Spline Laplacian estimate of EEG potentials over a realistic magnetic resonance-constructed scalp surface model

Carducci

et al. 2004

NeuroImage

Many recent neuroimaging studies of episodic memory have indicated an asymmetry in prefrontal involvement, with the left prefrontal cortex more involved than the right in encoding, the right more than the left in retrieval (hemispheric encoding and retrieval asymmetry, or HERA model). In this electroencephalographic (EEG) high-resolution study, we studied brain rhythmicity during a visual episodic memory (recognition) task. The theta (4-6 Hz), alpha (6-12 Hz) and gamma (28-48 Hz) oscillations were investigated during a visuospatial long-term episodic memory task including an encoding (ENC) and retrieval (RET) phases. During the ENC phase, 25 figures representing interiors of buildings ("indoor") were randomly intermingled with 25 figures representing landscapes ("landscapes"). Subject's response was given at left ("indoor") or right ("landscapes") mouse button. During the RET phase (1 h later), 25 figures representing previously presented "indoor" pictures ("tests") were randomly intermingled with 25 figures representing novel "indoor" ("distractors"). Again, a mouse response was required. Theta and alpha EEG results showed no change of frontal rhythmicity. In contrast, the HERA prediction of asymmetry was fitted only by EEG gamma responses, but only in the posterior parietal areas. The ENC phase was associated with gamma EEG oscillations over left parietal cortex. Afterward, the RET phase was associated with gamma EEG oscillations predominantly over right parietal cortex. The predicted HERA asymmetry was thus observed in an unexpected location. This discrepancy may be due to the differential sensitivity of neuroimaging methods to selected components of cognitive processing. The strict relation between gamma response and perception suggests that retrieval processes of long-term memory deeply impinged upon sensory representation of the stored material. © 2004 Elsevier Inc. All rights reserved

Section: Methodological Remarksmentioning

confidence: 99%

Section: Off-line Preliminary Data Analysismentioning

confidence: 99%

Section: Off-line Preliminary Data Analysismentioning

confidence: 99%

See 1 more Smart Citation

Human cortical EEG rhythms during long-term episodic memory task. A high-resolution EEG study of the HERA model

Carducci

et al. 2004

NeuroImage

“…The surface Laplacian estimation acts as a spatial filter that reduces head volume conductor effects and annuls electrode reference influence [2,38]. In some cases, the Laplacian values at the border electrodes were zeroed because of unreliability of the spline Laplacian estimate for these electrodes.…”

Section: Off-line Preliminary Data Analysismentioning

confidence: 99%

Sub-second “temporal attention” modulates alpha rhythms. A high-resolution EEG study

Miniussi

Cognitive Brain Research

et al. 2004

121

In the present high-resolution electroencephalographic (EEG) study, event-related desynchronization/synchronization (ERD/ERS) of alpha rhythms was computed during an S1 -S2 paradigm, in which a visual cue (S1) predicted a SHORT (600 ms) or LONG (1400 ms) foreperiod, preceding a visual go stimulus (S2) triggering right or left finger movement. Could orienting attention to a selective point in time influence the alpha rhythms as a function of the SHORT vs. LONG foreperiod? Stronger selective attentional modulations were predicted for the SHORT than LONG condition. EEG data from 54 channels were ''depurated'' from phase-locked visual evoked potentials and spatially enhanced by surface Laplacian estimation (i.e., final data analysis was conducted on 16 subjects having a sufficient number of artifact-free EEG single trials). Low-band alpha rhythms (about 6 -10 Hz) were supposed to be related to anticipatory attentional processes, whereas high-band alpha rhythms (10 -12 Hz) would indicate task-specific visuo-motor processes.Compared to the LONG condition (foreperiod), the SHORT condition induced a quicker and stronger ERS at low-band alpha rhythm (about 6 -8 Hz) over midline and bilateral prefrontal, sensorimotor, and posterior parietal areas. In contrast, the concomitant high-band alpha (about 10 -12 Hz) ERD/ERS showed no significant difference between the two conditions. In conclusion, temporal attention for a sub-second delay (800 ms) did modulate low-band alpha rhythm over large regions of both cortical hemispheres. D 2004 Elsevier B.V. All rights reserved.Theme: Neural basis of behavior Topic: Cognition

International Journal of Psychophysiology

“…Unfortunately, it was outside the scope of our work to discuss methods to make the scalp model more realistic and reduce error in electrode locations, as they require additional tools such as MRI that are often not readily available to most researchers. The interested reader is referred to the relevant references for this matter (Le and Gevins, 1993;Babiloni et al, 1996;Bortel and Sovka, 2013).…”

Section: Discussionmentioning

confidence: 99%

The surface Laplacian technique in EEG: Theory and methods

Carvalhaes¹,

Barros

2015

This paper reviews the method of surface Laplacian differentiation to study EEG. We focus on topics that are helpful for a clear understanding of the underlying concepts and its efficient implementation, which is especially important for EEG researchers unfamiliar with the technique. The popular methods of finite difference and splines are reviewed in detail. The former has the advantage of simplicity and low computational cost, but its estimates are prone to a variety of errors due to discretization. The latter eliminates all issues related to discretization and incorporates a regularization mechanism to reduce spatial noise, but at the cost of increasing mathematical and computational complexity. These and several others issues deserving further development are highlighted, some of which we address to the extent possible. Here we develop a set of discrete approximations for Laplacian estimates at peripheral electrodes and a possible solution to the problem of multiple-frame regularization. We also provide the mathematical details of finite difference approximations that are missing in the literature, and discuss the problem of computational performance, which is particularly important in the context of EEG splines where data sets can be very large. Along this line, the matrix representation of the surface Laplacian operator is carefully discussed and some figures are given illustrating the advantages of this approach. In the final remarks, we briefly sketch a possible way to incorporate finite-size electrodes into Laplacian estimates that could guide further developments.