Eeg Data Analysis
Eeg Data Analysis - Related Videos
10:35
Best Current Practice for Obtaining High Quality EEG Data During Simultaneous fMRI
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Cited by 49 •
2013
Simultaneous electroencephalography (EEG) and functional Magnetic Resonance imaging (fMRI) is a powerful neuroimaging tool. However, the inside of an MRI scanner forms a difficult environment for EEG data recording and safety must be considered whenever operating EEG equipment inside a scanner. Here, we present an optimised EEG-fMRI data acquisition protocol.
09:32
Cortical Source Analysis of High-Density EEG Recordings in Children
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Cited by 6 •
2014
In recent years, there has been increasing interest in estimating the cortical sources of scalp measured electrical activity for cognitive neuroscience experiments. This article describes how high density EEG is acquired and how recordings are processed for cortical source estimation in children from the age of 2 years at the London Baby Lab.
11:15
Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy
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Cited by 30 •
2013
Neuroimaging researchers typically consider the brain's response as the mean activity across repeated experimental trials and disregard signal variability over time as "noise". However, it is becoming clear that there is signal in that noise. This article describes the novel method of multiscale entropy for quantifying brain signal variability in the time domain.
09:36
Extracting Visual Evoked Potentials from EEG Data Recorded During fMRI-guided Transcranial Magnetic Stimulation
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2014
This paper describes a method for collecting and analyzing electroencephalography (EEG) data during concurrent transcranial magnetic stimulation (TMS) guided by activations revealed with functional magnetic resonance imaging (fMRI). A method for TMS artifact removal and extraction of event related potentials is described as well as considerations in paradigm design and experimental setup.
06:33
Method for Simultaneous fMRI/EEG Data Collection during a Focused Attention Suggestion for Differential Thermal Sensation
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Cited by 4 •
2014
We present a protocol for concurrent collection of EEG/fMRI data, and synchronized MR clock signal recording. We demonstrate this method using a unique paradigm whereby subjects receive ‘cold glove’ instructions during scanning, and EEG/fMRI data are recorded along with hand temperature measurements both before and after hypnotic induction.
FAQs
Filtering reduces noise in recorded signals before analysts calculate neural measures. This step helps distinguish relevant electrical activity from unwanted signal variation, making later interpretation more reliable. Because filtering is part of preprocessing rather than the final analysis, its main role is to prepare recordings for epoching and for extracting time-domain, frequency-domain, or connectivity features.
Artifacts can appear in the recording even though they do not represent the neural activity being studied. Eye movements are a specific example that analysts identify and remove during preprocessing. Managing these signals helps prevent non-neural activity from distorting event-related potentials, spectral power estimates, or connectivity results, improving the relevance of subsequent interpretations.
These measures describe neural activity from different analytical perspectives. Time-domain features characterize changes across time, frequency-domain features examine spectral power, and connectivity measures assess relationships among signals. Selecting among them depends on the research question, such as tracking responses to sensory input, examining behavior-related activity, or investigating coordinated brain activity.
A typical workflow begins by preprocessing the continuous recording, including filtering noise and identifying or removing artifacts. Analysts then segment the signal into meaningful epochs and extract features in the time or frequency domain, or calculate connectivity. The resulting measures can be interpreted in relation to sensory input, behavior, sleep, disease, or an intervention.
Researchers apply these analyses when they need to characterize how electrical activity changes across experimental or physiological conditions. Recordings can be examined in relation to sensory input, behavior, and sleep, allowing investigators to compare neural responses or activity patterns. This makes EEG analysis useful for studying cognition and broader questions about brain function in neuroscience.
In clinical investigation, extracted EEG measures can help researchers examine activity associated with neurological conditions. The same analytical framework can evaluate responses to interventions by comparing relevant neural measures across conditions or time points. Because EEG analysis can produce event-related potentials, spectral power, and connectivity outcomes, it offers several ways to characterize changes linked to disease or treatment.