Abstract
A general approach to time domain digital filtering is described, and examples of some filters used in EEG/ERP research are presented. Simulations are reported that evaluate the impact of the relative length of the filter weight series and the signal cycle to be filtered, the span and real-time density of the filter weights, and slow drift across the epoch being filtered. Results indicate that some filters commonly used in the EEG/ERP literature are inadequate. Frequency domain digital filtering is also briefly discussed. The fast Hartley transform, a fast but relatively unknown computational method for frequency domain filtering of ERP/EEG data, is introduced and compared with time domain filtering. Some practical recommendations are provided.
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Portions of the introductory material in this paper are based on a more general tutorial on filtering (Cook & Miller, 1992). J.B.N. was supported in part by NIMH Grant T32 MH14257 to Lawrence Jones, and this project was supported by NIMH Grant R01 MH39628 to G.A.M. and by the Departments of Psychology and Psychiatry and the Beckman Institute of the University of Illinois. We gratefully acknowledge the contributions of Richard Davidson, Blair Hicks, Brandy Isaacks, James Long, Bruce Wheeler, and especially Joseph Senulis.
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Nitschke, J.B., Miller, G.A. & Cook, E.W. Digital filtering in EEG/ERP analysis: Some technical and empirical comparisons. Behavior Research Methods, Instruments, & Computers 30, 54–67 (1998). https://doi.org/10.3758/BF03209416
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DOI: https://doi.org/10.3758/BF03209416