Optimal digital filters for long-latency components of the event-related brain potential.

Abstract:

:A fundamentally important problem for cognitive psychophysiologists is selection of the appropriate off-line digital filter to extract signal from noise in the event-related brain potential (ERP) recorded at the scalp. Investigators in the field typically use a type of finite impulse response (FIR) filter known as moving average or boxcar filter to achieve this end. However, this type of filter can produce significant amplitude diminution and distortion of the shape of the ERP waveform. Thus, there is a need to identify more appropriate filters. In this paper, we compare the performance of another type of FIR filter that, unlike the boxcar filter, is designed with an optimizing algorithm that reduces signal distortion and maximizes signal extraction (referred to here as an optimal FIR filter). We applied several different filters of both types to ERP data containing the P300 component. This comparison revealed that boxcar filters reduced the contribution of high-frequency noise to the ERP but in so doing produced a substantial attenuation of P300 amplitude and, in some cases, substantial distortions of the shape of the waveform, resulting in significant errors in latency estimation. In contrast, the optimal FIR filters preserved P300 amplitude, morphology, and latency and also eliminated high-frequency noise more effectively than did the boxcar filters. The implications of these results for data acquisition and analysis are discussed.

journal_name

Psychophysiology

journal_title

Psychophysiology

authors

Farwell LA,Martinerie JM,Bashore TR,Rapp PE,Goddard PH

doi

10.1111/j.1469-8986.1993.tb03357.x

subject

Has Abstract

pub_date

1993-05-01 00:00:00

pages

306-15

issue

3

eissn

0048-5772

issn

1540-5958

journal_volume

30

pub_type

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