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EEG, ERP, & MEG: Real Time Measures - Coggle Diagram

- - - - 1875 Richard Caton
        
        Measured electrical potentials in between cortical surface & skull
        
        In dogs & monkeys
        
        Observed variation in current when light shined into eye, to sensory stimulation, in sleep, in anaesthesia
      - 1929 Hans Berger
        
        First human EEG recording alpha & beta waves
        
        In 12 year old son
        
        Coined term EEG
    - - 1964 Grey Walter
        
        Recorded first cog ERP
        
        Contingent Negative Variation
        
        Developed first EEG topographic map
        
        To plot spatial distribution of electrical signal
        
        CNV
        
        ERP that develops in interval between warning & go stimulus
        
        Preparation for upcoming target
  - - - fNIRS
        
        Haemodynamic acquisition technique
        
        Indirect measure of neuronal activity
        
        Medium equipment cost
        
        Reasonable temporal resolution
        
        4 - 5 s
        
        Good spatial resolution
        
        4mm
      - fMRI
        
        Used for locating the where
        
        Haemodynamic acquisition technique
        
        Indirect measure of neuronal activity
        
        V. high equipment cost
        
        Reasonable temporal resolution
        
        4-5 s
        
        Excellent spatial resolution
        
        2mm
      - EEG
        
        Electromagnetic acquisition technique
        
        Direct measure of neuronal activity
        
        Low equipment cost
- - - - OPM-MEG
        
        Uses optically pumped magnetometers
        
        Almost as sensitive as SQUID but not require cooling
        
        Allow development of relatively cheap MEG systems
        
        Limitation
        
        Still under development (Hironga et al., 2020)
        
        Slightly less sensitive (Vrba, 2002)
        
        Advantage
        
        More portable
      - Single quantum interference device
        
        Sensors in machine
        
        Sensitivity (Vrba, 2002)
        
        V. sensitive as pick up v. tiny electrical signals
    - - EEG Recording
  - - - Nasion
        
        Point between forehead & nose
      - Inion
        
        Bump at back of head
      - Location
        
        Frontal
        
        Temporal
        
        Parietal
        
        Occipital
        
        Central
        
        Z for central line
      - Numbering
        
        Even = Right hemisphere
        
        Odd = Left hemisphere
  - - - Computation of NIC®
        
        Power of diff EEG bands can be computed in real-time for any specific channel
        
        Scalp map displays colour code to spatially represent power distribution
        
        Band ratio selected
        
        Cortical map uses backward problem solving to compute density of electric dipoles on brain surface
        
        Source localisation tool allows active brain areas to be identified based on electric potential values measured by scalp sensors
        
        EEG Bands
        
        Delta
        
        Theta
        
        Alpha
        
        Beta
        
        Gamma
  - - - Baseline Correction
        
        Subtract estimate of DC offset from waveform
        
        Mean pre-stimulus voltage a reasonable estimate
        
        Subtract value from each point in waveform
        
        Anything that messes up baseline will be propagated to amplitude correction
        
        Example: Ocular
        
        Blinking
        
        Saccades
        
        Example: Skin Potentials
        
        Sweating
        
        Example: Alpha Waves
        
        Dozing off
- - - - Naming Conventions
        
        P = Positive
        
        P1 = First positive
        
        P300 = Positive at >300ms
        
        N = Negative
        
        N1 = First negative
        
        N170 = Negativity at 170ms
      - Characterised By
        
        Latency
        
        Polarity
        
        Topography
      - Analysed by Comparing
        
        Amplitude
        
        Latency
        
        Topography
  - - - Advantage
        
        Not need P to have particular attention
        
        Good for younger ages
    - - Limitations of Peaks
        
        More susceptible to noise
        
        Biased by no. trials
        
        Occur at diff times across brain
  - - - Steady State VEP
        
        Rapid testing time
        
        High density ERP recording
        
        Types of SSVEP
        
        Orientation-reversal
        
        Motion-reversal
        
        Orientation-Reversal VEP (Braddick et al., 2005)
        
        Ventral
        
        Significant in first few weeks of life
        
        Motion-Reversal VEP (Braddick et al., 2005)
        
        Significant around 12 weeks
        
        Study: Global Motion & Form (Wattam-Bell et al., 2010)
        
        Reorganisation of global form & motion processing during human visual development
      - Coherent Motion Transient VEP (Gilbert et al., 2016)
        
        R: Response depend on age
        
        Study: Mapping Connections (Gilbert & Wu, 2013)
        
        Study: Connections (Burkhalter, 1993)
        
        At 4 months feedback connections to V1 immature in connection to forward connections
  - - - Study: Relation (Tibon et al., 2022)
        
        R: Map relation between trait-like brain networks & cog function
  - - - Study: Task Related Theta & Theta-Gamma Coupling (Tan et al., 2024)
        
        Task-related theat power positively related to cog functioning in frontal region
        
        Theta EEG power increases during engagement in memory, attention, & cog control tasks
        
        Applying current theta frequency to synchronise neural firing improves EF & memory
        
        Stronger theta-gamma coupling during task predicts memo performance
  - - - Study: Continuous Speech (Hamilton & Hurth, 2020)
      - Study: Navigating Artificial Environment (Krugliak & Clarke, 2022; Nicholls et al. 2022)
        
        M; VR/AR
- - - - Process
        
        Strength of relationship between each time point & underlying factors
        
        3 conditions/subjects/electrode sites
        
        Example: PCA
        
        Changes at B strongly covary w. changes at time C but only weakly covary w. changes at time A
        
        B & C strongly relate to single underlying source of variation
        
        A only weakly related
        
        Source to Scalp
        
        Voltage at electrode at time is weighted sum of all components active at that time
        
        Forward Problem
        
        Given sources, what are scalp waveforms
        
        Inverse Problem
        
        Given scalp waveforms, what are sources
        
        Scalp Distribution
        
        Single superficial dipole
        
        Deeper dipole
        
        Multiple dipoles
        
        Single Superficial Dipole
        
        Create relatively focused scalp distribution
        
        Distribution changes quite a bit as dipole rotated/shifted
        
        Easy to localise w. reasonable precision
        
        Deeper Dipole
        
        Create broader scalp distribution
        
        Changes in dipole location have smaller impact on scalp distribution
        
        Harder to localise w. precision
        
        Especially if data noisy
        
        Hard to distinguish from broadly distributed superficial activity
        
        Multiple Dipoles
        
        Can localise reasonably well w. two dipoles
        
        As long as reasonably far apart & superficial
        
        Some pairs dipoles make localisation difficult
        
        As no. dipoles increases, likelihood of difficult-to-localise situation increases
  - - - Operator Dependence
        
        Solution depend on no. dipoles & starting locations
        
        Easily biased by expectations
      - Difficult to Assess Accuracy
        
        5 free parameters per dipole
        
        When one parameter incorrect, other variables can slightly chang to maintain low residual variance
        
        In presence of noise errors in forward solution, substantially wrong solution may have lower residual variance than correct
        
        BESA software provide some info about margin of error but estimate depend on various unspecified factors
- - - - Forward Problem
        
        Possiblity to determine relative distribution of conductances
        
        From known orientation & location of set dipoles
      - Inverse Problem
        
        Possibiility to determine orientation & location of set dipoles from activity (conductance)