MEG: Basic Data Processing Analysis
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1 MEG: Basic Data Processing and Time-frequency Analysis Stephan Grimault, PhD November 22, 2006
2 General outline 1) Basic Pre-processing and processing of MEG data basic ERF (ERP) analysis and activation map Pre-processing and processing: definitions and purpose Pre-processing steps Processing steps 2) Another way to analyze the data: Time-frequency Analysis
3 Basic ERF (ERP) analysis Protocol example Stimuli
4 Activation map (1)
5 Activation map (2)
6 Pre-processing & Processing: definition and purpose Pre-processing : - Clean the data to keep only the functional signals of interest (involves detection and correction of noise and acquisition artifacts) - optimize data processing Processing: - analysis of functional signals of interest - to evaluate to cognitive hypotheses
7 General outline 1) Basic Pre-processing and processing of MEG data basic ERF (ERP) analysis and activation map Pre-processing and processing: definitions and purpose Pre-processing steps Processing steps 2) Another way to analyze the data: Time-frequency Analysis
8 Pre-processing is not trivial: MEG measures magnetic fields from femtotesla (10-15 T) to picotesla (10-12 T) Earth s magnetic field: 4,710-5 T. small magnetic field measurements lead to artifacts
9 Pre-processing steps (1): Environmental Noise Reduction
10 Pre-processing steps (2): DC offset removal
11 Pre-processing (3): Frequency filtering: High-pass filter Low-pass filter
12
13
14
15 6 f 4 f f
16 6 f 4 f f
17 6 f 4 f f 0.1 f
18 6 f 4 f f
19 6 f 4 f f 0.05 f
20 6 f 4 f f 0.1 f 0.05 f
21 γ β α θ Hz 12-45Hz Hz 4-8.5Hz δ Hz
22 Sampling rate = 600Hz t = s Signal Theory frequency MAX = Sampling/2 Low Pass Filter = Sampling rate /4 : 600Hz 150Hz
23 Pre-processing (3): Frequency filtering example High Pass Filter Low Pass Filter
24 Pre-processing (4): Eye artifact (blinks, motion) 20pT
25 Eye blink correction ex. 1
26 Eye blink correction ex. 2
27 Pre-processing (5): Current Noise at 60 Hz (50 Hz Europe)
28 Pre-processing (6): ECG or watch artifact
29 Pre-processing (7): Dental artifacts
30 Pre-processing (8): Subject motion artifact
31 General outline 1) Basic Pre-processing and processing of MEG data basic ERF (ERP) analysis and activation map Pre-processing and processing: definitions and purpose Pre-processing steps Processing steps 2) Another way to analyze the data: Time-frequency Analysis
32 Processing (1): single trial recording
33 Processing (1): Why record N trial SNR N
34 Processing (2) : select condition
35 Processing (3) : averaging
36 Processing ends work of interpretation begins! Protocol example Bonne chance! Stimuli
37 Basic Pre-processing and Processing of MEG data: Summary Pre processing Environmental Noise Reduction Frequency filtering : High Pass, Low Pass Eye blink correction 60 Hz or 50 Hz (Europe) ECG or watch artifact Processing Recording N trials by condition Select condition (trigger) Averaging
38 General outline 1) Basic Pre-processing and processing of MEG data basic ERF (ERP) analysis and activation map Pre-processing and processing: definitions and purpose Pre-processing steps Processing steps 2) Another way to analyze the data: Time-frequency Analysis
39 Time Frequency map representation
40 Keep Information from thetrials
41 Average after keep information from Trials
42
43 TF map calculation : the wavelets
44 TF map calculation : the wavelets
45 Time frequency analysis: one step more
46 Time frequency analysis: two step more Localisation? Localisation Discussion?
47 Thanks to: Franco Lepore (CERNEC) Pierre Jolicoeur (CERNEC) Équipe MEG (Anne Sophie, Christophe,, Jean- Marc, Kevin, Manon, Mihaela)
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