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Contrasts & Inference - EEG & MEG

This presentation covers the revision of ERPs/ERFs in SPM analysis, conventional quantification methods, and contrasts between M/EEG and fMRI regarding inference. Learn how SPM/EEG works, preprocessing steps, statistical analysis, and quantification techniques for ERPs. Explore the challenges in averaging ERPs and understanding signal characteristics, along with effective statistical approaches. Gain insights into temporal, spatial, and spatiotemporal quantification of ERPs, and hypothesis testing methods. Improve your understanding of group-electrode-condition interactions and statistical corrections. References included.

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Contrasts & Inference - EEG & MEG

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  1. Contrasts & Inference - EEG & MEG Outi Tuomainen & Rimona Weil 17.5.2005 mfd

  2. Outline • ERPs/ERFs in SPM: a revision • A short introduction to the "conventional" quantification of ERPs • Contrasts and inference in M/EEG vs. fMRI • How to do it in SPM + things to bear in mind

  3. How does SPM/EEG work? Preprocessing Projection SPM5-stats SPM{t} SPM{F} Control of FWE Raw M/EEG data 2D - scalp mass-univariate analysis Single trials Epoching Artefacts Filtering Averaging, etc. 3D-source space Kiebel, S. 2005

  4. Revision: ERPs/ERFs Average ERPs as an estimate of event-related EEG activity? Assumption 1: Detected signal should have stable characteristics in each single trial - Instead: multiple components whose amplitude and latency can vary independently (e.g. latency jitter) - So: the averaged ERP may present only gross picture of the neural processes elicited by the event of interest Kiebel & Friston 2004 Assumption 2: Background EEG is random and uncorrelated with ERP signal - Instead: EEG is not entirely uncorrelated with event-related activity

  5. Data (at each voxel) Single subject Multiple subjects Subject 1 Trial type 1 . . . . . . Subject j Trial type i . . . . . . Subject m Trial type n Kiebel, S. 2005

  6. Revision: ERPs/ERFs • "ERPs are signal-averaged epochs of EEG that are time-locked to the onset of stimulus" • So a waveform can be seen as a time series that plots scalp voltage (µV, T) over time (ms) • ERPs are usually recorder at multiple scalp electrode sites spatial parameter to complement the temporal and frequency information • Quantifying ERPs: can be organised into three categories: temporal, spatial and spatiotemporal

  7. Quantification of ERPs Cond2 Cond1 A) Temporal: - how waveforms recorded at individual sites vary over time across experimental conditions - amplitude and latency as a function of condition B) Spatial: - topographic mapping: quantifying variation in voltage across the scalp electrode array at single time point or time window Cond3 C) Spatiotemporal: - how scalp topographic patterns vary across time (correlation of successive topographic maps)

  8. Quantification of ERPs Effect-Specific Hypothesis vs. Effect-Unspecific Hypothesis - for example: component should be present at Cond1 not in Cond2 -> a priori restriction to a set of electrode sites and time window - Quantifying the waveform: A) peak amplitude (max/min), mean amplitude (typically arithmetic average), peak-to-peak amplitude, mean area amplitude B) latency measures: max/min point in a time window (peak-picking), onset/offset latencies Mean amplitude (µV), peak amplitude and latency measures (µV, ms) Statistics: (ANOVA/MANOVA and appropriate corrections* and follow-up tests); e.g. group-electrode-condition Mauchly’s test for sphericity ≤ 0.05; Greenhouse-Geisser and Huynh-Feldt corrections

  9. Quantification of ERPs • Mean amplitude by Condition (2, within-subject factor) & Group (2) at Fz (electrode, within-subject factor) • Electrode factor? -> e.g. if Left Hemisphere electrodes are likely to be systematically different from Right Hemisphere electrodes - In high-density montages it is a good idea to divide electrodes into averaged regions (anterior-posterior, left-right, ventral-dorsal)

  10. Outline • ERPs/ERFs in SPM: a revision • A short introduction to the "conventional" quantification of ERPs • Contrasts and inference in M/EEG vs. fMRI • How to do it in SPM + things to bear in mind References in the end ….

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