This work was partially supported by Hong Kong Baptist University and the Hong Kong Research Grants Council (HKBU 202710) to G.O. and C.Z. and a grant from the State of Berlin (NaFöG) to G.H. We want to thank Olaf Dimigen for instructions about aspects of EEG data handling.
Residue iteration decomposition (RIDE): A new method to separate ERP components on the basis of latency variability in single trials
Article first published online: 6 SEP 2011
Copyright © 2011 Society for Psychophysiological Research
Volume 48, Issue 12, pages 1631–1647, December 2011
How to Cite
Ouyang, G., Herzmann, G., Zhou, C. and Sommer, W. (2011), Residue iteration decomposition (RIDE): A new method to separate ERP components on the basis of latency variability in single trials. Psychophysiology, 48: 1631–1647. doi: 10.1111/j.1469-8986.2011.01269.x
- Issue published online: 8 NOV 2011
- Article first published online: 6 SEP 2011
- (Received May 8, 2011; Accepted June 14, 2011)
- mental chronometry;
- event-related potentials;
- single-trial responses;
- component separation;
- face recognition
Event-related brain potentials (ERPs) are important research tools because they provide insights into mental processing at high temporal resolution. Their usefulness, however, is limited by the need to average over a large number of trials, sacrificing information about the trial-by-trial variability of latencies or amplitudes of specific ERP components. Here we propose a novel method based on an iteration strategy of the residues of averaged ERPs (RIDE) to separate latency-variable component clusters. The separated component clusters can then serve as templates to estimate latencies in single trials with high precision. By applying RIDE to data from a face-priming experiment, we separate priming effects and show that they are robust against latency shifts and within-condition variability. RIDE is useful for a variety of data sets that show different degrees of variability and temporal overlap between ERP components.