFAC decomposition. ERP tensor can be regarded as a mixture that includes different kinds of brain activity, artifacts, interference, and noise. Using this framework, the desired brain activity could be extracted out from the mixture. The extracted signatures are then translated for different applications in brain-computer interface and cognitive neuroscience.

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

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