EEG Deblurring Techniques in a Clinical Context

Cincotti, Febo; Babiloni, Claudio; Miniussi, Carlo; Carducci, F.; Moretti, Davide Vito; Salinari, S.; Pascual-Marqui, Roberto D.; Rossini, Paolo Maria; Babiloni, Fabio

doi:10.1055/s-0038-1633846

Cited by 26 publications

(12 citation statements)

References 2 publications

(3 reference statements)

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“…Particularly in clinical settings, for which high-resolution EEG is often not feasible, the surface Laplacian transformation can maximize the signal-to-noise ratio for the measure of interest (e.g., Cincotti et al, 2004; McFarland, 2014; McFarland et al, 1997). In the latter case, the merits of surface Laplacian are specifically exploited for individual data.…”

Section: Common Surface Laplacian Concernsmentioning

confidence: 99%

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

Kayser

Tenke

2015

International Journal of Psychophysiology

203

176

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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.

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Section: Common Surface Laplacian Concernsmentioning

confidence: 99%

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

Kayser

Tenke

2015

International Journal of Psychophysiology

203

176

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“…In a number of circumstances, these solutions partially or completely converge with results obtained using other methods that vary widely in their assumptions, constraints, or underlying theoretical models. Cincotti et al (2004) argued that a surface Laplacian and a distributed inverse model may both be useful as deblurring methods in a clinical context. Foffani et al (2004) went even further, noting that an alternative decomposition method (independent component analysis) compared favorably with scalp Laplacians computed using realistic scalp models.…”

Section: Impact Of Spatial Scale On Csd Implementationsmentioning

confidence: 99%

Generator localization by current source density (CSD): Implications of volume conduction and field closure at intracranial and scalp resolutions

Tenke

Kayser

2012

Clinical Neurophysiology

245

256

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The topographic ambiguity and reference-dependency that has plagued EEG/ERP research throughout its history are largely attributable to volume conduction, which may be concisely described by a vector form of Ohm’s Law. This biophysical relationship is common to popular algorithms that infer neuronal generators via inverse solutions. It may be further simplified as Poisson’s source equation, which identifies underlying current generators from estimates of the second spatial derivative of the field potential (Laplacian transformation). Intracranial current source density (CSD) studies have dissected the “cortical dipole” into intracortical sources and sinks, corresponding to physiologically-meaningful patterns of neuronal activity at a sublaminar resolution, much of which is locally cancelled (i.e., closed field). By virtue of the macroscopic scale of the scalp-recorded EEG, a surface Laplacian reflects the radial projections of these underlying currents, representing a unique, unambiguous measure of neuronal activity at scalp. Although the surface Laplacian requires minimal assumptions compared to complex, model-sensitive inverses, the resulting waveform topographies faithfully summarize and simplify essential constraints that must be placed on putative generators of a scalp potential topography, even if they arise from deep or partially-closed fields. CSD methods thereby provide a global empirical and biophysical context for generator localization, spanning scales from intracortical to scalp recordings.

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“…In this respect, cortical source estimation is likely to provide a better signal conditioning in preparation of BCI processing. In fact, it is well know that the estimation of the cortical activity from scalp EEG data by solving the linear inverse problem with realistic head models greatly enhances the spatial details available when compared to the Surface Laplacian methods (Babiloni et al, 2000;Cincotti et al, 2004a;He et al, 2006;Mattia et al, 2006;Nunez, 1995;Urbano et al, 1998).…”

Section: Brain-computer Interfacesmentioning

confidence: 99%

High-resolution EEG techniques for brain–computer interface applications

Cincotti

Mattia

Aloise

et al. 2008

Journal of Neuroscience Methods

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High-resolution electroencephalographic (HREEG) techniques allow estimation of cortical activity based on non-invasive scalp potential measurements, using appropriate models of volume conduction and of neuroelectrical sources. In this study we propose an application of this body of technologies, originally developed to obtain functional images of the brain's electrical activity, in the context of brain-computer interfaces (BCI).Our working hypothesis predicted that, since HREEG pre-processing removes spatial correlation introduced by current conduction in the head structures, by providing the BCI with waveforms that are mostly due to the unmixed activity of a small cortical region, a more reliable classification would be obtained, at least when the activity to detect has a limited generator, which is the case in motor related tasks.HREEG techniques employed in this study rely on (i) individual head models derived from anatomical magnetic resonance images, (ii) distributed source model, composed of a layer of current dipoles, geometrically constrained to the cortical mantle, (iii) depth-weighted minimum L 2 -norm constraint and Tikhonov regularization for linear inverse problem solution and (iv) estimation of electrical activity in cortical regions of interest corresponding to relevant Brodmann areas.Six subjects were trained to learn self modulation of sensorimotor EEG rhythms, related to the imagination of limb movements. Off-line EEG data was used to estimate waveforms of cortical activity (cortical current density, CCD) on selected regions of interest. CCD waveforms were fed into the BCI computational pipeline as an alternative to raw EEG signals; spectral features are evaluated through statistical tests (r 2 analysis), to quantify their reliability for BCI control. These results are compared, within subjects, to analogous results obtained without HREEG techniques. The processing procedure was designed in such a way that computations could be split into a setup phase (which includes most of the computational burden) and the actual EEG processing phase, which was limited to a single matrix multiplication. This separation allowed to make the procedure suitable for on-line utilization, and a pilot experiment was performed.Results show that lateralization of electrical activity, which is expected to be contralateral to the imagined movement, is more evident on the estimated CCDs than in the scalp potentials. CCDs produce a pattern of relevant spectral features that is more spatially focused, and has a higher statistical significance (EEG: 0.20 ± 0.114 S.D.; CCD: 0.55 ± 0.16 S.D.; p = 10 −5 ). A pilot experiment showed that a trained subject could utilize voluntary modulation of estimated CCDs for accurate (eight targets) on-line control of a cursor.This study showed that it is practically feasible to utilize HREEG techniques for on-line operation of a BCI system; off-line analysis suggests that accuracy of BCI control is enhanced by the proposed method.

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EEG Deblurring Techniques in a Clinical Context

Cited by 26 publications

References 2 publications

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

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

Generator localization by current source density (CSD): Implications of volume conduction and field closure at intracranial and scalp resolutions

High-resolution EEG techniques for brain–computer interface applications

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