Classic EEG (ERPs)/ Advanced EEG. Quentin Noirhomme

Transcription

1 Classic EEG (ERPs)/ Advanced EEG Quentin Noirhomme

2 Outline Origins of MEEG Event related potentials Time frequency decomposition i Source reconstruction

3 Before to start EEGlab Fieldtrip (included in spm)

4 Part I: Origins EEG Discovered by Hans Berger in 1924 Non invasive measure of electrical brain activity

5 Origins: MEG 1968

6 Origins Baillet et al., IEEE Sig. Proc. Mag., 2001

7 Origins: Potentials

8 Origins Baillet et al., IEEE Sig. Proc. Mag., 2001

9 M/EEG vs. fmri

10 Raw EEG

11 Fp2 T4 EEG in coma Burst Suppression Alpha coma Isoelectric T4 02 Fp2 C4 C4 02 Fp1 T3 T3 01 Fp1 C3 C µv 50 µv 20 µv 20 µv 1 s 1 s 1 s Thömke et al. BMC Neurology :14 doi: /

12 EEG in sleep http\\:

13 EEG Rhythms Gamma : > 30 Hz eeg states.gif

14 Burst EEG events Spikes eeg states.gif

15 Part II: Event Related potentials Wolpaw et al., 2000

16 Averaging Adapted from Tallon Baudry and Bertrand, 1999 Average potential (across trials/ subjects) relative to some specific event in time

17 Preprocessing 1. Filtering 2. Segmentation 3. Artifact rejection 4. Averaging 5. Baseline removal

18 Filtering Why filter? EEG consists of a signal plus noise Some of the noise is sufficiently different in frequency content from the signal that it can be suppressed simply by attenuating ti different frequencies, thus making the signal more visible Non neural physiological activity (skin/sweat potentials) Noise from electrical outlets Highpass filter to remove drift due to sweating, Notch filter to remove the line noise (50 60Hz) Low pass filter (often 30Hz for ERP)

19 Segmentation

20 Artifacts

21 Artifacts

22 Artifacts

23 Artifacts

24 Artifacts

25 Artifact rejection Visual inspection of the data Thresholding (e.g., everything above 100µV) Statistical i method Independent component analysis good for blinks and other visual artifacts Help if you have EOG and EMG channels Do not trust automatic methods

26 Averaging

27 Averaging Assumes that only the EEG noise osevaries from trial to trial But amplitude and latency will vary

28 Averaging: effects of variance L t i ti b Latency variation can be a significant problem

29 Averaging Assumes that only the EEG noise osevaries from trial to trial But amplitude and latency will vary S/N ratio increases as a function of the square root of the number of trials. It s always better to try to decrease sources of noise than to increase thenumberof trials.

30 Baseline correction Remove the mean of the recorded baseline (e.g., 200 ms to 0 ms) Variation in baseline duration can induce change in potential amplitude Individually id for each electrode SPM does it automatically while segemting the data

31 Part III: Time frequency decomposition Adapted from Tallon Baudry and Bertrand, 1999

32 Evoked frequency Adapted from Tallon Baudry and Bertrand, 1999

33 Induced frequency decomposition Adapted from Tallon Baudry and Bertrand, 1999

34 Induced frequency decomposition Adapted from Tallon Baudry and Bertrand, 1999

35 Time frequency decomposition Adapted from Tallon Baudry and Bertrand, 1999

36 Continuous Morlet wavelet

37 Analysis Grand mean > > Average across subject Convert ERP or TF decomposition into images => first/second level lanalysis Source reconstruction => first/second level analysis

38 1 st Level Analysis select periods or time points in peri stimulus time Choice made a priori. sum over all time points

39 Part IV: Source reconstruction From luebeck.de, 2008

40 Source reconstruction 1. Forward Model 2. Inverse reconstruction

41 Forward modeling Electromagnetic head model Reconstruct electrode signals from electrical current in the head

42 Head model Spherical approximation Realistic head model Boundary element method Finite element method

43 SPM head model Compute transformation T Individual MRI Templates Apply inverse transformation T 1 Individual mesh BEM mesh

44 Head model Electrode locations Registration Landmark kbased Surface matching Leadfield fiducials fiducials Rigid transformation (R,t) Individual sensor space Individual MRI space

45 Inverse approaches Dipole Distributed dipoles Least square or Beamforming More unknowns than data

46 Distributed approach Y = KJ+ E No unique solution! Pi Priors: min( Y KJ 2 + λf(j) ) minimum overall activity Location Smoothness Bayesian model comparison

47 References Sylvain Baillet s presentation at HBM 2008 SPM for dummies presentations Baillet et al., IEEE Sig. Proc. Mag., 2001 Mtt Mattout, tphilli Phillips & Fit Friston (2005) SPM course s05/ppt/meeg_inv.ppt t SPM manual