Reconstructing ERP amplitude effects after compensating for trial-to-trial latency jitter: A solution based on a novel application of residue iteration decomposition.
Stimulus-locked averaged event-related potentials (ERPs) are among the most frequently used signals in Cognitive Neuroscience. However, the late, cognitive or endogenous ERP components are often variable in latency from trial to trial in a component-specific way, compromising the stability assumption underlying the averaging scheme. Here we show that trial-to-trial latency variability of ERP components not only blurs the average ERP waveforms, but may also attenuate existing or artificially induce condition effects in amplitude. Hitherto this problem has not been well investigated. To tackle this problem, a method to measure and compensate component-specific trial-to-trial latency variability is required. Here we first systematically analyze the problem of single trial latency variability for condition effects based on simulation. Then, we introduce a solution by applying residue iteration decomposition (RIDE) to experimental data. RIDE separates different clusters of ERP components according to their time-locking to stimulus onsets, response times, or neither, based on an algorithm of iterative subtraction. We suggest to reconstruct ERPs by re-aligning the component clusters to their most probable single trial latencies. We demonstrate that RIDE-reconstructed ERPs may recover amplitude effects that are diminished or exaggerated in conventional averages by trial-to-trial latency jitter. Hence, RIDE-corrected ERPs may be a valuable tool in conditions where ERP effects may be compromised by latency variability.
Department of Physics, Hong Kong Baptist University, Kowloon Tong, Hong Kong; Centre for Nonlinear Studies and The Beijing-Hong Kong-Singapore Joint Centre for Nonlinear and Complex Systems (Hong Kong), Institute of Computational and Theoretical Studies, Hong Kong Baptist University, Kowloon Tong, Hong Kong; Department of Psychology, Ernst Moritz Arndt Universität Greifswald, Germany.
Guang
G
Ouyang
Department of Psychology, Humboldt-Universität zu Berlin, D-10099 Berlin, Germany.
Werner
W
Sommer
Department of Physics, Hong Kong Baptist University, Kowloon Tong, Hong Kong; Centre for Nonlinear Studies and The Beijing-Hong Kong-Singapore Joint Centre for Nonlinear and Complex Systems (Hong Kong), Institute of Computational and Theoretical Studies, Hong Kong Baptist University, Kowloon Tong, Hong Kong; Research Centre, HKBU Institute of Research and Continuing Education, Virtual University Park Building, South Area Hi-tech Industrial Park, Shenzhen, China; Beijing Computational Science Research Center, Beijing, China. Electronic address: cszhou@hkbu.edu.hk.
Changsong
C
Zhou
D000328:Adult; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D004569:Electroencephalography / Q000379:methods*; D005071:Evoked Potentials / Q000502:physiology*; D006801:Humans; D012815:Signal Processing, Computer-Assisted
ERP; Latency correction; Latency variability; Method; Residue iteration decomposition