Decision Making with EEG Data Analysis, EEG ERP P300 N400 ICA FFT Wavelets LORETA EEG MicroStates - Neuromarketing for childs and NeuroEconomics, Social Value of Money
Decision Making with EEG Data Analysis, EEG ERP P300 N400 ICA FFT Wavelets LORETA EEG MicroStates - Neuromarketing for childs and NeuroEconomics, Social Value of Money
EEG ERP LORETA
The combination of EEG data analysis and decision making is a fascinating field with applications in various domains, including neuromarketing, neuroeconomics, and social sciences. EEG (Electroencephalography) allows us to measure electrical brain activity non-invasively, providing insights into cognitive processes and decision-making mechanisms. Several EEG components, such as ERP (Event-Related Potentials) like P300 and N400, can be analyzed to understand the brain's response to specific stimuli or events.
EEG ERP Components:
The P300 component is an EEG waveform associated with attention and cognitive processing. It is often used in brain-computer interfaces and can be analyzed to assess the brain's response to certain stimuli or decisions.
N400: The N400 component is an ERP that reflects semantic processing and is commonly used in language-related studies. It can provide insights into how individuals interpret and understand different stimuli.
Independent Component Analysis (ICA):
ICA is a signal processing technique used to separate mixed EEG signals into their constituent components. It helps identify the underlying brain sources contributing to the recorded EEG activity, enabling researchers to isolate and analyze specific neural processes.
FFT (Fast Fourier Transform) and Wavelet Analysis:
FFT and wavelet analysis are signal processing methods used to examine the frequency content of EEG signals. These techniques help identify patterns, oscillatory activity, and spectral changes in the brain's electrical signals.
LORETA (Low-Resolution Electromagnetic Tomography):
LORETA is a method used to estimate the three-dimensional distribution of electrical sources within the brain based on scalp EEG data. It can provide spatial information about brain activity, helping to localize the sources of neural activity.
EEG microstates refer to short-lasting, quasi-stable patterns of scalp potential topographies. Analyzing microstates can reveal fundamental building blocks of brain activity and provide insights into information processing and cognitive functions.
Neuromarketing and Neuroeconomics:
Neuromarketing applies neuroscientific techniques, including EEG analysis, to understand consumer behavior, preferences, and decision making. By examining neural responses to marketing stimuli, such as advertisements or product designs, researchers can gain insights into consumer motivations and optimize marketing strategies.
Neuroeconomics combines neuroscience, economics, and psychology to study the neural processes underlying economic decision making. EEG data analysis can help uncover the neural mechanisms involved in choices related to risk, reward, and social interactions, contributing to our understanding of economic behavior.
Social Value of Money:
The social value of money is a concept that explores the psychological and social aspects of money beyond its economic value. EEG analysis can be used to investigate how the brain responds to financial stimuli, such as gains, losses, and economic inequality. By studying neural markers associated with financial decision making, researchers can shed light on the social and emotional dimensions of money and its impact on individuals and societies.
In summary, EEG data analysis techniques, including ERP components, ICA, FFT, wavelets, LORETA, and microstates, provide valuable insights into decision-making processes, brain responses to stimuli, and cognitive functions. These techniques have applications in neuromarketing, neuroeconomics, and understanding the social value of money.
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 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
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 | 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 |
 | 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 |
 | 06:05:00 - 07:31:00 Decision Making |
 | 07:31:00 - 10:30:00 Decision Making |
 | 10:30:00 - 14:11:00 Decision Making |
 | 14:11:00 - 18:30:00 Money CreationValue and Social Value of MoneyCapitalism in the 21st centuryChicago Boys |
 | 18:30:00 - 22:45:00 Money CreationValue and Social Value of MoneyCapitalism in the 21st centuryChicago Boys |
 | 22:45:00 - 23:59:00 Money CreationValue and Social Value of MoneyCapitalism in the 21st centuryChicago Boys |
