Perda da Soberania Nacional, Translational Education, Jornada Pedagógica na educação de bebês - EEG Data Analysis, EEG MicroStates ERP P300 N400 ICA, FFT, Wavelets, LORETA
Perda da Soberania Nacional, Translational Education, Jornada Pedagógica na educação de bebês - EEG Data Analysis, EEG MicroStates ERP P300 N400 ICA, FFT, Wavelets, LORETA
First-Person Consciousness of our Babies
EEG data analysis:
EEG Microstates:
Microstates: Brief segments of EEG data that represent stable distributions of scalp electrical activity. They reflect different states of neuronal network activity in the brain.
Analysis: Microstate analysis provides insight into short-term brain dynamics, with potential applications in studying normal brain function, cognitive processes, and various neuropsychiatric disorders.
ERP (Event-Related Potentials):
P300: An ERP component occurring around 300 milliseconds post-stimulus, often associated with attention and working memory. P300 is usually elicited during "oddball" tasks, where subjects respond to infrequent targets amid frequent non-targets.
N400: Another ERP component, N400 is related to language processing, specifically the processing of words and their semantic congruity in a given context.
ICA (Independent Component Analysis):
Overview: ICA is a computational method used to separate a multivariate signal into additive, independent (non-Gaussian) components. It's often used to isolate sources of EEG activity and remove artifacts.
Application: In EEG analysis, ICA can be used to separate and exclude artifacts (like eye movements or muscle activity) from true neural signals.
FFT (Fast Fourier Transform):
Overview: FFT is a widely-used technique for converting a signal from its time domain to its frequency domain. It’s useful in analyzing the different frequencies present in EEG signals.
Application: FFT is used to explore the power spectrum of EEG data, helping researchers identify dominant frequencies and rhythms (like alpha, beta, theta, and gamma waves).
Wavelets:
Overview: Wavelet transform allows for the analysis of signals at multiple scales or resolutions. It’s particularly useful for non-stationary signals where frequency components might change over time.
Application: In EEG, wavelet analysis is often used for time-frequency analysis, providing insights into the dynamic changes in EEG signals during different cognitive tasks or states.
LORETA (Low Resolution Electromagnetic Tomography):
Overview: LORETA is a computational approach used to localize the source of EEG activity within the brain. It estimates the three-dimensional distribution of electrically active neurons.
Application: It's employed in clinical and research settings to localize the sources of specific EEG patterns or ERP components in the brain.
Considerations in EEG Analysis:
Preprocessing: Includes filtering, artifact rejection, and segmenting the EEG data.
Statistical Analysis: Involves comparing EEG metrics (like power spectral density, coherence, etc.) across different conditions or groups.
Interpretation: Requires careful consideration of the limitations and assumptions inherent to each analysis technique.
Conclusion:
Each of the above methods and approaches offers unique insights into the brain’s electrical activity, serving different purposes in EEG data analysis. Researchers and practitioners select these techniques based on the specific goals and context of their study, often using a combination of methods for comprehensive EEG analysis. Understanding and appropriately applying these techniques are crucial for valid and reliable interpretations of EEG data in neuroscience research.
IV Encontro Anual da Rede CpE - 2021 | Translational Education
O que é Poda Neuronal? Ferramentas para medir o desenvolvimento infantil e a qualidade no ensino na pré-escola.Vivemos a era da cultura digital. O conhecimento, muitas vezes, é intermediado pela tecnologia da informação e da comunicação Implications of neuroscience in the early years.Exercícios físicos regulares, aumento de atenção e motivação,Habilidades socioemocionais ou competências socioemocionais para lidar com as próprias emoções Etapa prelingüistica
Translational Education | MultiRio - Jornada Pedagógica 2/3
MultiRio - Jornada Pedagógica 2/3 - MAR 21
Translational Education | MultiRio - Jornada Pedagógica 3/3
MultiRio - Jornada Pedagógica 3/3 - MAR 21
EEG Data Analysis I | EEG Data Analysis
EEG Data AnalysisAnalyzer:Analysis software for EEG ERP P300 N400 research, Video integration, Raw Data Inspection, interactive ICA, FFT, Wavelets, LORETA, MR and CB artifact correction, Integration for eye-tracking data,CSD Current Source Density, Grand Average, Grand Segmentation, ERS/ERD Event-related synchronization and desynchronization, FFT Fast Fourier Transform, FFT Inverse, ICA Independent Component Analysis, Inverse ICA,Butterworth filter, Linear Derivation, LORETA for source analysis, Ocular Correction ICA based on ICA, PCA Principal Component Analysis, Segmentation,Topographic Interpolation, t-Test paired and unpaired t-Tests, Wavelets, Wavelet ExtractionFunctionalBESA Research:Data review and processing for reviewing and processing of your EEG or MEG data. Digital filtering: high, low, and narrow band pass, notch. Interpolation from recorded to virtual and source channels.Automated EOG and EKG artifact detection and correction. Advanced user-defined instantaneous artifact correction. Spectral analysis: FFT, DSA, power and phase mapping. Independent Component Analysis (ICA): Decomposition of EEG/MEG data into ICA components that can be used for artifact correction and as spatial sources in the source analysis window. Connectivity analysis, a unique feature for viewing brain activity, transforms surface signals into brain activity using source montages derived from multiple source models or beamformer imaging. This allows displaying ongoing EEG/MEG, single epochs, and averages with much higher spatial resolution. Source montages and 3D whole-head mapping. ERP analysis and averaging. Source localization and source imaging. Individual MRI and fMRI integration with BESA MRI and BrainVoyager. Source coherence and time-frequency analysis
EEG Data Analysis II | EEG Data Analysis
Perda da Soberania Nacional | Jackson Cionek
Sua Percepção é Sua Realidade
Aprenda a mudar sua Percepção
Sua percepção possui 6 variáveis fundamentais:
1 - Conectoma Cerebral;
2 - Nível de atividade de seus 11 sentidos;
3 - Memórias pré-ativadas;
4 - O nível de atenção;
5 - O estímulo apresentado;
6 - A consciência é Viés da Percepção.
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 | 00:00:00 - 02:54:00 O que é Poda Neuronal?Ferramentas para medir o desenvolvimento infantil e a qualidade no ensino na pré-escola.Vivemos a era da cultura digital. O conhecimento, muitas vezes, é intermediado pela tecnologia da informação e da comunicaçãoImplications of neuroscience in the early years.Exercícios físicos regulares, aumento de atenção e motivação,Habilidades socioemocionais ou competências socioemocionais para lidar com as próprias emoçõesEtapa prelingüistica |
 | 02:54:00 - 06:09:00 MultiRio - Jornada Pedagógica 2/3 - MAR 21 |
 | 06:09:00 - 09:28:00 MultiRio - Jornada Pedagógica 3/3 - MAR 21 |
 | 09:28:00 - 13:05:00 EEG Data AnalysisAnalyzer:Analysis software for EEG ERP P300 N400 research, Video integration, Raw Data Inspection, interactive ICA, FFT, Wavelets, LORETA, MR and CB artifact correction, Integration for eye-tracking data,CSD Current Source Density, Grand Average, Grand Segmentation, ERS/ERD Event-related synchronization and desynchronization, FFT Fast Fourier Transform, FFT Inverse, ICA Independent Component Analysis, Inverse ICA,Butterworth filter, Linear Derivation, LORETA for source analysis, Ocular Correction ICA based on ICA, PCA Principal Component Analysis, Segmentation,Topographic Interpolation, t-Test paired and unpaired t-Tests, Wavelets, Wavelet ExtractionFunctionalBESA Research:Data review and processing for reviewing and processing of your EEG or MEG data. Digital filtering: high, low, and narrow band pass, notch. Interpolation from recorded to virtual and source channels.Automated EOG and EKG artifact detection and correction. Advanced user-defined instantaneous artifact correction. Spectral analysis: FFT, DSA, power and phase mapping. Independent Component Analysis (ICA): Decomposition of EEG/MEG data into ICA components that can be used for artifact correction and as spatial sources in the source analysis window. Connectivity analysis, a unique feature for viewing brain activity, transforms surface signals into brain activity using source montages derived from multiple source models or beamformer imaging. This allows displaying ongoing EEG/MEG, single epochs, and averages with much higher spatial resolution. Source montages and 3D whole-head mapping. ERP analysis and averaging. Source localization and source imaging. Individual MRI and fMRI integration with BESA MRI and BrainVoyager. Source coherence and time-frequency analysis |
 | 13:05:00 - 17:46:00 EEG Data AnalysisAnalyzer:Analysis software for EEG ERP P300 N400 research, Video integration, Raw Data Inspection, interactive ICA, FFT, Wavelets, LORETA, MR and CB artifact correction, Integration for eye-tracking data,CSD Current Source Density, Grand Average, Grand Segmentation, ERS/ERD Event-related synchronization and desynchronization, FFT Fast Fourier Transform, FFT Inverse, ICA Independent Component Analysis, Inverse ICA,Butterworth filter, Linear Derivation, LORETA for source analysis, Ocular Correction ICA based on ICA, PCA Principal Component Analysis, Segmentation,Topographic Interpolation, t-Test paired and unpaired t-Tests, Wavelets, Wavelet ExtractionFunctionalBESA Research:Data review and processing for reviewing and processing of your EEG or MEG data. Digital filtering: high, low, and narrow band pass, notch. Interpolation from recorded to virtual and source channels.Automated EOG and EKG artifact detection and correction. Advanced user-defined instantaneous artifact correction. Spectral analysis: FFT, DSA, power and phase mapping. Independent Component Analysis (ICA): Decomposition of EEG/MEG data into ICA components that can be used for artifact correction and as spatial sources in the source analysis window. Connectivity analysis, a unique feature for viewing brain activity, transforms surface signals into brain activity using source montages derived from multiple source models or beamformer imaging. This allows displaying ongoing EEG/MEG, single epochs, and averages with much higher spatial resolution. Source montages and 3D whole-head mapping. ERP analysis and averaging. Source localization and source imaging. Individual MRI and fMRI integration with BESA MRI and BrainVoyager. Source coherence and time-frequency analysis |
 | 17:46:00 - 23:59:00 Sua Perecepção é Sua Realidade - Aprenda a mudar sua PercepçãoSua percepção possui 5 variáveis fundamentais:1 - Conectoma Cerebral;2 - Nível de atividade de seus 11 sentidos;3 - Memórias pre-ativadas;4 - O nível de atenção;5 - O estímulo apresentado;Veja os links abaixo:Pós Verdade e a Morte de Deus OriginalEstado Responsável Laico Democrático e de DireitoDeus a Verdade está entre nós - Nunca em nósReligare Politics and NeuroscienceConsciousness in First-PersonSer Humano é Ser Racional?Deus Laico Democrático e de DireitoConsciência no Corpo, Alma e Espírito sem Vida após a MortePerda da Soberania Nacional |
