Technical contributionAn on-line transformation of EEG scalp potentials into orthogonal source derivationsTransformation “on-line” de potentials EEG de scalp, en derivation orthogonale des sources
Abstract
A new type of EEG derivation has been investigated. This derivation, constituting a practical implementation of the Laplace operator, detects source activity as it appears at the surface level of the scalp. It is realized in the 10–20 system of electrode placement basically as an analogue superposition of four bipolar derivations, forming a star-like configuration around each electrode. Visual estimation of the topographical origins of a pattern, is thus replaced by a more efficient on-line process, which derives the source activity at the position of each individual electrode.
Practical correlation tests have shown that the separation of adjacent derivations is improved by a factor of between two and four, compared to the corresponding bipolar and common reference derivations. Any feature of local origin will therefore have a correspondingly increased signal-to-noise ratio prior to the stage of visual or automatic interpretation. As a consequence of the partition of the scalp field into 19 source areas, instead of utilizing an arbitrary number of potential differences, one fixed montage with 19 recorder channels is sufficient to present the total surface activity, within the limits of resolution of the electrode system.
Résumé
Un nouveau type de dérivation EEG est étudié. Cette dérivation qui est basée sur l'utilisation pratique de l'opérateur Laplacien, détecte l'activité de la source telle qu'elle apparaît au niveau de la surface du scalp. Elle a été réalisée dans le système 10–20, essentiellement par superposition analogique de quatre dérivations bipolaires, formant une configuration en étoile autour de chaque électrode. Grâce à cette nouvelle dérivation l'estimation visuelle de l'origine topographique d'un pattern, est remplacée par un procédé “on-line” plus efficace, qui dérive l'activité de la source au niveau de chaque électrode individuelle.
Des essais pratiques de corrélation ont montré que la séparation des dérivations adjacentes est améliorée par un facteur de deux à quatre, par rapport aux dérivations bipolaires ou avec référence commune correspondante. N'importe quel pattern d'origine locale aura donc un rapport signal-bruit augmenté avant même son interprétation visuelle ou automatique. Partant de la division du scalp en 19 surfaces de réception, un tel montage fixë à 19 canaux enregistreurs est suffisant pour connaître l'activité totale sur la surface (à l'intérieur limites de résolution du système d'électrode), au lieu de se limiter à un nombre arbitraire de différences de potentiels.
References (3)
- H.H. Jasper
The 10–20 electrode system of the International Federation
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A procedure to minimize EEG variability for BCI applications
2024, Biomedical Signal Processing and ControlA Brain-Computer Interface (BCI) decodes brain activities to translate them into computer commands. Electroencephalography is the most widely adopted technique for brain signal recording in BCIs, because of practical and safety reasons. However, EEG signals show a significant intra-subject variability, which constitutes a major challenge for BCI development. The main goal of this work is to improve a pseudo-online movement detection system using motor imagery EEG signals that simulate the BCI input. We propose a strategy that aims at minimizing the effects of the poor spatial resolution and the active reference electrode based on finding the best combinations of electrode pairs. The proposed method finds subject-specific pairs of electrodes along with signal transformations that provide the more stable results. The average accuracy across 15 subjects was 95 %. It was also seen that energy signals in the delta band (0–4 Hz) of the electrode line CCP (according to the 10–20 system) are associated to the lowest variability. The hypothesis of lower variability being associated to movement related information and therefore to higher accuracy in classification was confirmed by the results. The main conclusion is that it is possible to overcome in some level the signal variability without introducing mathematical or physical uncertainties inherent to commonly adopted approaches such as spatial filters or volume conduction modeling, for instance. The contribution of this work is the procedure to minimize EEG variability for BCI applications. The significance is the possibility to apply the procedure to any set of channels and transformations.
Targeting motor cortex high-excitability states defined by functional connectivity with real-time EEG–TMS
2023, NeuroImageWe tested previous post-hoc findings indicating a relationship between functional connectivity (FC) in the motor network and corticospinal excitability (CsE), in a real-time EEG-TMS experiment in healthy participants.
We hypothesized that high FC between left and right motor cortex predicts high CsE.
FC was quantified in real-time by single-trial phase-locking value (stPLV), and TMS single pulses were delivered based on the current FC. CsE was indexed by motor-evoked potential (MEP) amplitude in a hand muscle. Possible confounding factors (pre-stimulus -power and phase, interstimulus interval) were evaluated post hoc.
MEPs were significantly larger during high FC compared to low FC. Post hoc analysis revealed that the FC condition showed a significant interaction with -power in the stimulated hemisphere. Further, inter-stimulus interval (ISI) interacted with high vs. low FC conditions. In summary, FC was confirmed to be predictive of CsE, but should not be considered in isolation from -power and ISI. Moreover, FC was complementary to -phase in predicting CsE. Motor network FC is another marker of real-time accessible CsE beyond previously established markers, in particular phase and power of the rhythm, and may help define a more robust composite biomarker of high/low excitability states of human motor cortex.
Structural basis of envelope and phase intrinsic coupling modes in the cerebral cortex
2023, NeuroImageIntrinsic coupling modes (ICMs) can be observed in ongoing brain activity at multiple spatial and temporal scales. Two families of ICMs can be distinguished: phase and envelope ICMs. The principles that shape these ICMs remain partly elusive, in particular their relation to the underlying brain structure. Here we explored structure-function relationships in the ferret brain between ICMs quantified from ongoing brain activity recorded with chronically implanted micro-ECoG arrays and structural connectivity (SC) obtained from high-resolution diffusion MRI tractography. Large-scale computational models were used to explore the ability to predict both types of ICMs. Importantly, all investigations were conducted with ICM measures that are sensitive or insensitive to volume conduction effects. The results show that both types of ICMs are significantly related to SC, except for phase ICMs when using measures removing zero-lag coupling. The correlation between SC and ICMs increases with increasing frequency which is accompanied by reduced delays. Computational models produced results that were highly dependent on the specific parameter settings. The most consistent predictions were derived from measures solely based on SC. Overall, the results demonstrate that patterns of cortical functional coupling as reflected in both phase and envelope ICMs are both related, albeit to different degrees, to the underlying structural connectivity in the cerebral cortex.
Corticospinal excitability is highest at the early rising phase of sensorimotor µ-rhythm
2023, NeuroImageAlpha oscillations are thought to reflect alternating cortical states of excitation and inhibition. Studies of perceptual thresholds and evoked potentials have shown the scalp EEG negative phase of the oscillation to correspond to a short-lasting low-threshold and high-excitability state of underlying visual, somatosensory, and primary motor cortex. The negative peak of the oscillation is assumed to correspond to the state of highest excitability based on biophysical considerations and considerable effort has been made to improve the extraction of a predictive signal by individually optimizing EEG montages. Here, we investigate whether it is the negative peak of sensorimotor µ-rhythm that corresponds to the highest corticospinal excitability, and whether this is consistent between individuals.
In 52 adult participants, a standard 5-channel surface Laplacian EEG montage was used to extract sensorimotor µ-rhythm during transcranial magnetic stimulation (TMS) of primary motor cortex. Post-hoc trials were sorted from 800 TMS-evoked motor potentials (MEPs) according to the pre-stimulus EEG (estimated instantaneous phase) and MEP amplitude (as an index of corticospinal excitability). Different preprocessing transformations designed to improve the accuracy by which µ-alpha phase predicts excitability were also tested.
By fitting a sinusoid to the MEP amplitudes, sorted according to pre-stimulus EEG-phase, we found that excitability was highest during the early rising phase, at a significant delay with respect to the negative peak by on average 45° or 10 ms. The individual phase of highest excitability was consistent across study participants and unaffected by two different EEG-cleaning methods that utilize 64 channels to improve signal quality by compensating for individual noise level and channel covariance. Personalized transformations of the montage did not yield better prediction of excitability from µ-alpha phase.
The relationship between instantaneous phase of a brain oscillation and fluctuating cortical excitability appears to be more complex than previously hypothesized. In TMS of motor cortex, a standard surface Laplacian 5-channel EEG montage is effective in extracting a predictive signal and the phase corresponding to the highest excitability appears to be consistent between individuals. This is an encouraging result with respect to the clinical potential of therapeutic personalized brain interventions in the motor system. However, it remains to be investigated, whether similar results can be obtained for other brain areas and brain oscillations targeted with EEG and TMS.
Phase matters when there is power: Phasic modulation of corticospinal excitability occurs at high amplitude sensorimotor mu-oscillations
2022, Neuroimage: ReportsPrior studies have suggested that oscillatory activity in cortical networks can modulate stimulus-evoked responses through time-varying fluctuations in neural excitation-inhibition dynamics. Studies combining transcranial magnetic stimulation (TMS) with electromyography (EMG) and electroencephalography (EEG) can provide direct measurements to examine how instantaneous fluctuations in cortical oscillations contribute to variability in TMS-induced corticospinal responses. However, the results of these studies have been conflicting, as some reports showed consistent phase effects of sensorimotor mu-rhythms with increased excitability at the negative mu peaks, while others failed to replicate these findings or reported unspecific mu-phase effects across subjects. Given the lack of consistent results, we systematically examined the modulatory effects of instantaneous and pre-stimulus sensorimotor mu-rhythms on corticospinal responses with offline EEG-based motor evoked potential (MEP) classification analyses across five identical visits. Instantaneous sensorimotor mu-phase or pre-stimulus mu-power alone did not significantly modulate MEP responses. Instantaneous mu-power analyses showed weak effects with larger MEPs during high-power trials at the overall group level analyses, but this trend was not reproducible across visits. However, TMS delivered at the negative peak of high magnitude mu-oscillations generated the largest MEPs across all visits, with significant differences compared to other peak-phase combinations. High power effects on MEPs were only observed at the trough phase of ongoing mu oscillations originating from the stimulated region, indicating site and phase specificity, respectively. More importantly, such phase-dependent power effects on corticospinal excitability were reproducible across multiple visits. We provide further evidence that fluctuations in corticospinal excitability indexed by MEP amplitudes are partially driven by dynamic interactions between the magnitude and the phase of ongoing sensorimotor mu oscillations at the time of TMS, and suggest promising insights for (re)designing neuromodulatory TMS protocols targeted to specific cortical oscillatory states.
µ-rhythm phase from somatosensory but not motor cortex correlates with corticospinal excitability in EEG-triggered TMS
2022, Journal of Neuroscience MethodsSensorimotor µ-rhythm phase is correlated with corticospinal excitability. Transcranial magnetic stimulation (TMS) of motor cortex results in larger motor evoked potentials (MEPs) during the negative peak of the EEG oscillation as extracted with a surface Laplacian. However, the anatomical source of the relevant oscillation is not clear and demonstration of the relationship is sensitive to the choice of EEG montage.
Here, we compared two EEG montages preferentially sensitive to oscillations originating from the crown of precentral gyrus (dorsal premotor cortex) vs. postcentral gyrus (secondary somatosensory cortex). We hypothesized that the EEG signal from precentral gyrus would correlate more strongly with MEP amplitude, given that the corticospinal neurons are located in the anterior wall of the sulcus and the corticospinal tract has input from premotor cortex.
Real-time EEG-triggered TMS of motor cortex was applied in 6 different conditions in randomly interleaved order, 3 phase conditions (positive peak, negative peak, random phase of the ongoing µ-oscillation), and each phase condition for 2 different EEG montages corresponding to oscillations preferentially originating in precentral gyrus (premotor cortex) vs. postcentral gyrus (somatosensory cortex), extracted using FCC3h vs. C3 centered EEG montages.
The negative vs. positive peak of sensorimotor µ-rhythm as extracted from the C3 montage (postcentral gyrus, somatosensory cortex) correlated with states of high vs. low corticospinal excitability (p < 0.001), replicating previous findings. However, no significant correlation was found for sensorimotor µ-rhythm as extracted from the neighboring FCC3 montage (precentral gyrus, premotor cortex). This implies that EEG-signals from the somatosensory cortex are better predictors of corticospinal excitability than EEG-signals from the motor areas.
The extraction of a brain oscillation whose phase corresponds to corticospinal excitability is highly sensitive to the selected EEG montage and the location of the EEG sensors on the scalp. Here, the cortical source of EEG oscillations predicting response amplitude does not correspond to the cortical target of the stimulation, indicating that even in this simple case, a specific neuronal pathway from somatosensory cortex to primary motor cortex is involved.