Review of individualized current flow modeling studies for transcranial electrical stimulation.

There is substantial intersubject variability of behavioral and neurophysiological responses to transcranial electrical stimulation (tES), which represents one of the most important limitations of tES. Many tES protocols utilize a fixed experimental parameter set disregarding individual anatomical and physiological properties. This one-size-fits-all approach might be one reason for the observed interindividual response variability. Simulation of current flow applying head models based on available anatomical data can help to individualize stimulation parameters and contribute to the understanding of the causes of this response variability. Current flow modeling can be used to retrospectively investigate the characteristics of tES effectivity. Previous studies examined, for example, the impact of skull defects and lesions on the modulation of current flow and demonstrated effective stimulation intensities in different age groups. Furthermore, uncertainty analysis of electrical conductivities in current flow modeling indicated the most influential tissue compartments. Current flow modeling, when used in prospective study planning, can potentially guide stimulation configurations resulting in individually effective tES. Specifically, current flow modeling using individual or matched head models can be employed by clinicians and scientists to, for example, plan dosage in tES protocols for individuals or groups of participants. We review studies that show a relationship between the presence of behavioral/neurophysiological responses and features derived from individualized current flow models. We highlight the potential benefits of individualized current flow modeling.

Biomechanical modeling for the estimation of muscle forces: toward a common language in biomechanics, medical engineering, and neurosciences.

Different research fields, such as biomechanics, medical engineering or neurosciences take part in the development of biomechanical models allowing for the estimation of individual muscle forces involved in motor action. The heterogeneity of the terminology used to describe these models according to the research field is a source of confusion and can hamper collaboration between the different fields. This paper proposes a common language based on lexical disambiguation and a synthesis of the terms used in the literature in order to facilitate the understanding of the different elements of biomechanical modeling for force estimation, without questioning the relevance of the terms used in each field or the different model components or their interest. We suggest that the description should start with an indication of whether the muscle force estimation problem is solved following the physiological movement control (from the nervous drive to the muscle force production) or in the opposite direction. Next, the suitability of the model for force production estimation at a given time or for monitoring over time should be specified. Authors should pay particular attention to the method description used to find solutions, specifying whether this is done during or after data collection, with possible method adaptations during processing. Finally, the presence of additional data must be specified by indicating whether they are used to drive, assist, or calibrate the model. Describing and classifying models in this way will facilitate the use and application in all fields where the estimation of muscle forces is of real, direct, and concrete interest.

One Definition to Join Them All: The N-Spherical Solution for the EEG Lead Field.

Albeit its simplicity, the concentric spheres head model is widely used in EEG. The reason behind this is its simple mathematical definition, which allows for the calculation of lead fields with negligible computational cost, for example, for iterative approaches. Nevertheless, the literature shows contradictory formulations for the electrical solution of this head model. In this work, we study several different definitions for the electrical lead field of a four concentric spheres conduction model, finding that their results are contradictory. A thorough exploration of the mathematics used to build these formulations, provided in the original works, allowed for the identification of errors in some of the formulae, which proved to be the reason for the discrepancies. Moreover, this mathematical review revealed the iterative nature of some of these formulations, which allowed us to develop a formulation to solve the lead field in a head model built from an arbitrary number of concentric, homogeneous, and isotropic spheres.

Influence of Differences in the Density of Seawater on the Measurement of the Underwater Gravity Gradient.

In preparing gravity gradient reference maps for navigation purposes, researchers have tended to use a constant value for the density of seawater. However, the actual seawater density at a particular location may vary due to the effects of longitude, latitude and bathymetry. In this study, the right rectangular prism method was used to calculate the disturbing gravity gradient caused by the mass deficiency of seawater for three different seawater profiles in an area east of Taiwan. For this purpose, two seawater density models were used as alternatives to the constant seawater density model, and the alteration in the gravity gradient was calculated to quantify the error in the gravity gradient as a result of using a constant seawater density. The results demonstrated that the error in the gravity gradient can reach 1E for water at large depths. Moreover, the difference between the amplitude of the error of the corrected thermocline and that for the uncorrected seawater density model was found to be quite small. If a gravity gradient reference map with accuracy better than 1E is to be realized, the seawater density cannot be taken as constant during forward modeling.

Influence of Sea Level Anomaly on Underwater Gravity Gradient Measurements.

Considering the theoretical research needs of gravity gradient detection and navigation, this study uses the right rectangular prism method to calculate the disturbing gravity gradient from sea level anomalies in the range of 5° × 5° in the Kuroshio extension area of the western Pacific with large sea level anomalies. The disturbing gravity gradient is obtained in different directions within a depth of 50 m below the mean sea level based on the principle of the disturbing gravity gradient. The calculation results show that the sea level anomalies at local positions significantly impact the underwater gravity gradient measurements, with the maximum contribution exceeding 10 E and the maximum difference between different locations exceeding 20 E. The change of the sea level anomaly over time also significantly impacts the measurement of the underwater gravity gradient, with the maximum change value exceeding 20 E. The impact will have a corresponding change with the seasonal change of the sea level anomaly. Therefore, the underwater carrier needs to consider the disturbing gravity gradient caused by sea level anomalies when using the gravity gradient for underwater detection and navigation.

Evaluation of Void Defects behind Tunnel Lining through GPR forward Simulation.

Voids, a common defect in tunnel construction, lead to the deterioration of the lining structure and reduce the safety of tunnels. In this study, ground-penetrating radar (GPR) was used in tunnel lining void detection. Based on the finite difference time domain (FDTD) method, a forward model was established to simulate the process of tunnel lining void detection. The area of the forward image and the actual void area was analyzed based on the binarization method. Both the plain concrete and reinforced concrete lining with various sizes of air-filled and water-filled voids were considered. The rationality of the model was verified by measured data. It was observed that the response mode of voids can be hyperbolic, bowl-shaped, and strip-shaped, and this depends on the void's width. Compared with the air-filled voids, water filling increases the response range of the voids and produces a virtual image. Although the diffracted wave caused by a steel bar will bring about significant interference to the void response, the center position of the voids can be accurately located using 3D GPR.

Stimulus-aware spatial filtering for single-trial neural response and temporal response function estimation in high-density EEG with applications in auditory research.

A common problem in neural recordings is the low signal-to-noise ratio (SNR), particularly when using non-invasive techniques like magneto- or electroencephalography (M/EEG). To address this problem, experimental designs often include repeated trials, which are then averaged to improve the SNR or to infer statistics that can be used in the design of a denoising spatial filter. However, collecting enough repeated trials is often impractical and even impossible in some paradigms, while analyses on existing data sets may be hampered when these do not contain such repeated trials. Therefore, we present a data-driven method that takes advantage of the knowledge of the presented stimulus, to achieve a joint noise reduction and dimensionality reduction without the need for repeated trials. The method first estimates the stimulus-driven neural response using the given stimulus, which is then used to find a set of spatial filters that maximize the SNR based on a generalized eigenvalue decomposition. As the method is fully data-driven, the dimensionality reduction enables researchers to perform their analyses without having to rely on their knowledge of brain regions of interest, which increases accuracy and reduces the human factor in the results. In the context of neural tracking of a speech stimulus using EEG, our method resulted in more accurate short-term temporal response function (TRF) estimates, higher correlations between predicted and actual neural responses, and higher attention decoding accuracies compared to existing TRF-based decoding methods. We also provide an extensive discussion on the central role played by the generalized eigenvalue decomposition in various denoising methods in the literature, and address the conceptual similarities and differences with our proposed method.

Detectability of cerebellar activity with magnetoencephalography and electroencephalography.

Electrophysiological signals from the cerebellum have traditionally been viewed as inaccessible to magnetoencephalography (MEG) and electroencephalography (EEG). Here, we challenge this position by investigating the ability of MEG and EEG to detect cerebellar activity using a model that employs a high-resolution tessellation of the cerebellar cortex. The tessellation was constructed from repetitive high-field (9.4T) structural magnetic resonance imaging (MRI) of an ex vivo human cerebellum. A boundary-element forward model was then used to simulate the M/EEG signals resulting from neural activity in the cerebellar cortex. Despite significant signal cancelation due to the highly convoluted cerebellar cortex, we found that the cerebellar signal was on average only 30-60% weaker than the cortical signal. We also made detailed M/EEG sensitivity maps and found that MEG and EEG have highly complementary sensitivity distributions over the cerebellar cortex. Based on previous fMRI studies combined with our M/EEG sensitivity maps, we discuss experimental paradigms that are likely to offer high M/EEG sensitivity to cerebellar activity. Taken together, these results show that cerebellar activity should be clearly detectable by current M/EEG systems with an appropriate experimental setup.

The Sensitivity of Ear-EEG: Evaluating the Source-Sensor Relationship Using Forward Modeling.

Ear-EEG allows to record brain activity in every-day life, for example to study natural behaviour or unhindered social interactions. Compared to conventional scalp-EEG, ear-EEG uses fewer electrodes and covers only a small part of the head. Consequently, ear-EEG will be less sensitive to some cortical sources. Here, we perform realistic electromagnetic simulations to compare cEEGrid ear-EEG with 128-channel cap-EEG. We compute the sensitivity of ear-EEG for different cortical sources, and quantify the expected signal loss of ear-EEG relative to cap-EEG. Our results show that ear-EEG is most sensitive to sources in the temporal cortex. Furthermore, we show how ear-EEG benefits from a multi-channel configuration (i.e. cEEGrid). The pipelines presented here can be adapted to any arrangement of electrodes and can therefore provide an estimate of sensitivity to cortical regions, thereby increasing the chance of successful experiments using ear-EEG.

A Mechanistic Framework for Explaining Audience Design in Language Production.

Audience design refers to the situation in which speakers fashion their utterances so as to cater to the needs of their addressees. In this article, a range of audience design effects are reviewed, organized by a novel cognitive framework for understanding audience design effects. Within this framework, feedforward (or one-shot) production is responsible for feedforward audience design effects, or effects based on already known properties of the addressee (e.g., child versus adult status) or the message (e.g., that it includes meanings that might be confusable). Then, a forward modeling approach is described, whereby speakers independently generate communicatively relevant features to predict potential communicative effects. This can explain recurrent processing audience design effects, or effects based on features of the produced utterance itself or on idiosyncratic features of the addressee or communicative situation. Predictions from the framework are delineated.

EECoG-Comp: An Open Source Platform for Concurrent EEG/ECoG Comparisons-Applications to Connectivity Studies.

Electrophysiological Source Imaging (ESI) is hampered by lack of "gold standards" for model validation. Concurrent electroencephalography (EEG) and electrocorticography (ECoG) experiments (EECoG) are useful for this purpose, especially primate models due to their flexibility and translational value for human research. Unfortunately, there is only one EECoG experiments in the public domain that we know of: the Multidimensional Recording (MDR) is based on a single monkey ( www.neurotycho.org ). The mining of this type of data is hindered by lack of specialized procedures to deal with: (1) Severe EECoG artifacts due to the experimental produces; (2) Sophisticated forward models that account for surgery induced skull defects and implanted ECoG electrode strips; (3) Reliable statistical procedures to estimate and compare source connectivity (partial correlation). We provide solutions to the processing issues just mentioned with EECoG-Comp: an open source platform ( https://github.com/Vincent-wq/EECoG-Comp ). EECoG lead fields calculated with FEM (Simbio) for MDR data are also provided and were used in other papers of this special issue. As a use case with the MDR, we show: (1) For real MDR data, 4 popular ESI methods (MNE, LCMV, eLORETA and SSBL) showed significant but moderate concordance with a usual standard, the ECoG Laplacian (standard partial [Formula: see text]); (2) In both monkey and human simulations, all ESI methods as well as Laplacian had a significant but poor correspondence with the true source connectivity. These preliminary results may stimulate the development of improved ESI connectivity estimators but require the availability of more EECoG data sets to obtain neurobiologically valid inferences.

Gradient-Based Automatic Lookup Table Generator for Radiative Transfer Models.

Physically based radiative transfer models (RTMs) are widely used in Earth observation to understand the radiation processes occurring on the Earth's surface and their interactions with water, vegetation, and atmosphere. Through continuous improvements, RTMs have increased in accuracy and representativity of complex scenes at expenses of an increase in complexity and computation time, making them impractical in various remote sensing applications. To overcome this limitation, the common practice is to precompute large lookup tables (LUTs) for their later interpolation. To further reduce the RTM computation burden and the error in LUT interpolation, we have developed a method to automatically select the minimum and optimal set of input-output points (nodes) to be included in an LUT. We present the gradient-based automatic LUT generator algorithm (GALGA), which relies on the notion of an acquisition function that incorporates: 1) the Jacobian evaluation of an RTM and 2) the information about the multivariate distribution of the current nodes. We illustrate the capabilities of GALGA in the automatic construction and optimization of MODTRAN-based LUTs of different dimensions of the input variables space. Our results indicate that when compared with a pseudorandom homogeneous distribution of the LUT nodes, GALGA reduces:1) the LUT size by >24%; 2) the computation time by 27%; and 3) the maximum interpolation relative errors by at least 10%. It is concluded that an automatic LUT design might benefit from the methodology proposed in GALGA to reduce interpolation errors and computation time in computationally expensive RTMs.

A Semi-Automatic Coupling Geophone for Tunnel Seismic Detection.

The tunnel seismic method allows for the detection of the geology in front of a tunnel face for the safety of tunnel construction. Conventional geophones have problems such as a narrow spectral width, low sensitivity, and poor coupling with the tunnel wall. To tackle issues above, we propose a semi-automatic coupling geophone equipped with a piezoelectric sensor with a spectral range of 10-5000 Hz and a sensitivity of 2.8 V/g. After the geophone was manually pushed into the borehole, it automatically coupled with the tunnel wall under the pressure of the springs within the device. A comparative experiment showed that the data spectrum acquired by the semi-automatic coupling geophone was much higher than that of the conventional geophone equipped with the same piezoelectric sensor. The seismic data were processed in combination with forward modeling. The imaging results also show that the data acquired by the semi-automatic coupling geophone were more in line with the actual geological conditions. In addition, the semi-automatic coupling geophone's installation requires a lower amount of time and cost. In summary, the semi-automatic coupling geophone is able to efficiently acquire seismic data with high fidelity, which can provide a reference for tunnel construction safety.

Sensitivity Analysis of Leakage Correction of GRACE Data in Southwest China Using A-Priori Model Simulations: Inter-Comparison of Spherical Harmonics, Mass Concentration and In Situ Observations.

The Gravity Recovery and Climate Experiment (GRACE) level-2 spherical harmonic (SH) solutions are noisy and thus require filtering. Filtering reduces noise but affects signal quality via signal leakage. Generally, a leakage correction is required for GRACE applications to remove leakage signal and recover the true signal. Forward modelling based on some a priori information is a widely used approach for leakage correction of GRACE data. The a priori information generally relies on global hydrological model simulations. There are many global hydrological models and therefore it is of interest to explore how different global hydrology model simulations influence leakage correction results. This study investigated the sensitivity of three leakage correction methods (additive method, scaling factor method and multiplicative method) to five global hydrology model simulations (four models from the Global Land Data Assimilation System (GLDAS) and the WaterGAP Global Hydrology Model (WGHM)). The sensitivity analysis was performed with observational data in Southwest China and one sub-region, Guangxi. Results show that although large differences were identified among the five global model simulations, the additive and scaling factor methods are less affected by the choice of a priori model in comparison to the multiplicative approach. For the additive and scaling factor methods, WGHM outperforms the other four GLDAS models in leakage correction of GRACE data. GRACE data corrected with the multiplicative method shows the highest amount of error, indicating this method is not applicable for leakage correction in the study area. This study also assessed the level-3 mascon (mass concentration) solutions of GRACE data. The mascon-based results are nearly as good as the leakage corrected results based on SH solutions.

Automatic skull segmentation from MR images for realistic volume conductor models of the head: Assessment of the state-of-the-art.

Anatomically realistic volume conductor models of the human head are important for accurate forward modeling of the electric field during transcranial brain stimulation (TBS), electro- (EEG) and magnetoencephalography (MEG). In particular, the skull compartment exerts a strong influence on the field distribution due to its low conductivity, suggesting the need to represent its geometry accurately. However, automatic skull reconstruction from structural magnetic resonance (MR) images is difficult, as compact bone has a very low signal in magnetic resonance imaging (MRI). Here, we evaluate three methods for skull segmentation, namely FSL BET2, the unified segmentation routine of SPM12 with extended spatial tissue priors, and the skullfinder tool of BrainSuite. To our knowledge, this study is the first to rigorously assess the accuracy of these state-of-the-art tools by comparison with CT-based skull segmentations on a group of ten subjects. We demonstrate several key factors that improve the segmentation quality, including the use of multi-contrast MRI data, the optimization of the MR sequences and the adaptation of the parameters of the segmentation methods. We conclude that FSL and SPM12 achieve better skull segmentations than BrainSuite. The former methods obtain reasonable results for the upper part of the skull when a combination of T1- and T2-weighted images is used as input. The SPM12-based results can be improved slightly further by means of simple morphological operations to fix local defects. In contrast to FSL BET2, the SPM12-based segmentation with extended spatial tissue priors and the BrainSuite-based segmentation provide coarse reconstructions of the vertebrae, enabling the construction of volume conductor models that include the neck. We exemplarily demonstrate that the extended models enable a more accurate estimation of the electric field distribution during transcranial direct current stimulation (tDCS) for montages that involve extraencephalic electrodes. The methods provided by FSL and SPM12 are integrated into pipelines for the automatic generation of realistic head models based on tetrahedral meshes, which are distributed as part of the open-source software package SimNIBS for field calculations for transcranial brain stimulation.

The FieldTrip-SimBio pipeline for EEG forward solutions.

BACKGROUND: Accurately solving the electroencephalography (EEG) forward problem is crucial for precise EEG source analysis. Previous studies have shown that the use of multicompartment head models in combination with the finite element method (FEM) can yield high accuracies both numerically and with regard to the geometrical approximation of the human head. However, the workload for the generation of multicompartment head models has often been too high and the use of publicly available FEM implementations too complicated for a wider application of FEM in research studies. In this paper, we present a MATLAB-based pipeline that aims to resolve this lack of easy-to-use integrated software solutions. The presented pipeline allows for the easy application of five-compartment head models with the FEM within the FieldTrip toolbox for EEG source analysis. METHODS: The FEM from the SimBio toolbox, more specifically the St. Venant approach, was integrated into the FieldTrip toolbox. We give a short sketch of the implementation and its application, and we perform a source localization of somatosensory evoked potentials (SEPs) using this pipeline. We then evaluate the accuracy that can be achieved using the automatically generated five-compartment hexahedral head model [skin, skull, cerebrospinal fluid (CSF), gray matter, white matter] in comparison to a highly accurate tetrahedral head model that was generated on the basis of a semiautomatic segmentation with very careful and time-consuming manual corrections. RESULTS: The source analysis of the SEP data correctly localizes the P20 component and achieves a high goodness of fit. The subsequent comparison to the highly detailed tetrahedral head model shows that the automatically generated five-compartment head model performs about as well as a highly detailed four-compartment head model (skin, skull, CSF, brain). This is a significant improvement in comparison to a three-compartment head model, which is frequently used in praxis, since the importance of modeling the CSF compartment has been shown in a variety of studies. CONCLUSION: The presented pipeline facilitates the use of five-compartment head models with the FEM for EEG source analysis. The accuracy with which the EEG forward problem can thereby be solved is increased compared to the commonly used three-compartment head models, and more reliable EEG source reconstruction results can be obtained.

Forward Modeling of Coronal Mass Ejection Flux Ropes in the Inner Heliosphere with 3DCORE.

Forecasting the geomagnetic effects of solar storms, known as coronal mass ejections (CMEs), is currently severely limited by our inability to predict the magnetic field configuration in the CME magnetic core and by observational effects of a single spacecraft trajectory through its 3-D structure. CME magnetic flux ropes can lead to continuous forcing of the energy input to the Earth's magnetosphere by strong and steady southward-pointing magnetic fields. Here we demonstrate in a proof-of-concept way a new approach to predict the southward field B z in a CME flux rope. It combines a novel semiempirical model of CME flux rope magnetic fields (Three-Dimensional Coronal ROpe Ejection) with solar observations and in situ magnetic field data from along the Sun-Earth line. These are provided here by the MESSENGER spacecraft for a CME event on 9-13 July 2013. Three-Dimensional Coronal ROpe Ejection is the first such model that contains the interplanetary propagation and evolution of a 3-D flux rope magnetic field, the observation by a synthetic spacecraft, and the prediction of an index of geomagnetic activity. A counterclockwise rotation of the left-handed erupting CME flux rope in the corona of 30° and a deflection angle of 20° is evident from comparison of solar and coronal observations. The calculated Dst matches reasonably the observed Dst minimum and its time evolution, but the results are highly sensitive to the CME axis orientation. We discuss assumptions and limitations of the method prototype and its potential for real time space weather forecasting and heliospheric data interpretation.

Hybrid Scheme for Modeling Local Field Potentials from Point-Neuron Networks.

With rapidly advancing multi-electrode recording technology, the local field potential (LFP) has again become a popular measure of neuronal activity in both research and clinical applications. Proper understanding of the LFP requires detailed mathematical modeling incorporating the anatomical and electrophysiological features of neurons near the recording electrode, as well as synaptic inputs from the entire network. Here we propose a hybrid modeling scheme combining efficient point-neuron network models with biophysical principles underlying LFP generation by real neurons. The LFP predictions rely on populations of network-equivalent multicompartment neuron models with layer-specific synaptic connectivity, can be used with an arbitrary number of point-neuron network populations, and allows for a full separation of simulated network dynamics and LFPs. We apply the scheme to a full-scale cortical network model for a ∼1 mm2 patch of primary visual cortex, predict laminar LFPs for different network states, assess the relative LFP contribution from different laminar populations, and investigate effects of input correlations and neuron density on the LFP. The generic nature of the hybrid scheme and its public implementation in hybridLFPy form the basis for LFP predictions from other and larger point-neuron network models, as well as extensions of the current application with additional biological detail.

Effects of uncertainty in head tissue conductivity and complexity on EEG forward modeling in neonates.

In this study, we investigated the impact of uncertainty in head tissue conductivities and inherent geometrical complexities including fontanels in neonates. Based on MR and CT coregistered images, we created a realistic neonatal head model consisting of scalp, skull, fontanels, cerebrospinal fluid (CSF), gray matter (GM), and white matter (WM). Using computer simulations, we investigated the effects of exclusion of CSF and fontanels, discrimination between GM and WM, and uncertainty in conductivity of neonatal head tissues on EEG forward modeling. We found that exclusion of CSF from the head model induced the strongest widespread effect on the EEG forward solution. Discrimination between GM and white matter also induced a strong widespread effect, but which was less intense than that of CSF exclusion. The results also showed that exclusion of the fontanels from the neonatal head model locally affected areas beneath the fontanels, but this effect was much less pronounced than those of exclusion of CSF and GM/WM discrimination. Changes in GM/WM conductivities by 25% with respect to reference values induced considerable effects in EEG forward solution, but this effect was more pronounced for GM conductivity. Similarly, changes in skull conductivity induced effects in the EEG forward modeling in areas covered by the cranial bones. The least intense effect on EEG was caused by changes in conductivity of the fontanels. Our findings clearly emphasize the impact of uncertainty in conductivity and deficiencies in head tissue compartments on modeling research and localization of brain electrical activity in neonates. Hum Brain Mapp 37:3604-3622, 2016. © 2016 Wiley Periodicals, Inc.

Uncovering cortical MEG responses to listened audiobook stories.

Naturalistic stimuli, such as normal speech and narratives, are opening up intriguing prospects in neuroscience, especially when merging neuroimaging with machine learning methodology. Here we propose a task-optimized spatial filtering strategy for uncovering individual magnetoencephalographic (MEG) responses to audiobook stories. Ten subjects listened to 1-h-long recording once, as well as to 48 repetitions of a 1-min-long speech passage. Employing response replicability as statistical validity and utilizing unsupervised learning methods, we trained spatial filters that were able to generalize over datasets of an individual. For this blind-signal-separation (BSS) task, we derived a version of multi-set similarity-constrained canonical correlation analysis (SimCCA) that theoretically provides maximal signal-to-noise ratio (SNR) in this setting. Irrespective of significant noise in unaveraged MEG traces, the method successfully uncovered feasible time courses up to ~120 Hz, with the most prominent signals below 20 Hz. Individual trial-to-trial correlations of such time courses reached the level of 0.55 (median 0.33 in the group) at ~0.5 Hz, with considerable variation between subjects. By this filtering, the SNR increased up to 20 times. In comparison, independent component analysis (ICA) or principal component analysis (PCA) did not improve SNR notably. The validity of the extracted brain signals was further assessed by inspecting their associations with the stimulus, as well as by mapping the contributing cortical signal sources. The results indicate that the proposed methodology effectively reduces noise in MEG recordings to that extent that brain responses can be seen to nonrecurring audiobook stories. The study paves the way for applications aiming at accurately modeling the stimulus-response-relationship by tackling the response variability, as well as for real-time monitoring of brain signals of individuals in naturalistic experimental conditions.