for EEG and MEG

pancakesbootΤεχνίτη Νοημοσύνη και Ρομποτική

24 Νοε 2013 (πριν από 3 χρόνια και 8 μήνες)

70 εμφανίσεις

Pre
-
processing

for EEG and MEG

Przemek Tomalski

&

Kathrin Cohen Kadosh

Recording EEG

QuickTime™ and a
TIFF (Uncompressed) decompressor
are needed to see this picture.
Two crucial steps


Activity caused by your stimulus (ERP) is
‘hidden’ within continuous EEG stream


ERP is your ‘signal’, all else in EEG is ‘noise’


Event
-
related activity should not be random, we
assume all else is


Epoching



cutting the data into chunks referenced
to stimulus presentation


Averaging



calculating the mean value for each
time
-
point across all epochs

Extracting ERP from EEG


ERPs
emerge from
EEG as you
average
trials
together

Overview Pre
-
processing


Converting the data


Epoching/Segmentation


Filtering


Artifact Detection/Rejection


Averaging


Re
-
referencing

Convert the data

Overview Pre
-
processing


Converting the data


Epoching /Segmentation


Filtering


Artifact Detection/Rejection


Averaging


Re
-
referencing

Epoching

Segmenting (Epoching)

Segment length:

at least 100 ms should precede the stimulus onset (see

baseline correction). The time
-

frequency analysis can distort the signal

at both ends of the segment, make sure you do not lose important

data and that the baseline segment is still long enough after cutting off the

affected portions. The affected segment length depends on the frequency

in an inverse manner (length ms ~ 2000/freq Hz)


The segment should not be too long nevertheless, the longer it is the

bigger the chance to include an artifact!


Epoching
-

SPM


Overview Pre
-
processing


Converting the data


Epoching/Segmentation


Filtering


Artifact Detection/Rejection


Averaging


Re
-
referencing

Filtering


Types of filters:


highpass


lowpass


notch (stopband filter)


Butterworth (bandpass filter, backward and forward)

! (require signal processing toolbox in Matlab)


Effects of filtering the raw data

QuickTime™ and a
TIFF (Uncompressed) decompressor
are needed to see this picture.
QuickTime™ and a
TIFF (Uncompressed) decompressor
are needed to see this picture.
QuickTime™ and a
TIFF (Uncompressed) decompressor
are needed to see this picture.
Filtering in SPM

Overview Pre
-
processing


Converting the data


Epoching/Segmentation


Filtering


Artifact Detection/Rejection


Averaging


Re
-
referencing

Artifacts in EEG signal

Blinks

Eye
-
movements

Muscle activity

EKG

Skin potentials

Alpha waves


Eye blinks

QuickTime™ and a
TIFF (Uncompressed) decompressor
are needed to see this picture.
Eye movements

QuickTime™ and a
TIFF (Uncompressed) decompressor
are needed to see this picture.
Sweat artifacts


QuickTime™ and a
TIFF (Uncompressed) decompressor
are needed to see this picture.
Artefact detection
-

SPM

Artifact correction


Rejecting ‘artifact’ epochs costs you data


Using a simple artefact detection method will lead
to a high level of false
-
positive artifact detection


Rejecting only trials in which artifact occurs might
bias your data


Alternative methods of ‘Artifact Correction’ exist

Artifact correction
-

SPM


SPM uses a robust
average procedure to
weight each value
according to how far
away it is from the
median value for that
timepoint

Weighting

Value

Outliers are
given less
weight

Points close
to median
weighted ‘1’

Artifact correction
-

SPM


Normal
average


Robust
Weighted
Average

Robust averaging
-

SPM

Artifact avoidance


Blinking


Avoid contact lenses


Build ‘blink breaks’ into your paradigm


If subject is blinking too much


tell them


EMG


Ask subjects to relax, shift position, open mouth slightly


Alpha waves


Ask subject to get a decent night’s sleep beforehand


Have more runs of shorter length


talk to subject in between


Vary ISI


alpha waves can become entrained to stimulus

Overview Pre
-
processing


Converting the data


Epoching/Segmentation


Filtering


Artifact Detection/Rejection


Averaging


Re
-
referencing

Averaging

Averaging



S/N ratio increases as a
function of the square
root of the number of
trials.


As a general rule, it’s
always better to try to
decrease sources of noise
than to increase the
number of trials.


Averaging

Averaging


Assumes that only the EEG noise varies from trial
to trial


But


amplitude and latency will vary


Variable latency is usually a bigger problem than
variable amplitude

Averaging: effects of variance

Latency variation can be a
significant problem

Overview Pre
-
processing


Converting the data


Epoching/Segmentation


Filtering


Artifact Detection/Rejection


Averaging


Re
-
referencing

Re
-
referencing

It is important to re
-
reference the data in order to estimate a true,
nonarbitrary zero value to which to reference the voltage
measurements.


There are many different ways to re
-
reference, depending on the
experimental question. Possibly the best solution: average
reference, improves with increasing number of channels

Other option: linked mastoids, vertex, etc.

Re
-
referencing

Re
-
referencing

What comes next?


Visual inspection

of individual data


Grand mean


Statistical Analysis



General Recommendations


Use short blocks, ca. 2 min with breaks


Keep recording time under 45min


Keep it small and simple


Look for main effects and not for complex
interactions


Don’t go fishing!

References


Luck, S. J. (2005). An introduction to
the event
-
related potential technique.
Cambridge, MA: MIT Press.


Picton, T. W., Bentin, S., Berg, P.,
Donchin, E., Hillyard, E., Johnson, J.
R., et al. (2000). Guidelines for using
human event
-
related potentials to study
cognition: Recording standards and
publication criteria, Psychophysiology,
37, 127
-
152.


SPM Manual


Thank you!


And many thanks to Dr. Vladimir Litvak for
his advice!