Offline Eeg Analysis
Offline Eeg Analysis - Related Videos
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.
12:07
Using an EEG-Based Brain-Computer Interface for Virtual Cursor Movement with BCI2000
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Cited by 26 •
2009
In this video, we demonstrate the steps required to run a brain-computer interface experiment, including setting up the EEG cap, calibrating the system, and training the user to move a cursor in two dimensions using imagined movements.
19:32
Preterm EEG: A Multimodal Neurophysiological Protocol
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Cited by 39 •
2012
This video explains the background theory of the neonatal EEG activity and the sensory responses, followed by a live demonstration of their recording in neonatal intensive care unit.
09:19
Electro-encephalography (EEG)
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2023
EEG is a non-invasive technique that can measure brain activity. The neural activity generates electrical signals that are recorded by EEG electrodes placed on the scalp. When an individual is engaged in performing a cognitive task, brain activity changes and these changes can be recorded on the EEG graph. Therefore, it is a powerful tool for cognitive scientist aiming to better understand the neural correlates associated with different aspects of cognition, which will ultimately help them...
06:59
High-density EEG Recordings of the Freely Moving Mice using Polyimide-based Microelectrode
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Cited by 33 •
2011
In this article, we described the surgery procedure and handling tips for implantation of ultra-thin polyimide-based microelectrode array (PBM-array) on the mouse skull for acquisition of high-density encephalography (EEG) in a mouse model.
FAQs
Referencing and filtering shape the signal that later analyses interpret. Referencing defines the voltage comparison used across scalp channels, while filtering emphasizes selected temporal or frequency characteristics and suppresses unwanted components. Because these choices can alter apparent waveforms or oscillatory measures, documenting them is essential when comparing participants, conditions, or repeated analyses.
Artifact rejection and epoching determine which portions of a recording enter interpretation. Rejection removes segments affected by non-neural contamination, whereas epoching partitions continuous data into analysis windows associated with the study design. The resulting choices influence whether event-related potentials or oscillatory changes remain measurable, so consistent criteria are important for valid comparisons across datasets.
Time-domain analysis emphasizes how voltage changes unfold, frequency-domain analysis quantifies activity according to frequency, and time-frequency analysis examines how frequency content changes over time. These approaches answer different questions rather than serving as interchangeable labels. Selecting among them depends on whether the research focus is waveform features, oscillations, or their temporal evolution.
A practical workflow begins by preparing the recorded signals, choosing a reference, applying filters, rejecting artifacts, and dividing the data into epochs. Researchers then select time-domain, frequency-domain, or time-frequency measures suited to the hypothesis. Preserving these processing decisions allows the same recordings to be reanalyzed with adjusted parameters and supports validation across participants.
It can quantify electrical activity associated with perception, cognition, and sleep, while also supporting investigation of neurological disorders. Depending on the analysis, researchers may examine event-related potentials, oscillations, or connectivity. This breadth makes the approach useful for relating recorded brain activity to experimental conditions and for identifying patterns that may contribute to clinically relevant biomarkers.
Because processing occurs after recording, investigators can revisit the same data, adjust analysis parameters, and test whether findings remain consistent. Analyses across participants further support validation rather than reliance on a single recording. In neuroscience, this repeatability is especially valuable when evaluating candidate biomarkers or comparing activity across perception, cognition, sleep, or disorder-related conditions.