Issues and considerations for using the scalp surface Laplacian in EEG/ERP research: A tutorial review.
- Authors
- Kayser, JΓΌrgen; Tenke, Craig E
- Year
- 2015
- Journal
- International journal of psychophysiology : official journal of the International Organization of Psychophysiology
- PMID
- 25920962
- DOI
- 10.1016/j.ijpsycho.2015.04.012
- PMCID
- PMC4537804
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.
Schematic sagittal view of the brain with two scalp electrodes (E1, E2). (A) Given a cortical dipole pointing with its negative pole towards E1, a negative potential is measured by the voltage meter for site E1 if site E2 serves as the recording reference (Ref). (B) For the same dipole, a positive potential is measured for E2 if E1is used as the reference. In either case, however, the measured potential difference indicates that E2 is more positive than E1.
LLM interpretation
This figure consists of two schematic sagittal diagrams (A and B) of a brain with two scalp electrodes, E1 and E2, measuring a cortical dipole. In panel A, E1 is the recording site and E2 is the reference, resulting in a negative potential measurement. In panel B, the roles are reversed with E2 as the recording site and E1 as the reference, resulting in a positive potential measurement.
Frequency spectra (3β15 Hz) at selected anterior (AFz, F3) and posterior (POz, P3) sites (A) and corresponding peak theta (5β6 Hz) and peak alpha (10β11 Hz) scalp distributions (B, C; mean of shaded frequency ranges in A) of an individual participant during an auditory working memory task. Shown are mean fast Fourier transform (FFT) amplitudes derived from 220 8-s epochs (72-channel EEG montage, 256 samples/s, 20% Hanning taper window, resolution 0.125 Hz) converted to common surface potential [Β΅V] references (NR: nose reference; LM: link-mastoids reference; AR: average reference) and also transformed to current source densities (CSD [Β΅V/cm2]; spherical spline parameters: m = 4, Ξ» = 10β5, 50 iterations; Perrin et al., 1989). Anterior and posterior sites were selected to reflect both the topographical maximum of theta and alpha as well as nearby (off-maximum) locations (marked circles in B and C).
LLM interpretation
This figure consists of frequency spectra (A) and scalp topography maps (B, C) for a single participant during an auditory working memory task. Panel A shows FFT amplitudes across frequencies (3β15 Hz) at four electrode sites (AFz, F3, POz, P3), with shaded regions highlighting peak theta (5β6 Hz) and alpha (10β11 Hz) activity. Panels B and C display the spatial distribution of these peak frequencies across the scalp using four different reference/transformation methods: nose reference (NR), link-mastoids (LM), average reference (AR), and current source density (CSD).
Grand mean waveforms (-60 to 800 ms) at selected midline sites (Cz, Pz) for target (A) and novel (B) stimuli and corresponding topographies of mean P3b (time interval 320β420 ms) and P3a (220β320 ms) amplitudes (C-F; shaded ranges in A and B) recorded during an auditory novelty oddball task (data of 49 healthy adults from Tenke et al., 2010). Compared are common surface potential [Β΅V] references (NR: nose reference; LM: link-mastoids reference; AR: average reference) and the surface Laplacian transformations (CSD [Β΅V/cm2]; spherical spline parameters: m = 4, Ξ» = 10β5, 50 iterations; Perrin et al., 1989). Selected intervals and sites correspond to latency peaks and topographical maxima of P3b and P3a (marked circles in C-F).
LLM interpretation
This figure presents EEG data from an auditory novelty oddball task, featuring grand mean waveforms (A, B) and corresponding scalp topographies (C-F). The waveforms show voltage/current density over time at sites Cz and Pz for target and novel stimuli, with shaded regions indicating the P3a (220β320 ms) and P3b (320β420 ms) time intervals. The topographies compare four reference/transformation methods (NR, LM, AR, and CSD), illustrating the spatial distribution of amplitudes for these two components across the scalp.
Grand mean difference waveforms (-40 to 440 ms) at selected sites (TP9, FC3, Cz, FC6) derived from deviant-minus-standard tones (A) and corresponding mismatch negativity (MMN) topographies (B; mean amplitudes for time interval 100β200 ms as indicated by shaded ranges in A) recorded during a frequency (pitch) MMN paradigm (unpublished data of 8 healthy adults, 72-channel EEG montage, 256 samples/s; see text for further method details). Selected sites correspond to topographical MMN minima and maxima (marked circles in B), which differ between ERP and CSD data (ERP references and CSD transformations as in Fig. 11).
LLM interpretation
Figure A consists of four line graphs showing difference waveforms (voltage/current density vs. latency in ms) at EEG sites TP9, FC3, Cz, and FC6, with shaded regions indicating the 100β200 ms time interval. The waveforms compare three reference conditions (NR, LM, AR) and a CSD transformation, showing varying peak amplitudes and polarities across the selected sites. Figure B displays four corresponding topographic heatmaps for NR, LM, AR, and CSD, illustrating the spatial distribution of MMN amplitudes across the scalp.
Response- (A) and stimulus-locked (C) grand mean waveforms (N = 17) recorded with a 129-channel geodesic sensor net EEG system (Tucker, 1993) during tonal and phonetic oddball tasks requiring a left or right response button press to target stimuli (data from Kayser and Tenke, 2006b). Baseline corrections for response- and stimulus-locked ERPs are β300 to β100 ms and β100 to 0 ms, respectively. Topographies correspond to a mid-frontal response-related negativity (FRN) peaking at about 50 ms post response (B: mean amplitude during time interval 25β75 ms; shaded ranges in A) and approximately 500 ms post stimulus onset (D: 420β580 ms; shaded ranges in C). Selected sites (see insets for electrode names in A and C) were left mid-central (43), left frontocentral (13), right frontocentral (107) and right mid-central (94) for response-locked waveforms, and left and right central (38, 88), mid-frontocentral (6) and mid-centroparietal (62) for stimulus-locked waveforms, which approximated CSD sink and source maxima (marked circles in B and D; ERP references and CSD transformations as in Fig. 11).
LLM interpretation
This figure presents EEG grand mean waveforms and corresponding scalp topographies for response-locked (A, B) and stimulus-locked (C, D) conditions. The waveforms show voltage changes over time for left and right button presses across selected electrode sites, with shaded regions indicating the time intervals for the mid-frontal response-related negativity (FRN). The topographies visualize the spatial distribution of mean amplitudes for non-target (NR), left-target (LM), and right-target (AR) stimuli, alongside current source density (CSD) maps.
Schematic of interaction between the strength and orientation of a neuronal generator (modeled as a dipole) and choice of EEG reference. Assumed are two hypothetical EEG reference schemes in which reference (-) and recording (+) sites are either placed in line (A, C) or perpendicular (B, D) to the orientation of one dipole (green). A. Two dipoles of equal strength but different orientation will yield different surface potentials in favor of the green dipole which orientation best matches the alignment of reference and recording site. B. The same constellation of dipoles will favor the red dipole if reference and recording site are vertically aligned to the green dipole (i.e., rendering no potential difference between reference and recording site). C. Two dipoles of different strength but equal orientation will favor the green dipole if reference and recording site are aligned in parallel to the orientation of these dipoles. D. However, even in this scenario, a vertical alignment of reference and recording site to the orientation of these dipoles will render the same (i.e., zero) surface potential for either dipole.
LLM interpretation
This figure is a four-panel schematic diagram (A-D) illustrating how the orientation and strength of neuronal dipoles (red and green) interact with EEG reference and recording site placement. Panels A and C show recording sites aligned parallel to the dipoles, while panels B and D show them placed perpendicularly. The diagrams demonstrate that surface potential is maximized when the dipole orientation matches the alignment of the recording (+) and reference (-) sites, and is nullified when they are perpendicular.
Ratio of scalp measure to cortical potential as a function of dipole layer size. A, B: Vertex scalp potentials for a 81-channel 10β10 system EEG montage (average reference) due to simulated dipole layers of varying angular extent, forming superficial spherical caps in a four-shell forward solution (Berg, 2006) for two different ratios of brain-to-skull conductivity with the following conductivities [S/m]: brain = 0.33, skull = 0.0042 (A: ratio = 78.6) or 0.0084 (B: ratio = 39.3), CSF = 1.0, scalp = 0.33. C, D: Corresponding surface Laplacian estimates (CSD: current source density) using spherical splines of different flexibility (m = 2β7; Ξ» = 10β5; Perrin et al., 1989). Note that surface potentials are primarily sensitive to broad dipole layers, whereas the sensitivity of spherical spline surface Laplacian estimates varies with spline flexibility.
LLM interpretation
This figure consists of four line plots showing the relationship between dipole layer cap size (x-axis) and either surface potential (A, B) or current source density (CSD) (C, D). Panels A and B show that surface potential peaks at a cap size of approximately 10 cm, with a higher peak magnitude for the lower brain-to-skull conductivity ratio (39.3). Panels C and D show CSD estimates for various spline flexibilities ($m=2$ to $7$), where increasing $m$ shifts the peak sensitivity toward larger cap sizes and reduces the peak amplitude.
Impact of the EEG reference (NR: nose reference; LM: linked-mastoids reference; AR: average reference) on grand mean (N = 44) ERP [Β΅V] waveforms (-100 to 1100 ms, 100 ms pre-stimulus baseline) to foveal presentations of common words (A) or unknown faces (B) recorded during a continuous recognition memory task (data of healthy adults from Kayser et al., 2010). Enlargements of selected sites are shown on the right to highlight the substantial differences in ERP waveforms between references, although these can be observed at all sites. Rectangle enlargements directly compare ERPs referenced to linked mastoids at sites TP9 and TP10 (i.e., left and right mastoid), revealing symmetric activity with opposite polarity.
LLM interpretation
This figure consists of two panels (A for Words, B for Faces) showing event-related potential (ERP) waveforms across multiple scalp electrode sites for three different EEG references: nose (NR, green), linked-mastoids (LM, red), and average (AR, blue). The x-axis represents latency in milliseconds (-100 to 1100 ms) and the y-axis represents amplitude in microvolts ($\mu$V). Enlarged circular and rectangular insets highlight substantial differences in waveform polarity and morphology between the three reference types, specifically demonstrating symmetric activity with opposite polarity at sites TP9 and TP10 when using the linked-mastoids reference.
Selected ERP waveforms (colored lines, PO4: thick, T8: thin) for words (column 1) and faces (column 2) as shown in Fig. 2, and ERP difference waveforms (i.e., faces-minus-words; column 3), comparing three common EEG references (NR: nose reference; LM: linked-mastoids reference; AR: average reference; colors as in Fig. 2). The shaded areas and the black dashed lines depict the differences between the two ERPs shown in each subgraph (i.e., thick-minus-thin colored lines). Relative ERP differences between any two sites are unaffected by the EEG reference, as revealed by identical difference waveforms in each column (dashed lines). It is obvious that any attempt to quantify deflections (i.e., peak and troughs) of the colored lines will yield different results for each EEG reference, notwithstanding their invariance in topography across references.
LLM interpretation
This figure consists of a 3x3 grid of Event-Related Potential (ERP) waveforms comparing three EEG references (NR in green, LM in red, AR in blue) across three conditions: Words, Faces, and a difference wave (Faces minus Words). Each plot displays waveforms for two electrode sites (PO4 as thick lines, T8 as thin lines), with shaded areas and dashed lines representing the difference between these two sites. While the absolute waveforms vary significantly by reference, the dashed difference lines remain identical across the rows within each column.
First and (negative) second (spatial) derivatives of a data series measured at one time point (sample) across nine (scalp) locations (labeled A-I), with negative values plotted upwards. The resulting differences between consecutive data points (1st derivative) are plotted half-way between locations (gray squares). Analogously, the resulting (negative) differences between consecutive difference values (2nd derivative) are plotted half-way between these intermediate locations (i.e., the middle of a 3-point computation; red squares).
LLM interpretation
This line plot displays a data series (green circles) and its first (gray squares) and negative second (red squares) spatial derivatives across nine scalp locations labeled A-I. The y-axis is inverted, with negative values plotted upwards. The data series shows a peak between locations D and E, while the derivatives highlight the rate of change and curvature between these locations.
Local Hjorth differentiation grid consisting of 3β5 nearest neighbors for 67-channel EEG montage (cf. Fig. 2). Mutual and single (one-directional) neighbors are marked by blue and red arrows for each scalp site.
LLM interpretation
This figure is a network diagram representing a 67-channel EEG montage on a circular scalp map. Nodes represent scalp sites, connected by blue arrows indicating mutual neighbors and red arrows indicating single (one-directional) neighbors. The layout illustrates a local Hjorth differentiation grid where each site is linked to 3β5 nearest neighbors.
(A) Topographies of grand mean ERPs [Β΅V] to word stimuli (Fig. 2A) at the peak latency for N1 (144 ms) and P3 (560 ms) referenced to nose (NR), linked mastoids (LM) or the average of all 67 sites (AR). (B) Local Hjorth estimates of these N1 and P3 topographies using differentiation grids consisting of 3β5 (cf. Fig. 5), 8β9, 24β25 or 66 nearest neighbors. All topographies were created from the ERP or local Hjorth values at each sites using a spherical spline surface Laplacian interpolation (m = 2; Ξ» = 0; Perrin et al., 1989).
LLM interpretation
This figure presents EEG topographic maps of grand mean ERPs (A) and local Hjorth estimates (B) for N1 and P3 components. Panel A compares three reference conditions (NR, LM, AR), while Panel B shows the effect of varying nearest-neighbor differentiation grids (3β5, 8β9, 24β25, 66). Color scales indicate voltage in $\mu$V, with red representing positive and blue representing negative values.
Data interpolation of discrete data values (green circles, top panel; cf. Fig. 4) and corresponding surface Laplacian estimates (negative second spatial derivative, bottom panel) using spherical splines of different flexibility (m = 2β5; Ξ» = 10β5; Perrin et al., 1989). Because the equivalent spacing of the 9 locations labeled A-I is identical to the scalp surface distances of 9 positions in the coronal plane of the 10β10 system, ranging from T7 to T8 (Jurcak et al., 2007), the hypothetical data series may be conceived as an ERP topography having a negative maximum at vertex (e.g., N1 peak at Cz), with corresponding units of Β΅V (surface potential) and Β΅V/cm2 (surface Laplacian).
LLM interpretation
This figure consists of two line plots showing data interpolation and corresponding surface Laplacian estimates across nine locations (labeled A-I and T7-T8). The top panel displays discrete data values (green circles) fitted with spherical splines of varying flexibility ($m = 2β5$), showing a negative peak at the center. The bottom panel shows the resulting surface Laplacian estimates, where lower flexibility values (e.g., $m=2$, red line) produce high-amplitude oscillations compared to the smoother curves of higher flexibility values.
A, B. Grand mean ERP [Β΅V] topography of auditory N1 (AR: average reference; unpublished data, N = 164, 112 ms peak amplitude, 80 dB tones presented during a loudness intensity paradigm) and corresponding surface Laplacian estimates (CSD: current source density [Β΅V/cm2]) using spherical splines of different flexibility (m = 2β5; Ξ» = 10β5; Perrin et al., 1989). Topographies were created for the original 72-channel EEG montage (A) and a subset consisting of 31 scalp locations (B). Gray circles mark scalp locations in the coronal plane. Due to the large differences in amplitude range associated with variations of spline flexibility, asymmetric scales were used to optimally represent the scalp distributions of ERP and CSD values; however, the ratios between positive and negative extremes are the same for each scale to preclude distortion of their relative value (i.e., green represents zero in all scales). C, D. Visual N1 and P3 (AR as shown in Fig. 2A) and corresponding CSD topographies of different spline flexibility (m = 2β5; Ξ» = 10β5) using symmetric scales adjusted to the data range for each map.
LLM interpretation
This figure presents a series of EEG scalp topography maps comparing average reference (AR) ERPs and current source density (CSD) estimates across four conditions (A-D) and five different spline flexibility levels (m = 2β5). Rows A and B show auditory N1 topographies for a 72-channel montage and a 31-channel subset, while rows C and D show visual N1 and P3 topographies. Each map is accompanied by a color scale indicating voltage ($\mu$V) or current density ($\mu$V/cmΒ²), with green representing zero.
Surface Laplacian estimates [Β΅V/cm2] employing optimized smoothing for different spherical spline orders (m = 2β5). The optimal value for Ξ» was separately determined for each spline flexibility by computing the cross-validation (CV) criterion from the individual auditory N1 topographies (N = 164) at 112 ms constituting the overall grand mean 72-channel ERP topography (Fig. 8A). Accordingly, the sum of the squared differences between the observed potential and the estimated potential (i.e., using a 71-channel spherical spline interpolation with m flexibility and Ξ» smoothing) was repeatedly computed for each of the 164 topographies at any site for different Ξ» values to determine the minimum of the underlying function corresponding to a particular value of m (cf. Pascual-Marqui et al., 1988; Stone, 1974).
LLM interpretation
This figure consists of four topographic heatmaps showing surface Laplacian estimates [Β΅V/cmΒ²] for different spherical spline orders ($m = 2, 3, 4, 5$) and their corresponding optimized smoothing values ($\lambda$). Each map displays a distribution of electrical potentials across a 72-channel scalp layout, with color scales indicating voltage values ranging from negative (blue) to positive (red). As the spline order $m$ increases and $\lambda$ decreases, the spatial distribution of the potentials becomes smoother and more consistent across the maps.
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External
| Title | Authors | Journal | Year | Link |
|---|---|---|---|---|
| Boosting Spatial Properties of Single-Flicker SSVEP via Laplacian Electrodes. | Luo R et al. | β | 2025 | β |
| Characterizing the Heartbeat-Evoked Potential: A Two-Component Model of Cardiac Signal Processing? | Gautier R et al. | β | 2025 | β |
| Common and distinct neural substrates of rule- and similarity-based category learning. | Li J et al. | β | 2025 | β |
| Cortical activation and functional connectivity in visual-cognitive-motor networks during motor-cognitive exercise. | HΓΌlsdΓΌnker T et al. | β | 2025 | β |
| Decoding brain signals: A comprehensive review of EEG-Based BCI paradigms, signal processing and applications. | Yadav H et al. | β | 2025 | β |
| EEG-based information transfer paths during motion discrimination: Detectable differences between normal cognition and MCI. | Renli A et al. | β | 2025 | β |
| EEG is better when cleaning effectively targets artifacts. | Bailey NW et al. | β | 2025 | β |
| EEG theta and alpha biomarkers during an avoid-avoid conflict task: Links to anxiety. | Stocker B et al. | β | 2025 | β |
| Effects of Different Preprocessing Pipelines on Motor Imagery-Based Brain-Computer Interfaces. | Gao X et al. | β | 2025 | β |
| Fresh Pecorino Cheese Produced by Ewes Fed Silage with Prickly Pear By-Products: VOC, Chemical, and Sensory Characteristics Detected with a Neuro-Sensory Approach Combining EEG and TDS. | Gannuscio R et al. | β | 2025 | β |
| Genetic modulation of brain dynamics in neurodevelopmental disorders: the impact of copy number variations on resting-state EEG. | Dubois AEE et al. | β | 2025 | β |
| Looking into Working Memory to Verify Potential Search Targets. | Wang ηζζ S et al. | β | 2025 | β |
| Spatial but not temporal orienting of attention enhances the temporal acuity of human peripheral vision. | Foerster FR et al. | β | 2025 | β |
| Altered Cortical Activity during a Finger Tap in People with Stroke. | Balasubramanian P et al. | β | 2024 | β |
| Asymmetries in event-related potentials part 1: A systematic review of face processing studies. | Reinke P et al. | β | 2024 | β |
| Classical, spaced, or accelerated transcranial magnetic stimulation of motor cortex for treating neuropathic pain: A 3-arm parallel non-inferiority study. | Mussigmann T et al. | β | 2024 | β |
| Efficacy of combination scalp acupuncture for post-stroke cognitive impairment: a systematic review and meta-analysis. | Li S et al. | β | 2024 | β |
| Exploring the neural underpinnings of chord prediction uncertainty: an electroencephalography (EEG) study. | Ono K et al. | β | 2024 | β |
| Feedback negativity and feedback-related P3 in individuals at risk for depression: Comparing surface potentials and current source densities. | Gao Y et al. | β | 2024 | β |
| Get ready! High urgency reduces beta band cortico-muscular coherence during motor preparation. | Marinovic W et al. | β | 2024 | β |
| Listen to the beat: Behavioral and neurophysiological correlates of slow and fast heartbeat sounds. | Vicentin S et al. | β | 2024 | β |
| Novel methodology for detection and prediction of mild cognitive impairment using resting-state EEG. | Deng J et al. | β | 2024 | β |
| Rhythmically Modulating Neural Entrainment during Exposure to Regularities Influences Statistical Learning. | Batterink LJ et al. | β | 2024 | β |
| The effect of temporal predictability on sensory gating: Cortical responses inform perception. | Favero JD et al. | β | 2024 | β |
| Auditory Evoked P300 Potential in Patients With Parkinson's Disease. | Rajendran D et al. | β | 2023 | β |
| Evolving changes in cortical and subcortical excitability during movement preparation: A study of brain potentials and eye-blink reflexes during loud acoustic stimulation. | Nguyen AT et al. | β | 2023 | β |
| Healthy ageing and cognitive impairment alter EEG functional connectivity in distinct frequency bands. | Kumar WS et al. | β | 2023 | β |
| Impaired proactive cognitive control in Parkinson's disease. | Kricheldorff J et al. | β | 2023 | β |
| Laterality indices consensus initiative (LICI): A Delphi expert survey report on recommendations to record, assess, and report asymmetry in human behavioural and brain research. | Vingerhoets G et al. | β | 2023 | β |
| Maximizing the potential of EEG as a developmental neuroscience tool. | Buzzell GA et al. | β | 2023 | β |
| Mild Cognitive Impairment in African Americans Is Associated with Differences in EEG Theta/Beta Ratio. | Martin T et al. | β | 2023 | β |
| Motor Cortical Correlates of Paired Associative Stimulation Induced Plasticity: A TMS-EEG Study. | Costanzo M et al. | β | 2023 | β |
| N1-P2 event-related potentials and perceived intensity are associated: The effects of a weak pre-stimulus and attentional load on processing of a subsequent intense stimulus. | Favero JD et al. | β | 2023 | β |
| Resting-state frontal, frontlateral, and parietal alpha asymmetry:A pilot study examining relations with depressive disorder type and severity. | Marcu GM et al. | β | 2023 | β |
| Skill and experience impact neural activity during global and local biological motion processing. | DeCouto BS et al. | β | 2023 | β |
| Spatial proximity to others induces plastic changes in the neural representation of the peripersonal space. | Fossataro C et al. | β | 2023 | β |
| Tool use acquisition induces a multifunctional interference effect during object processing: evidence from the sensorimotor mu rhythm. | Foerster FR | β | 2023 | β |
| A comparison and classification of oscillatory characteristics in speech perception and covert speech. | Moon J et al. | β | 2022 | β |
| A systematic data-driven approach to analyze sensor-level EEG connectivity: Identifying robust phase-synchronized network components using surface Laplacian with spectral-spatial PCA. | Smith EE et al. | β | 2022 | β |
| A tutorial on the use of temporal principal component analysis in developmental ERP research - Opportunities and challenges. | Scharf F et al. | β | 2022 | β |
| Cortical network modularity changes along the course of frontotemporal and Alzheimer's dementing diseases. | Franciotti R et al. | β | 2022 | β |
| Don't Stop Me Now: Neural Underpinnings of Increased Impulsivity to Temporally Predictable Events. | Korolczuk I et al. | β | 2022 | β |
| EEG asymmetry and cognitive testing in MCI identification. | Martin T et al. | β | 2022 | β |
| Intelligence and Visual Mismatch Negativity: Is Pre-Attentive Visual Discrimination Related to General Cognitive Ability? | Hilger K et al. | β | 2022 | β |
| Large-scale frontoparietal theta, alpha, and beta phase synchronization: A set of EEG differential characteristics for freezing of gait in Parkinson's disease? | Karimi F et al. | β | 2022 | β |
| Multisensory integration and interactions across vision, hearing, and somatosensation in autism spectrum development and typical development. | Dwyer P et al. | β | 2022 | β |
| Neural correlates of familiar and unfamiliar action in infancy. | Chung H et al. | β | 2022 | β |
| P3b Amplitude and Latency in Tic Disorders: A Meta-Analysis. | Yang Y et al. | β | 2022 | β |
| Shifting Baselines: Longitudinal Reductions in EEG Beta Band Power Characterize Resting Brain Activity with Intensive Meditation. | Skwara AC et al. | β | 2022 | β |
| Still Wanting to Win: Reward System Stability in Healthy Aging. | Opitz L et al. | β | 2022 | β |
| The neural correlates of psychosocial stress: A systematic review and meta-analysis of spectral analysis EEG studies. | Vanhollebeke G et al. | β | 2022 | β |
| The novel frontal alpha asymmetry factor and its association with depression, anxiety, and personality traits. | Monni A et al. | β | 2022 | β |
| Β΅-rhythm phase from somatosensory but not motor cortex correlates with corticospinal excitability in EEG-triggered TMS. | Zrenner C et al. | β | 2022 | β |
| Cortical mechanisms underlying variability in intermittent theta-burst stimulation-induced plasticity: A TMS-EEG study. | Leodori G et al. | β | 2021 | β |
| Dissociating disorders of depression, anxiety, and their comorbidity with measures of emotional processing: A joint analysis of visual brain potentials and auditory perceptual asymmetries. | Panier LYX et al. | β | 2021 | β |
| EEG as a Functional Marker of Nicotine Activity: Evidence From a Pilot Study of Adults With Late-Life Depression. | Conley AC et al. | β | 2021 | β |
| Face-like configurations modulate electrophysiological mismatch responses. | Galigani M et al. | β | 2021 | β |
| Hyperconnectivity in Dementia Is Early and Focal and Wanes with Progression. | Bonanni L et al. | β | 2021 | β |
| Learning spatial filters from EEG signals with Graph Signal Processing methods. | Humbert P et al. | β | 2021 | β |
| Midfrontal theta oscillations and conflict monitoring in children and adults. | Chevalier N et al. | β | 2021 | β |
| Post-task modulation of resting state EEG differentiates MCI patients from controls. | Kavcic V et al. | β | 2021 | β |
| Resting-state EEG Dynamics Reveals Differences in Network Organization and its Fluctuation between Frequency Bands. | Zink N et al. | β | 2021 | β |
| Rhythmic Modulation of Visual Perception by Continuous Rhythmic Auditory Stimulation. | Bauer AR et al. | β | 2021 | β |
| SSVEP phase synchronies and propagation during repetitive visual stimulation at high frequencies. | Tsoneva T et al. | β | 2021 | β |
| SUPFUNSIM: Spatial Filtering Toolbox for EEG. | Rykaczewski K et al. | β | 2021 | β |
| The interplay between domain-general and domain-specific mechanisms during the time-course of verbal associative learning: An event-related potential study. | Ramos-Escobar N et al. | β | 2021 | β |
| Tool use and function knowledge shape visual object processing. | Foerster FR et al. | β | 2021 | β |
| Anodal transcranial direct current stimulation enhances the efficiency of functional brain network communication during auditory attentional control. | Zink N et al. | β | 2020 | β |
| Distilling the distinct contralateral and ipsilateral attentional responses to lateral stimuli and the bilateral response to midline stimuli for upper and lower visual hemifield locations. | Monnier A et al. | β | 2020 | β |
| Dopamine D1, but not D2, signaling protects mental representations from distracting bottom-up influences. | Bensmann W et al. | β | 2020 | β |
| Effects of the unilateral dynamic handgrip on resting cortical activity levels: A replication and extension. | Mirifar A et al. | β | 2020 | β |
| Frontal theta and posterior alpha in resting EEG: A critical examination of convergent and discriminant validity. | Smith EE et al. | β | 2020 | β |
| International Federation of Clinical Neurophysiology (IFCN) - EEG research workgroup: Recommendations on frequency and topographic analysis of resting state EEG rhythms. Part 1: Applications in clinical research studies. | Babiloni C et al. | β | 2020 | β |
| Mental rotation with abstract and embodied objects as stimuli: evidence from event-related potential (ERP). | Jansen P et al. | β | 2020 | β |
| Methodological Considerations on EEG Electrical Reference: A Functional Brain-Heart Interplay Study. | Candia-Rivera D et al. | β | 2020 | β |
| Reconsidering electrophysiological markers of response inhibition in light of trigger failures in the stop-signal task. | Skippen P et al. | β | 2020 | β |
| Too little, too late, and in the wrong place: Alpha band activity does not reflect an active mechanism of selective attention. | Antonov PA et al. | β | 2020 | β |
| Anodal tDCS modulates cortical activity and synchronization in Parkinson's disease depending on motor processing. | Schoellmann A et al. | β | 2019 | β |
| Anticipatory Distractor Suppression Elicited by Statistical Regularities in Visual Search. | Wang B et al. | β | 2019 | β |
| A Signature of Passivity? An Explorative Study of the N3 Event- Related Potential Component in Passive Oddball Tasks. | Kotchoubey B et al. | β | 2019 | β |
| Beta and Theta Oscillations Differentially Support Free Versus Forced Control over Multiple-Target Search. | van Driel J et al. | β | 2019 | β |
| Cognitive neurophysiology: Event-related potentials. | Helfrich RF et al. | β | 2019 | β |
| Does cognitive control ability mediate the relationship between reward-related mechanisms, impulsivity, and maladaptive outcomes in adolescence and young adulthood? | McKewen M et al. | β | 2019 | β |
| EEG Frontal Alpha Asymmetry and Dream Affect: Alpha Oscillations over the Right Frontal Cortex during REM Sleep and Presleep Wakefulness Predict Anger in REM Sleep Dreams. | Sikka P et al. | β | 2019 | β |
| Family Risk for Depression and Prioritization of Religion or Spirituality: Early Neurophysiological Modulations of Motivated Attention. | Kayser J et al. | β | 2019 | β |
| Get Set or Get Distracted? Disentangling Content-Priming and Attention-Catching Effects of Background Lure Stimuli on Identifying Targets in Two Simultaneously Presented Series. | Verleger R et al. | β | 2019 | β |
| Increases in theta CSD power and coherence during a calibrated stop-signal task: implications for goal-conflict processing and the Behavioural Inhibition System. | Lockhart TS et al. | β | 2019 | β |
| Mapping attention during gameplay: Assessment of behavioral and ERP markers in an auditory oddball task. | NΓΊΓ±ez Castellar EP et al. | β | 2019 | β |
| Mu rhythm desynchronization is specific to action execution and observation: Evidence from time-frequency and connectivity analysis. | Debnath R et al. | β | 2019 | β |
| Neural anticipatory mechanisms predict faster reaction times and higher fluid intelligence. | McKinney TL et al. | β | 2019 | β |
| Numbers in action during cognitive flexibility - A neurophysiological approach on numerical operations underlying task switching. | Petruo VA et al. | β | 2019 | β |
| Prestimulus cortical EEG oscillations can predict the excitability of the primary motor cortex. | Ogata K et al. | β | 2019 | β |
| Single-subject analysis of N400 event-related potential component with five different methods. | KallionpÀÀ RE et al. | β | 2019 | β |
| The Intensity of Early Attentional Processing, but Not Conflict Monitoring, Determines the Size of Subliminal Response Conflicts. | Bensmann W et al. | β | 2019 | β |
| The latency of a visual evoked potential tracks the onset of decision making. | Nunez MD et al. | β | 2019 | β |
| The neurophysiological basis of developmental changes during sequential cognitive flexibility between adolescents and adults. | Giller F et al. | β | 2019 | β |
| The P300 Event-Related Potential Component and Cognitive Impairment in Epilepsy: A Systematic Review and Meta-analysis. | Zhong R et al. | β | 2019 | β |
| BEAPP: The Batch Electroencephalography Automated Processing Platform. | Levin AR et al. | β | 2018 | β |
| Central theta amplitude as a negative correlate of performance proficiency in a dynamic visuospatial task. | Cross-Villasana F et al. | β | 2018 | β |
| Did You Listen to the Beat? Auditory Steady-State Responses in the Human Electroencephalogram at 4 and 7Β Hz Modulation Rates Reflect Selective Attention. | Jaeger M et al. | β | 2018 | β |
| Enhanced theta-gamma coupling associated with hippocampal volume increase following high-frequency left prefrontal repetitive transcranial magnetic stimulation in patients with major depression. | Noda Y et al. | β | 2018 | β |
| Event-Related Potential Responses to Task Switching Are Sensitive to Choice of Spatial Filter. | Wong ASW et al. | β | 2018 | β |
| Interhemispheric Transfer Time Asymmetry of Visual Information Depends on Eye Dominance: An Electrophysiological Study. | Chaumillon R et al. | β | 2018 | β |
| Intracranial source activity (eLORETA) related to scalp-level asymmetry scores and depression status. | Smith EE et al. | β | 2018 | β |
| Measuring Engagement in eHealth and mHealth Behavior Change Interventions: Viewpoint of Methodologies. | Short CE et al. | β | 2018 | β |
| On the Efficiency of Individualized Theta/Beta Ratio Neurofeedback Combined with Forehead EMG Training in ADHD Children. | Bazanova OM et al. | β | 2018 | β |
| On the Neurophysiological Mechanisms Underlying the Adaptability to Varying Cognitive Control Demands. | Zink N et al. | β | 2018 | β |
| Principles behind variance misallocation in temporal exploratory factor analysis for ERP data: Insights from an inter-factor covariance decomposition. | Scharf F et al. | β | 2018 | β |
| Resting-state brain oscillations predict trait-like cognitive styles. | Erickson B et al. | β | 2018 | β |
| Subcortical sources dominate the neuroelectric auditory frequency-following response to speech. | Bidelman GM | β | 2018 | β |
| Temporal stability of posterior EEG alpha over twelve years. | Tenke CE et al. | β | 2018 | β |
| The Way We Do the Things We Do: How Cognitive Contexts Shape the Neural Dynamics of Motor Areas in Humans. | Vidal F et al. | β | 2018 | β |
| Association of posterior EEG alpha with prioritization of religion or spirituality: A replication and extension at 20-year follow-up. | Tenke CE et al. | β | 2017 | β |
| Auditory Target and Novelty Processing in Patients with Unilateral Hippocampal Sclerosis: A Current-Source Density Study. | VilΓ -BallΓ³ A et al. | β | 2017 | β |
| Bidirectional Frontoparietal Oscillatory Systems Support Working Memory. | Johnson EL et al. | β | 2017 | β |
| Brain electrical activity signatures during performance of the Multisource Interference Task. | GonzΓ‘lez-Villar AJ et al. | β | 2017 | β |
| Comparison of linear spatial filters for identifying oscillatory activity in multichannel data. | Cohen MX | β | 2017 | β |
| Computation of Surface Laplacian for tri-polar ring electrodes on high-density realistic geometry head model. | Junwei Ma et al. | β | 2017 | β |
| Current Source Density Estimation Enhances the Performance of Motor-Imagery-Related Brain-Computer Interface. | Rathee D et al. | β | 2017 | β |
| Demonstrating test-retest reliability of electrophysiological measures for healthy adults in a multisite study of biomarkers of antidepressant treatment response. | Tenke CE et al. | β | 2017 | β |
| Genetic influences on functional connectivity associated with feedback processing and prediction error: Phase coupling of theta-band oscillations in twins. | Demiral ΕB et al. | β | 2017 | β |
| Hierarchy measurement for modeling network dynamics under directed attacks. | Rubinson M et al. | β | 2017 | β |
| Lateralization of language function in epilepsy patients: A high-density scalp-derived event-related potentials (ERP) study. | Trimmel K et al. | β | 2017 | β |
| Local and interregional alpha EEG dynamics dissociate between memory for search and memory for recognition. | van Driel J et al. | β | 2017 | β |
| Motivated attention and family risk for depression: Neuronal generator patterns at scalp elicited by lateralized aversive pictures reveal blunted emotional responsivity. | Kayser J et al. | β | 2017 | β |
| P300 amplitude and latency in autism spectrum disorder: a meta-analysis. | Cui T et al. | β | 2017 | β |
| Synchronization of fronto-parietal beta and theta networks as a signature of visual awareness in neglect. | Yordanova J et al. | β | 2017 | β |
| Theta- and delta-band EEG network dynamics during a novelty oddball task. | Harper J et al. | β | 2017 | β |
| Training of support afferentation in postmenopausal women. | Bazanova OM et al. | β | 2017 | β |
| Comparison of linear spatial filters for identifying oscillatory activity in multichannel data | Cohen MX | β | 2016 | β |
| Impact of the reference choice on scalp EEG connectivity estimation. | Chella F et al. | β | 2016 | β |
| Linking Theoretical Decision-making Mechanisms in the Simon Task with Electrophysiological Data: A Model-based Neuroscience Study in Humans. | Servant M et al. | β | 2016 | β |
| Methodological Considerations about the Use of Bimodal Oddball P300 in Psychiatry: Topography and Reference Effect. | SchrΓΆder E et al. | β | 2016 | β |
| Neuronal generator patterns at scalp elicited by lateralized aversive pictures reveal consecutive stages of motivated attention. | Kayser J et al. | β | 2016 | β |
| Preventing (impulsive) errors: Electrophysiological evidence for online inhibitory control over incorrect responses. | Burle B et al. | β | 2016 | β |
| The effect of vagus nerve stimulation on response inhibition. | Schevernels H et al. | β | 2016 | β |
| The relationship between baseline EEG spectra power and memory performance in older African Americans endorsing cognitive concerns in a community setting. | Kavcic V et al. | β | 2016 | β |
| Visual processing during natural reading. | Weiss B et al. | β | 2016 | β |
| Electrophysiological evidence for the involvement of proactive and reactive control in a rewarded stop-signal task. | Schevernels H et al. | β | 2015 | β |
| Hemifield-dependent N1 and event-related theta/delta oscillations: An unbiased comparison of surface Laplacian and common EEG reference choices. | Kayser J et al. | β | 2015 | β |
| On the benefits of using surface Laplacian (current source density) methodology in electrophysiology. | Kayser J et al. | β | 2015 | β |
| Posterior EEG alpha at rest and during task performance: Comparison of current source density and field potential measures. | Tenke CE et al. | β | 2015 | β |
| Surface Laplacians (SL) and phase properties of EEG rhythms: Simulated generators in a volume-conduction model. | Tenke CE et al. | β | 2015 | β |
| Unilateral Left-Hand Contractions Produce Widespread Depression of Cortical Activity after Their Execution. | Cross-Villasana F et al. | β | 2015 | β |