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Demetris Roumis edited this page May 23, 2023 · 22 revisions

EEG Research Overview

Experiment Duration, Channels, Subjects

  • EEG recording sessions typically last from 15 minutes to a few hours, depending on the type of study. Some studies, such as sleep studies, can last up to 8-10 hours. There's nothing stopping anyone from doing full or multi-day recordings but that is probably not fun for the participants, so seldom done.
  • Typical Channel Count:
    • Clinical systems often use about 20 channels following the 10-20 system of electrode placement.
    • Research-grade systems can have 32, 64, 128, or 256 channels.
    • High-density systems can have up to several thousands of channels.
  • Studies typically range from a few subjects (e.g., 10-20 for a pilot study) to several hundred in large-scale studies.

Common Experiment Types

  • Resting State: Participants are asked to relax with their eyes open or closed without performing any task.
  • Event-Related Potentials (ERPs): EEG response to a specific sensory, cognitive, or motor event.
  • Cognitive Tasks: Tasks that require mental processing like memory tasks, attention tasks, etc.
  • Neurofeedback: Participants are given real-time feedback about their brainwave patterns and asked to control them.
  • Brain-Computer Interface (BCI): Interactions between the brain and an external device.

Other Modalities Commonly Used Simultaneously

  • fMRI: Functional Magnetic Resonance Imaging for high-resolution spatial imaging.
  • fNIRS: Functional Near-Infrared Spectroscopy for assessing cerebral oxygenation.
  • MEG: Magnetoencephalography for magnetic field measurements.
  • Eye Tracking: To monitor visual attention and detect blinks/artifacts.
  • Behavioral Measures: Reaction times, accuracy, accelerometers, position, etc.
  • Biophysical Measures: electrodermal activity, electrocardiogram, temperature, respiration, etc.

Data

Data Size

  • An estimate is that a one-hour recording from a 64-channel system sampled at 500 Hz and saved in 16-bit would take at least 220 MB of storage in raw binary format, not including overhead from metadata, etc.
    • 👉 Therefore, relative to ephys and imaging, I don't think we need to prioritize thinking about larger-than-memory data visualization for EEG just yet.

Data Format

  • dtype in-memory: typically floats. Given the signal range, single-precision floats are sufficient.
  • dtype on-disk: typically signed integers, often 16-bit.
  • Common formats include European Data Format (EDF), BioSemi Data Format (BDF), and BrainVision EEG (BV).
  • Some labs use proprietary formats linked to the specific EEG hardware they use.

Data and Signal Specification

  • Units: EEG data is typically recorded in microvolts (µV).
  • Typical Signal Range: Most EEG signals fall within a range of plus or minus 100 µV, though larger signals may be recorded, particularly in the presence of artifacts such as blinks.
  • Sampling Rate: Commonly between 250 to ~1000 Hz in most modern EEG systems, but some studies may use higher sampling rates, especially for specific purposes like studying high-frequency oscillations.
  • Frequency Range: Typically 1-100 Hz is analyzed, although the human brain produces activity in a wide range of frequencies, with significant signals detectable up to 500 Hz or higher. EEG is most commonly associated with lower frequency bands: delta (0-4 Hz), theta (4-8 Hz), alpha (8-12 Hz), beta (12-30 Hz), and gamma (30-100+ Hz).

Data Generation/Simulation

  • Artificial Neural Networks: These can be used to simulate the underlying neuronal activity and then a model of EEG signal generation can be used to convert this activity into EEG-like data.
  • Dipole Models: These simulate the electrical activity of the cerebral cortex as a distribution of current dipoles. These dipoles generate electric fields that propagate to the scalp, where they can be summed to simulate EEG signals.
  • Stochastic Processes: Random processes, possibly with specific statistical properties, can be used to simulate EEG data. This might include autoregressive models, or simple Gaussian noise.
  • Physiologically-Based Models: These use equations derived from the biophysics of neurons and brain tissue to simulate the generation of EEG signals. An example of such a model is the neural mass model.
  • Sim Software: Tools such as neurodsp, Brainstorm, MNE include built-in functions for simulating EEG data.
  • first plan: Use simple noisy sine waves until something more realistic is required.
  • 👉 updated plan 230522: use power-law Brownian noise process to simulate slightly more realistic EEG data
    • In the context of EEG data generation, the power-law Brownian noise process is often used as a model for the background activity of the brain. It captures the fractal-like properties observed in the brain's electrical activity, where fluctuations at different timescales exhibit similar statistical properties.

Lists/Sources of real data

Specific real datasets of interest

Software

Common Analysis Packages (Especially Python)

  • MNE: Python-based software for M/EEG data processing.
  • EEGLAB: An interactive Matlab toolbox for processing continuous and event-related EEG, MEG and other electrophysiological data.
  • Brainstorm: An open-source application dedicated to the analysis of brain recordings.
  • FieldTrip: Matlab software toolbox for MEG and EEG analysis.
  • PyEEG: A Python module for EEG feature extraction.

Common Visualization Solutions (Especially Python)

  • MNE: Includes capabilities for visualizing EEG data, topographic maps, etc.
  • EEGLAB: Provides extensive graphical capabilities.
  • Matplotlib and Seaborn
  • Plotly
  • Bokeh

Processing and Analysis

  • Data Cleaning: EEG data is often noisy and needs to be cleaned. Techniques include filtering to isolate the frequency bands of interest and remove noise, artifact rejection to remove non-neural signals, and Independent Component Analysis (ICA) to statistically separate sources of signal.

Contents

Meetings

Modality Notes

Other

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