Preprocessing for EEG and MEG Przemek Tomalski Kathrin

Pre-processing for EEG and MEG Przemek Tomalski & Kathrin Cohen Kadosh

Recording EEG

Two crucial steps n n n 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 n Types of filters: highpass n lowpass n notch (stopband filter) n Butterworth (bandpass filter, backward and forward) ! (require signal processing toolbox in Matlab) n

highpass 0. 3 Hz Lowpass 30 Hz Raw data Effects of filtering the raw data

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

Eye movements

Sweat artifacts

Artefact detection - SPM

Artifact correction n n 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 n Weighting Value Outliers are given less weight Points close to median weighted ‘ 1’ SPM uses a robust average procedure to weight each value according to how far away it is from the median value for that timepoint

Artifact correction - SPM n n Normal average Robust Weighted Average

Robust averaging - SPM

Artifact avoidance n Blinking n Avoid contact lenses Build ‘blink breaks’ into your paradigm If subject is blinking too much – tell them n EMG n Ask subjects to relax, shift position, open mouth slightly n Alpha waves n n n 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 n n 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 n n n 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 n Use short blocks, ca. 2 min with breaks n Keep recording time under 45 min n Keep it small and simple n n 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. n 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. n SPM Manual n

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