Initial Analysis of the EEG Signal Processing Methods for Studying Correlations between Muscle and Brain Activity

2010 ◽  
pp. 220-225 ◽  
Author(s):  
Helena Valentová ◽  
Jan Havlík
Keyword(s):  
Signal Processing ◽  
Brain Activity ◽  
Eeg Signal ◽  
Processing Methods ◽  
Initial Analysis ◽  
Signal Processing Methods ◽  
Eeg Signal Processing

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.

Study on Arithmetic Library of Signal Process Based on PDA

2011 ◽  
Vol 282-283 ◽  
pp. 326-329
Author(s):  
De Rong Jiang ◽  
Jing Hai Yin ◽  
Jian Feng Hu
Keyword(s):  
Signal Processing ◽  
Arithmetic Function ◽  
Eeg Signal ◽  
Signal Process ◽  
Processing Methods ◽  
C Language ◽  
Signal Processing Methods ◽  
Net Framework

OBJECTIVE: To find a method which can establish arithmetic library of signal process based on PDA, and apply the method to process the EEG signal, and though function of the Mathworks Matlab R2010a is very large, there is currently no MATLAB version for PDA, In order to surpass the limited arithmetic on PDA, the arithmetic of Mathworks Matlab R2010a must be converted to C# language. METHOD: Several signal processing methods can convert the arithmetic function in Mathworks Matlab R2010a to arithmetic library of signal process with C# language, which is resort to every line function of Mathworks Matlab R2010a. RESULT: Three signal process functions have been converted to arithmetic library of signal process which can run on .net framework. CONCLUSION: Because of the limit of memory and instructor on PDA, there is impossible that the arithmetic of Mathworks Matlab R2010a run on PDA, therefore the arithmetic has been converted to arithmetic library of signal process with C# language.

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 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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