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In-Depth Notes on EEG Concepts and Applications

Introduction to EEG

  • Electroencephalography (EEG) is a method to measure the electrical activity of the brain through electrodes placed on the scalp.
  • EEG primarily reflects the activity of the cerebral cortex which covers older brain structures like the limbic system and brainstem.
  • Deep brain regions require implanted electrodes for detailed assessment.

Measuring Brain Activity with EEG

  • EEG reflects the cumulative electrical activity of numerous neurons, particularly cortical pyramidal cells which are aligned perpendicularly to the cortical surface.
  • When these neurons receive similar inputs, their synchronous electrical changes—postsynaptic potentials—combine to produce detectable signals on the scalp.
  • EEG mainly tracks larger regions of activation with neurons needing to be synchronized for effective detection.
  • Typical EEG rhythms include:
    • Alpha waves (8-12 Hz): Awake relaxed state, synchronized activity.
    • Beta waves (13-30 Hz): Awake attentive state, cognitive activity, desynchronized.
    • Gamma waves (30-80 Hz): Integration of stimuli.
    • Delta waves (1-5 Hz): Deep sleep or high mental activity.
    • Theta waves (4-8 Hz): Deep sleep or attention periods.

EEG Recording Techniques

  • Simple measurements can be made using a pair of electrodes and a ground electrode, measuring voltage differences.
  • International 10-20 System: A standard for electrode placement (prefrontal, frontal, parietal, occipital, temporal, and central) to ensure consistency in studies.
  • Challenges: The EEG signal is weak; amplification (up to a million times) is necessary.
  • EEG is sensitive to artifacts generated by muscle movements, eye blinks, and external electrical sources.

Signal Analysis

  • The EEG signal is generally recorded in the 0 to 100 Hz domain with a sampling rate of 500-1000 Hz.
  • Electrode preparation involves skin cleansing (alcohol rub) and potentially slight abrasion to enhance signal clarity.
  • Fast Fourier Transform: A common method for analyzing EEG data—it decomposes signals into constituent frequency components for better understanding.

Spatial Analysis and Source Localization

  • Analyzing the spatial aspects of EEG activity requires multiple electrodes.
  • Source localization methods allow the identification of the origin of brain activity in 3D models, combining EEG with other imaging technologies like fMRI for higher spatial accuracy.

Event-Related Potentials (ERP)

  • ERP refers to the brain's electrical response to specific stimuli measured through electrodes.
  • Examples of ERP components:
    • P1 (P100): Positive deflection after 65-100 ms, driven by the visual cortex.
    • N1: Larger negative deflection peaking at 150-200 ms.
    • P2 (P200): A large positive wave after 150-250 ms, can also have centro-frontal components.
  • Responses show attentional influences on components (especially P1 and N1).
  • Auditory processing is quicker than visual, influencing reaction times (auditory: 140-160 ms, visual: 180-200 ms).

Classification of ERP Components

  • Early components (before P1) indicate non-cortical processing and are generated in subcortical regions (e.g., brainstem-evoked responses).
  • Exogenous components: Fast responses determined by stimulus characteristics within first 100 ms.
  • Endogenous components: Reflect higher processing levels; P300 wave (~300 ms) is notable here.

EEG Applications in Psychological Research

  • EEG is utilized in various fields: sleep studies, activation, conditioning, hypnosis, and emotional responses.
  • Research areas for ERP include attention, perception, and the processing of visual/auditory stimuli.

Conclusion

  • EEG serves as a vital tool in psychology and neuroscience for investigating brain function and understanding complex neural processes.