Evidence map›Paper›PMID 25920962›Full record

ReviewInternational journal of psychophysiology : official journal of the International Organization of Psychophysiology2015

Issues and considerations for using the scalp surface Laplacian in EEG/ERP research: A tutorial review.

Jürgen Kayser, Craig E Tenke

Abstract readReview
In one paragraph

Review in International journal of psychophysiology : official journal of the International Organization of Psychophysiology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 82 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
82citing papers in PubMed, 4 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

82 citing papers in PubMed, 4 syntheses or guidelines pooled it.

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  13. Looking into Working Memory to Verify Potential Search Targets.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025
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  20. Novel methodology for detection and prediction of mild cognitive impairment using resting-state EEG.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2024
    Article

22 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Jürgen KayserDivision of Cognitive Neuroscience, New York State Psychiatric Institute, New York, NY, USA; Department of Psychiatry, Columbia University College of Physicians & Surgeons, New York, NY, USA. Electronic address: kayserj@nyspi.columbia.edu.
Craig E TenkeDivision of Cognitive Neuroscience, New York State Psychiatric Institute, New York, NY, USA; Department of Psychiatry, Columbia University College of Physicians & Surgeons, New York, NY, USA.

Funding

Three Generations at High and Low Risk for Depression Followed LongitudinallyR01MH036197 · NIMH · YALE UNIVERSITY · PI POSNER, JONATHAN E, WEISSMAN, MYRNA M · 1985 to 2020
$15.0M
Biosignatures of Treatment Remission in Major DepressionU01MH092250 · NIMH · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI MCGRATH, PATRICK J, PARSEY, RAMIN V. · 2010 to 2015
$8.9M
BEHAVIORAL, ERP AND RCBF ASYMMETRY IN AFFECTIVE DISORDERR01MH036295 · NIMH · NEW YORK STATE PSYCHIATRIC INSTITUTE DBA RESEARCH FOUNDATION FOR MENTAL HYGIENE, INC · PI BRUDER, GERARD E · 1985 to 2012
$3.7M
Olfaction and Emotion in Depression: Behavioral Electrodermal and ERP MeasuresR21MH082393 · NIMH · NEW YORK STATE PSYCHIATRIC INSTITUTE DBA RESEARCH FOUNDATION FOR MENTAL HYGIENE, INC · PI KAYSER, RALF JÜRGEN · 2008 to 2009
$396k
Time-Frequency Measures for Predicting Response to AntidepressantsR03MH094356 · NIMH · NEW YORK STATE PSYCHIATRIC INSTITUTE DBA RESEARCH FOUNDATION FOR MENTAL HYGIENE, INC · PI BRUDER, GERARD E, TENKE, CRAIG E · 2011 to 2012
$160k
NIMH NIH HHS MH036197NIMH NIH HHS MH036295NIMH NIH HHS MH082393NIMH NIH HHS MH092250NIMH NIH HHS MH094356NIMH NIH HHS R01 MH036197NIMH NIH HHS R01 MH036295NIMH NIH HHS R03 MH094356NIMH NIH HHS R21 MH082393NIMH NIH HHS U01 MH092250
6 · The paper itself

Abstract

Despite the recognition that the surface Laplacian may counteract adverse effects of volume conduction and recording reference for surface potential data, electrophysiology as a discipline has been reluctant to embrace this approach for data analysis. The reasons for such hesitation are manifold but often involve unfamiliarity with the nature of the underlying transformation, as well as intimidation by a perceived mathematical complexity, and concerns of signal loss, dense electrode array requirements, or susceptibility to noise. We revisit the pitfalls arising from volume conduction and the mandated arbitrary choice of EEG reference, describe the basic principle of the surface Laplacian transform in an intuitive fashion, and exemplify the differences between common reference schemes (nose, linked mastoids, average) and the surface Laplacian for frequently-measured EEG spectra (theta, alpha) and standard event-related potential (ERP) components, such as N1 or P3. We specifically review common reservations against the universal use of the surface Laplacian, which can be effectively addressed by employing spherical spline interpolations with an appropriate selection of the spline flexibility parameter and regularization constant. We argue from a pragmatic perspective that not only are these reservations unfounded but that the continued predominant use of surface potentials poses a considerable impediment on the progress of EEG and ERP research.

Indexed as

Brain MappingMathematicsScalpAnimalsBrainElectroencephalographyEvoked PotentialsHumansCurrent source density (CSD)EEG recording referenceEEG spectraERP componentEvent-related potentials (ERPs)Spherical spline interpolationSurface Laplacian (SL)Volume conduction

Identifiers

PMID25920962
PMCPMC4537804

What OpenQuestion holds

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Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.