Simulation Studies of Latency Measures of Components of the Event-Related Brain Potential


  • This research was supported in part by a grant from the Air Force Office of Scientific Research, contract #F49620-83-0144, with Dr. Al Fregly as Project Director, and by a grant from the National Institute of Mental Health, MH41445. A partial report of the data was presented at the 24th Annual Meeting of the Society for Psychophysiological Research, Milwaukee, Wisconsin, October 18–21, 1984.

  • We wish to thank Monica Fabiani, Demetrios Karis, Greg Miller, Chris Wood, Bob Sclabassi, and an anonymous reviewer for their helpful comments on earlier versions of this paper.

Address requests for reprints to: Gabriele Gratton, University of Illinois, Psychology Department, 603 E. Daniel, Champaign, IL 61820.


We compared the accuracy of P300 latency estimates obtained with different procedures under several simulated signal and noise conditions. Both preparatory and signal detection techniques were used. Preparatory techniques included frequency filters and spatial filters (single electrode selection and Vector filter). Signal detection techniques included peak-picking, cross-correlation, and Woody filter. Accuracy in the latency estimation increased exponentially as a function of the signal-to-noise ratio. Both Woody filter and cross-correlation provided better estimates than peak-picking, although this advantage was reduced by frequency filtering. For all signal detection techniques, Vector filter provided better estimates than single electrode selection. Large component overlap impaired the accuracy of the estimates obtained with both single electrode selection and Vector filter, but with Vector filter impairment occurred only when the overlapping component had a scalp distribution that was similar to the scalp distribution of the signal component. The effects of varying noise characteristics, P300 duration and latency, and the parameters of Vector filter were also investigated.