EEG Data Analysis EEG ERP P300 N400 ICA, FFT, Wavelets, LORETA - NeuroEconomics, Neuromarketing for Decision Making

NeuroEconomics Neuromarketing for Decision Making

No for Universal Income - SIM para o  DREX ser nosso Rendimento Brasil

SIM para o  DREX ser nosso Rendimento Brasil

A análise de dados de EEG (Eletroencefalograma), especialmente no contexto de potenciais evocados (como P300 e N400), e técnicas como ICA (Independent Component Analysis), FFT (Fast Fourier Transform), Wavelets e LORETA (Low Resolution Electromagnetic Tomography), é fundamental em campos como a Neuroeconomia e o Neuromarketing. Essas técnicas são usadas para entender melhor os processos de tomada de decisão no cérebro. 

EEG e Potenciais Evocados:

P300: Um potencial evocado relacionado com a atenção e a tomada de decisão. O P300 é geralmente observado quando uma pessoa detecta um estímulo raro ou significativo.

N400: Associado à processamento da linguagem e semântica. O N400 é observado quando uma palavra em uma frase não se encaixa no contexto esperado.

ICA (Independent Component Analysis): Esta técnica é utilizada para separar sinais misturados em componentes independentes. No contexto do EEG, o ICA pode ajudar a isolar sinais de interesse (como aqueles associados a processos cognitivos específicos) de outros sinais ou ruídos.

FFT (Fast Fourier Transform): Uma ferramenta matemática usada para transformar sinais do domínio do tempo para o domínio da frequência. Isso é útil para analisar as diferentes frequências de ondas cerebrais presentes em dados de EEG, como alfa, beta, delta, e teta.

Wavelets: Uma técnica que permite a análise de sinais em diferentes escalas de frequência e tempo simultaneamente. Isso é particularmente útil para analisar sinais não estacionários, como os dados de EEG, que podem mudar ao longo do tempo.

LORETA: Uma técnica de imagem cerebral que estima a localização tridimensional da atividade cerebral subjacente aos sinais de EEG, oferecendo uma visão mais detalhada da origem dos sinais cerebrais.

Aplicação em Neuroeconomia e Neuromarketing:

Neuroeconomia: Utiliza essas técnicas para entender como as pessoas tomam decisões econômicas, incluindo avaliação de riscos, recompensas, e o processamento de informações complexas.

Neuromarketing: Aplica conhecimentos de neurociência para entender como os consumidores reagem a produtos, marcas e publicidade. Por exemplo, o P300 pode ser usado para avaliar a atenção dos consumidores a anúncios, e o N400 pode ajudar a entender como eles processam informações sobre produtos.

Benefícios e Limitações: Essas técnicas oferecem insights valiosos sobre o funcionamento cerebral, mas também têm limitações. Por exemplo, a resolução espacial do EEG é limitada, e as interpretações dos dados devem ser feitas com cautela. Além disso, questões éticas são especialmente pertinentes no neuromarketing, onde o objetivo é influenciar o comportamento do consumidor.

Em resumo, a análise de dados de EEG, utilizando técnicas como ICA, FFT, Wavelets e LORETA, é extremamente valiosa para a pesquisa em neuroeconomia e neuromarketing, proporcionando uma compreensão mais profunda dos processos neurais subjacentes à tomada de decisão.

BESA 1/2 | 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 Extraction FunctionalBESA 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

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BESA 1/2 | EEG Data Analysis 00:00:00 - 02:32:00 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

BESA 2/2 | EEG Data Analysis 02:32:00 - 06:05:00 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

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