Response-Time Corrected Averaging of Event-Related Potentials

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1 Response-Time Corrected Averaging of Event-Related Potentials Hecke Schrobsdorff Bernstein Center for Computational Neuroscience Göttingen University of Göttingen, Institute for Nonlinear Dynamics CNS*2007 Workshop Synchronization of brain signals: What is real, what is not?

2 Outline 1 Introduction 2 Event Related Potentials 3 Response Latency Correction 4 Discussion

3 Introduction Introduction What are ERP components? Our Background BCCN Project C4: aging effects in selective attention joint project of modelers and experimentalists main research tool: negative priming EEG recordings to access internal mechanisms A Modelers Interest in ERPs clarification of temporal variance localization of mechanisms in time and space

4 Introduction A Physicists Questions If the components of event related potentials reflect different processing stages during an experimental trial, how can they be found by pure grand averaging? Problems to cope with signal to noise ratio 10% strong variation in a subjects reaction times huge interindividual reaction time differences different strategies amongst subjects

5 Introduction Eliminating Reaction Time Variance Main Statement Behavioral reaction times can be used to normalize the time interval between stimulus onset and response for averaging. But where does the temporal variance come from? A linear stretch is unlikely. At least perceptual stages are highly automatic. Which processing stages contribute how strong to the reaction time variance? Is this question reasonable?

6 Event Related Potentials EEG Data Cleaning downsampling to 500Hz band pass filter: [0.2,20]Hz conditional segmentation [-100,1500]ms baseline correction for [-100,0]ms ocular correction (Gratton & Coles), VEOG channel: FPz reconstruction of FPz = (FP1+FP2)/2 individual low cutoff filter 0.5Hz baseline correction for [-100,0]ms artifact rejection, bounds [-100,100]µV) 10% trials excluded new reference: (TP10+TP9)/2, reconstruction of FCz average

7 TP9 FT7 T7 TP7 F7 P7 FC5 C5 CP5 AF7 F5 P5 C3 PO7 F3 FC3 CP3 P3 Fpz Fp1 AF3 AFz O1 F1 FC1 C1 CP1 PO3 P1 Fz Cz CPz Pz POz Oz F2 Fp2 FC2 C2 CP2 P2 AF4 O2 F4 P4 PO4 FC4 C4 CP4 AF8 F6 P6 PO8 F8 FC6 C6 P8 FT8 T8 CP6 TP8 Response-Time Corrected Averaging of Event-Related Potentials Event Related Potentials ERP Analysis an old slide NP PP CO 10µV 1s J. Behrendt, H. Gibbons, HS, M. Ihrke, J. M. Herrmann, M. Hasselhorn Event-Related Brain Potential Correlates of Identity Negative Priming, in preparation

8 TP9 FT7 T7 TP7 F7 P7 FC5 C5 CP5 AF7 F5 P5 C3 PO7 F3 FC3 CP3 P3 Fpz Fp1 AF3 AFz O1 F1 FC1 C1 CP1 PO3 P1 Fz Cz CPz Pz POz Oz F2 Fp2 FC2 C2 CP2 P2 AF4 O2 F4 P4 PO4 FC4 C4 CP4 AF8 F6 P6 PO8 F8 FC6 C6 P8 FT8 T8 CP6 TP8 Response-Time Corrected Averaging of Event-Related Potentials Event Related Potentials ERP Analysis an old slide P5 NP PP CO NP PP CO P6 NP PP CO 10µV 1s J. Behrendt, H. Gibbons, HS, M. Ihrke, J. M. Herrmann, M. Hasselhorn Event-Related Brain Potential Correlates of Identity Negative Priming, in preparation

9 Event Related Potentials Behavioral Data Reaction Times for the ERPs RT SD effect Cohen d [ms] [ms] [ms] effect CO PP NP Both effects are highly significant. How can we trust EEG-component differences that... needed such a complex data cleaning, and still include a temporal variance of >20%. So why not normalize the reaction times?

10 Response Latency Correction Response Latency Correction What is the real ERP? We want to maximize the quality of the ERP average. Therefore we have to minimize the impact of reaction time variance on the ERP average. Assumptions There is a real ERP signal u(t) in every trial. In the measured signal u i (t), the real ERP u(t) is distorted by possible time shifts of components and very strong noise. Components still have the same order. Component latency differences correlate with reaction time differences.

11 Response Latency Correction Response Latency Correction We are looking for a map TW : R + C 0, TW(RT i ) = {φ i : [0, RT i ] [0, mean(rt)]} argument: the current trial i s reaction time RT i maps to: a time-warp function φ i which maps the interval of the current trial to the duration of the average of all trials Properties of φ i monotonically increasing (no doubling of components) continuous (no jumps in time) it minimizes u(t) u i (φ i (t)) in some norm

12 Response Latency Correction The first attempt towards RLC Use a static nonlinear time-warping function Calculate the new latency of samplepoint t in trial i according to: ( ) t k φ i (t) = t + (RT RT i ) k = 1, 2, 3, 4 RT i early components are hardly shifted late samplepoints carry most shift applied to optimizing only P300 k = 3 performed best. RTi RT RTj RT H. Gibbons, J. Stahl. Response-time correceted averaging of event-related potentials, Clinical Neurophysiology, (118): , 2007.

13 Response Latency Correction A More Flexible Approach We need a cleaner signal Single trial ERPs with wavelets RMSE=1.63 SNR= Finding φ i Dissimilarity measure d(s(x), u(y)) := s(x) ũ(y) + s (x) ũ (y) s(x) := s(x) s x s 2 x determining min( s u d ) by finding the minimal path through the matrix d jk = d(s j, u k ) T. Picton, M. Hunt, R. Mowrey, R. Rodriguez and J. Maru Evaluation of brain stem auditory evoked potentials using dynamic time warping, Electroencephalography and Clinical Neurophysiology, 71(3):212 25,1988

14 Response Latency Correction The Time-Warping Algorithm Input EEG data with time markers for trial onset and reaction time. 1 Calculate the ERP average (classically). 2 Determine the φ i. 3 Time warp the data of every trial. 4 Calculate a cleaner average based on the warped trials. 5 Iterate with the new average until convergence Output ERP average without variance due to reaction time differences.

15 Response Latency Correction Alternative Approach Model the φ i as one chain of springs Assumes a common source of the temporal variance. Directly shows the processing steps that vary most. Needs harder calculations.

16 Response Latency Correction Performance Measures How good is our method? Consider the pointwise variance of the warped trials. Generate artificial Data and compare the output with the known underlying ERP Single trials, generated with gaussian rts with sd= Sample Trial with white gaussian noise added Mean of single trials (black curve is error) Mean of trials with white gaussian noise added

17 Discussion Self-Critical Questions Do ERPs exist? Is it possible to extract meaningful single-trial ERPs? Do we destroy the meaning of components by the method? All psychological interpretations were done with the classical averaging. Do we generate artificial components by shifting the trials until there are correlations? What do you think?

18 Discussion Conclusion Take Home Message Outlook A response-time correction before averaging is necessary. We know how to do it. At least roughly. The φ i can tell us, where the response-time variance comes from. Implementation of the algorithm with your comments. Optimization of the algorithm. Plugin for the free matlab package EEGLAB.

19 Discussion Thanks to the Experts Henning Gibbons Torsten Wüstenberg Ralph Meier Miguel Valencia Ustárroz... to the BCCN People Theo Geisel Michael Herrmann Marcus Hasselhorn Tobias Niemann Jörg Behrendt Matthias Ihrke... and to You!

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