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Contrasts & Inference - EEG & MEG Outi Tuomainen & Rimona Weil 17.5.2005 mfd 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 How does SPM/EEG work? Preprocessing Raw M/EEG data Projection SPM5-stats 2D - scalp SPM{t} SPM{F} Control of FWE mass-univariate analysis Single trials Epoching Artefacts Filtering Averaging, etc. 3D-source space Kiebel, S. 2005 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 Assumption 2: Background EEG is random and uncorrelated with ERP signal - Instead: EEG is not entirely uncorrelated with event-related activity Kiebel & Friston 2004 Data (at each voxel) Single subject Multiple subjects Trial type 1 ... ... Subject j ... ... Trial type i Subject 1 Trial type n Subject m Kiebel, S. 2005 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 Quantification of ERPs 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 Cond2 Cond3 C) Spatiotemporal: - how scalp topographic patterns vary across time (correlation of successive topographic maps) Quantification of ERPs Effect-Specific Hypothesis vs. EffectUnspecific 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 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 (anteriorposterior, left-right, ventral-dorsal) 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 ….