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Residue iteration decomposition (RIDE): A new method to separate ERP components on the basis of latency variability in single trials

Authors

  • Guang Ouyang,

    1. Department of Physics, Hong Kong Baptist University, Kowloon Tong, Hong Kong
    2. Centre for Nonlinear Studies and The Beijing–Hong Kong–Singapore Joint Centre for Nonlinear and Complex Systems (Hong Kong), Hong Kong Baptist University, Kowloon Tong, Hong Kong
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  • Grit Herzmann,

    1. Department of Psychology and Neuroscience, University of Colorado at Boulder, Boulder, Colorado, USA
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  • Changsong Zhou,

    1. Department of Physics, Hong Kong Baptist University, Kowloon Tong, Hong Kong
    2. Centre for Nonlinear Studies and The Beijing–Hong Kong–Singapore Joint Centre for Nonlinear and Complex Systems (Hong Kong), Hong Kong Baptist University, Kowloon Tong, Hong Kong
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  • Werner Sommer

    1. Department of Psychology, Humboldt-Universität zu Berlin, Berlin, Germany
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  • 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.

Address correspondence to: Changsong Zhou, Department of Physics, Hong Kong Baptist University, Kowloon Tong, Hong Kong; e-mail: cszhou@hkbu.edu.hk; or Werner Sommer, Humboldt-Universität zu Berlin, Institut für Psychologie, Rudower Chaussee 18; e-mail: Werner.sommer@cms.hu-berlin.de

Abstract

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.

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