dataesr/scientific-paragraphs-categorization huggingface dataset:
However, to the best of the authors' knowledge, there currently exists no software package whose sole or primary purpose it is to simulate different, configurable, and/or custom types of EEG data, i. e. a dedicated, general-purpose EEG data simulation tool. We therefore present SEREEGA, short for Simulating Event-Related EEG Activity, making EEG data simulation more accessible to researchers. SEREEGA is a free and open-source MATLAB-based toolbox to generate simulated, eventrelated EEG data. Starting with a forward model obtained from a head model or pre-generated lead field, dipolar brain components can be defined. Each component has a specified position and orientation in the head model. Different activation patterns or signals can be assigned to these components. Scalp EEG data is simulated by projecting all signals from all components onto the scalp and summing these projections together.
SEREEGA is modular in that different head models and lead fields can be supported, as well as different activation signals. Five lead fields are currently supported, four pre-generated, the fifth customisable according to the user's needs from a standard head model. Five types of activation signals are provided, allowing the simulation of different types of systematic (eventrelated) activity in both the time and the frequency domain, as well as the inclusion of any already existing time series as an activation signal. This toolbox is intended to be a tool to generate data with a known ground truth in order to evaluate neuroscientific and signal processing methods, such as blind source separation, source localisation, connectivity measures, brain-computer interface classifier accuracy, derivative EEG measures, et cetera.
Principles of EEG Simulation To simulate EEG data, the toolbox solves the forward problem of EEG, prescribing how activation signals from specific sources in the brain are projected onto an array of electrodes on the scalp. In matrix notation, this can be written as x = As + , with x denoting the vector of the recorded or simulated scalp signal, s the source activation signal, A the projection matrix used to project signals from the source to the scalp electrodes, and denoting a vector of noise. In SEREEGA, the user defines s, A, and , allowing EEG data x to be simulated. Source activations are defined on the basis of so-called components: for each component, any number of different signal classes can be defined, which prescribe how corresponding activation signals are to be generated. For each signal class, a type of activation signal (for example, an event-related potential; see Section 2.
Notation Description Â := A •,h,• ∈ R n chs ×3 Projection matrix of one source A ∈ R n chs ×nsrcs×3 SEREEGA simulates one segment of EEG data, or one epoch, at a time, and repeats this n eps times to obtain a larger data set. In the following, we first consider a single epoch. The activation signal of each class, g k c ∈ R ntps , is considered as a vector which consists of the activation signal (i. e. the amplitude time series) for all n tps time points in this epoch. Thus, for each component k, its activation s k consists of the summed activations of all signal classes g k c assigned to that component, which are n k cls many: s k = n k cls c=1 g k c. For most signal classes, its exact activation signal g k c is determined procedurally at runtime based on the specified parameters. We now describe how an activation signal is projected to the channels.
Platform and License MATLAB R2014b or higher is recommended for SEREEGA. Some optional functions depend on the Digital Signal Processing (DSP) toolbox version 8. 6 (R2014a) or higher. EEGLAB  (Delorme & Makeig, 2004 ) is required as it is used for a number of functions. Lead field generation either requires additional head model files which can be downloaded from their respective websites, or the FieldTrip toolbox  (Oostenveld, Fries, Maris, & Schoffelen, 2011). Since SEREEGA is modular, future functions may have further dependencies. SEREEGA is licensed under the GNU General Public License, version 3, and the code is publicly available on GitHub 1. This manuscript refers to version 1. 0. 5-beta.
Terminology and Workflow SEREEGA is available as an EEGLAB plug-in including a graphical user interface (GUI) that allows the core steps of designing and running a simulation to be performed; see figure 1. For more advanced use, SEREEGA is based on written commands and assignments. A general configuration variable holds information about the number of epochs to simulate, their length, and their sampling rate. This configuration, as well as many other variables, are contained within MATLAB structure arrays. SEREEGA's forward model is contained in a lead field structure array. The lead field structure contains all possible sources within a virtual brain, modelled as dipoles at specific locations along with their projection patterns. The word source thus corresponds to an index in this lead field.
The Gaussian function uses MATLAB's DSP toolbox to generate noise with a spectral characteristic of 1/f n , with n either -2, -1, 0, 1, or 2. The uniform function first samples the signal from a uniform distribution and then applies a Fourier transformation to achieve the same spectral characteristic. Data For the inclusion of any given pre-existing time series, for example data generated elsewhere or taken from existing EEG recordings, a data class can be defined. It must be given a matrix of data, epoch-dependent indices, and an absolute or relative amplitude. It will project the indicated data scaled to the given amplitude for each simulated epoch. Autoregressive model A final type of class generates signals based on a linear autoregressive model (ARM), following the approach by  Haufe and Ewald (2016).
epochs = struct('n', 100, 'length', 1000, 'srate', 1000); leadfield = lf_generate_fromnyhead('montage', 'S64'); sourcelocs = lf_get_source_spaced(leadfield, 64, 25); signal = struct('type', 'noise', 'color', 'brown',. 'amplitude', 1, 'amplitudeDv',. 5); components = utl_create_component(sourcelocs, signal, leadfield); data = generate_scalpdata(components, leadfield, epochs); EEG = utl_create_eeglabdataset(data, epochs, leadfield); EEG = utl_add_icaweights_toeeglabdataset(EEG, components, leadfield); The epochs structure array contains three fields configuring the number of epochs, their length in ms, and the sampling rate in Hz. On the next line, a lead field is obtained from the New York Head using a preconfigured montage of 64 channels. Next, 64 source locations are randomly selected from the lead field, such that each source is at least 25 mm apart from all the others.
Discussion In this paper we presented the core functionality of SEREEGA, a free and open-source MATLABbased toolbox to simulate event-related EEG activity. SEREEGA allows any number of components to be defined in a virtual brain model, each with a specific location in the brain, a freely oriented scalp projection, and any number of signals. Five different types of activity are currently supported, allowing systematic effects in both the time and frequency domain to be simulated, mimicking known EEG features. Simulated EEG data can serve as ground truth for the development of new EEG analysis methods or the validation of existing ones. The presented data set serves as a demonstration of some of SEREEGA's functions: a set of known cortical effects was simulated, mimicking a hypothetical experiment.
The included stochastic noise processes follow current simulation standards, where in particular 1/f and 1/f 2 frequency scales have been identified to characterise EEG  (Bédard, Kröger, & Destexhe, 2006; Freeman et al. , 2009). This is most likely a simplification. Empirical EEG recordings may have different spectral properties, especially non-stationarities over time, and are influenced by deeper and smaller processes whose activity is not directly measurable using EEG. However, there is currently no consensus regarding an alternative noise simulation method. In SEREEGA, any time series can be included in a simulation using the data class. This also allows noise extracted from empirical EEG recordings to be used. Where available, this may be a better solution. In the future, we may consider a public database of such recordings for all users to draw from.
In fact, such a database could contain empirical recordings reflecting known cortical responses as well, allowing simulations to be pieced together by combining different parts of real EEG data. For this to be feasible, a large number of empirical data sets must be made available using a single, standardised format to describe them. Hierarchical Event Descriptor (HED) tags  (Bigdely-Shamlo et al. , 2016)  offer a way to do that, and the Human Electrophysiology, Anatomic Data and Integrated Tools (HeadIT) Resource is one such database that could serve as precursor to the database needed for this purpose (Swartz Center for Computational Neuroscience (SCCN), n. d. ). Notable sources of artefacts are the eyes and muscles. The head models currently included in SEREEGA are cortical models and do not contain sources in the eyes or muscles.
To the best of the authors' knowledge, no head model is currently available that does. A project is currently underway to construct a head model that contains a larger part of the human anatomy. For the moment, SEREEGA allows custom sources to be added to a simulation, by manually providing their coordinates and projection patterns. These can be taken from empirical recordings. When taking data from empirical recordings, potential anatomical differences should be taken into account. Since SEREEGA supports different head models, it is not necessarily the case that they use the same coordinate format (e. g. Talairach, MNI, or custom measurements). Sources found at the same [x, y, z] location in different empirical studies or SEREEGA scripts may correspond to different actual locations depending on the head model used.
Although authors who publish head models and lead fields often take great care to standardise their formats, they are not all uniform. Sometimes this is necessary: the dimensions of the Pediatric Head Atlas covering zero-to two-year-olds are of course smaller than those of an adult human's head model. SEREEGA includes plotting functions to investigate the size, shape, and location of a lead field's head model and the sources contained within, as well as to compare them against a standard adult head. To the extent that their details were sufficiently reported, all (software) simulations that were performed by the authors mentioned in the introduction of this paper can be done using SEREEGA. The five signal types and other functions of SEREEGA cover and extend the processes used in those methods.
Acknowledgements Part of this work was supported by the  Deutsche Forschungsgemeinschaft  (grant number  ZA 821/3-1 ),  Idex Bordeaux  and  LabEX CPU/SysNum , and the  European Research Council  with the  BrainConquest  project (grant number  ERC-2016-STG-714567 ). The authors thank  Mahta Mousavi  for the band-pass filter design,  Stefan Haufe  for the autoregressive signal generation code and accompanying support, and  Ramón Martínez-Cancino  for his assistance with the GUI development.
In the cases presented and analyzed here, we followed two teachers working in the same upper secondary school (grades 10 to 12): • Coralie teaches English. She has 20 years of experience as a secondary teacher but only 3 years in upper secondary education; • Valeria teaches mathematics. She has 33 years of experience as a secondary teacher (with only 4 years in lower secondary education). For each teacher, we collected the following data: • General data: a questionnaire about the teacher's profile; a general interview on the teacher's resources; representation of the teacher's resource system. • Data concerning a specific teaching unit: a video of the preparation of a lesson; a video of the lesson, interviews after the lesson; a logbook completed by the teacher throughout the process; collection of their main resources.
Use of digital resources Valeria mainly uses two kinds of digital resources: a platform called LaboMEP and an institutional repository called Eduscol. LaboMEP is designed by an association of mathematics teachers, Sésamath  (Sabra & Trouche, 2011). It is a complete virtual learning environment, consisting of textbooks, software and online exercises. Valeria only uses the online exercises. She uses it to reinforce her students' previous knowledge and be sure that all the students master the necessary knowledge before she starts introducing new content. It is connected with an o. i. she developed over the years: any grade 10 class is very heterogeneous. In this upper secondary school, the students come from more than twenty different lower secondary schools (indeed it offers specific technical options).
She does not know what they have learned before; grade 9 teachers might have quite different approaches. LaboMEP enables her to choose different exercises for different students. The students work on it in class, but at their own pace thanks to the software feedback and the help it proposes. Valeria considers that she could not provide such individualized feedback herself because of lack of time. Valeria also uses Eduscol, an institutional repository, to search for introductory activities. She usually starts each new chapter by giving her students an introductory problem. She does not keep records of the introductory problems she uses from year to year (unlike the course). She likes to search the Internet for new introductory problems each year. We observed her choice on Eduscol of a particular resource, for the introduction of the concept of variation of functions.
In mathematics, Valeria uses the online exercises in LaboMEP to review grade 9 contents. This is linked with an o. i. concerning the need to reactivate previous knowledge; the digital resource opens up the possibility to organize this as homework, with a feedback on their work for students (by the software), and an access to this work for the teacher. Using LaboMEP also leads Valeria to organize a more differentiated teaching, according to her students' different needs, and to develop o. i. linked with her interest in differentiated teaching. Moreover, Valeria uses many specific mathematical software -we saw above the example of GeoGebra -this use is linked with the official curriculum and initial mathematics teacher education evolution, which now, in France, dedicate an important place to such software.
B. Instrumentation The pendulum test apparatus consists of two bars connected by a low friction hinge joint that houses an absolute joint angle encoder and an analog gyroscope mounted on the segment aligned with the shank of the subject (Fig. 1 ). We used a gyroscope to avoid numerical derivation problems ensuing form the encoder data. We also recorded the muscle activity of the quadriceps m. and hamstrings m. This was accomplished with 10 mm flat pregelled Ag/AgCl electrodes (GS26, Bio-Medical Inc. , Warren, USA) and EMG amplifiers (Biovision Inc, Wehrheim, Germany) for bipolar recordings. The ground electrode was placed over the bony Signals from the sensors and EMG amplifiers were subsequently digitized (NI 6009 USB A/D card, 14-bit resolution, 1 kHz sampling rate). The data processing and further details concerning the device can be found in Popović-Maneski et al.
D. Data analysis The EMG signals were band-pass filtered between 30 and 300 Hz, normalized to the maximal value for each channel and each swing, and filtered with a notch filter at 50 Hz with a 3 rd order Butterworth filter. The signals from the absolute joint angle encoder and the gyroscope were filtered with the moving average that included 20 samples. Having in mind that resting angles in different subjects are not the same, we subtracted the mean values of the angle and velocity from the corresponding data sets. We developed a program for data acquisition in a LabVIEW environment, and automatic data processing with Matlab. The primary parameters from the pendulum test used in our analysis are: R2n -the normalized relaxation index, N -the number of swings, φmax -the first maximum of the goniogram after releasing the leg, and ωmax and ωmin -the maximum and minimum angular velocity of the shank as defined in  [24].
and in non-summer months. We mapped the extent of interpolation versus extrapolation and the proportion of data nearby in environmental space when models calibrated on existing survey data were used for prediction across the entire Mediterranean Sea. Using model predictions to map cetacean densities in the eastern and southern Mediterranean, characterised by warmer, less productive waters, and more intense eddy activity, would lead to potentially unreliable extrapolations. We stress the need for systematic surveys of cetaceans in these environmentally unique Mediterranean waters, particularly in non-summer months. A gap analysis-defined here as the process of assembling various datasets over a desired study area to identify where or when knowledge is lacking-is the first important step towards the development of large-scale species distribution models (SDMs)  [1] [2] [3] [4] [5].
In the marine realm, data collection is particularly difficult, resource intensive and expensive  6. As a result, our knowledge of mobile marine species distributions and densities is far from complete, underlining the need for gap analyses across large ocean basins. Gap analyses of mobile marine species observation data have led to identification of geographic areas and seasons with important knowledge gaps  7, 8. For example, a global gap analysis of line transect surveys used to derive abundance estimates and habitat-based density models of cetaceans has revealed large geographic gaps in the Southern Hemisphere and seasonal gaps in non-summer months  8. Assessing the geographic and seasonal coverage of species observation datasets is informative but not sufficient to assess the feasibility of SDMs, which typically rely on inferred species-environment relationships to derive predictions.
It is critical to assess the coverage of datasets in environmental space to evaluate the extent of extrapolation when a SDM calibrated on existing data is used for prediction across a study region. Extrapolation in environmental space can lead to highly uncertain predictions because species-environment relationships are unknown in unsampled environments  9, 10. The extent of extrapolation has traditionally been visualized with environmental envelopes based on the ranges of individual covariates spanned by the data  [10] [11] [12]. However, approaches based on such univariate envelopes fail to detect combinations of covariates that are within the univariate environmental space but outside the multivariate environmental space  13. An analysis of datasets' coverage in multivariate environmental space can flag potentially unreliable extrapolations resulting from these novel combinations  10, 14, 15.
Cetacean population monitoring represents no exception: line transect survey programs to estimate cetacean abundances have been implemented mostly by European countries in the northwestern and central Mediterranean  [23] [24] [25] [26]. The wide range of some cetaceans across the Mediterranean Sea (e. g. , fin whale and sperm whale  [27] [28] [29]  ), combined with their vulnerability to the multiple anthropogenic pressures  [30] [31] [32] [33]  , stress the need to develop predictive models to map their densities throughout the Mediterranean Sea. As part of a regional-scale collaboration, we assembled for the first time line transect survey data collected across the Mediterranean Sea to identify gaps in the geographic, temporal, and environmental coverage of survey effort. Our objective was to evaluate the feasibility of mapping cetacean densities in the entire Mediterranean Sea by using models calibrated on available survey data and various environmental covariates.
Discussion We aggregated over 300,000 km of cetacean line transect surveys suitable for estimating abundance that were conducted in the Mediterranean Sea within the past 20 years by numerous organisations. Survey coverage was heterogeneous geographically and seasonally: large data gaps were present in the eastern and southern Mediterranean in all seasons and elsewhere in non-summer months. Survey coverage was also heterogeneous in environmental space. Surveys covered a number of static bathymetric features (e. g. , coastal areas, continental slopes, submarine canyons and seamounts), but not the full range of dynamic oceanographic conditions found within the  Table  3. Spatial extent of extrapolation (i. e. , the percentage of cells of the study area where extrapolation occurred) with single covariates and combinations of covariates.
For dynamic covariates, the mean extent of extrapolation averaged over the 12 month period is provided, followed by the minimum and maximum monthly extents in parentheses. SST: sea surface temperature; PP: primary productivity; EKE: eddy kinetic energy. Mediterranean Sea. In particular, Mediterranean waters characterised by comparatively warmer temperatures, lower productivity and higher eddy activity were poorly surveyed for cetaceans. This raised the prospect that cetacean density models fitted to environmental covariates would have to be extrapolated in order to provide predictions for the entire Mediterranean Sea. To assess extrapolation in environmental space, we mapped two metrics: the extent of extrapolation versus interpolation-a binary metric indicating whether a point is within the convex hull enclosing the sampled environmental space-and the proportion of data nearby in multivariate environmental space-a continuous metric derived from Gower's distance.
It also identifies predictions that occur in sparsely sampled regions of environmental space despite being classified as interpolations by falling within sampled environmental space. For example, this situation was observed in the northern Adriatic Sea where low proportions of data nearby would occur although interpolation is predominant (see Fig. 6 ). Using the binary metric that distinguishes interpolation from extrapolation as the only measure of the reliability of predictions would lead to an overly high degree of confidence in predictions in the northern Adriatic Sea. Mesgaran et al. 15  proposed a modification of the Mahalanobis distance to assess deviations between the prediction and the calibration dataset using the mean and linear correlations between covariates  15. We did not follow their approach because the covariates we selected a priori were not highly correlated (see Methods).
Conclusion and Perspectives Systematic survey efforts for cetaceans have previously been recognized as heterogeneous across the Mediterranean Sea  18, 45, 46  , but this heterogeneity was never quantified. By aggregating surveys conducted throughout the Mediterranean Sea, we were able to identify gaps in the spatiotemporal and environmental coverage of cetacean surveys for the first time. We strongly recommend that future surveys be conducted in the eastern and southern Mediterranean where waters are characterised by warmer sea surface temperature, lower productivity, and higher eddy kinetic energy. These environmental conditions are found nowhere else in surveyed regions; they are unique to the eastern and southern Mediterranean and were under-represented in our dataset. Conducting surveys there would be particularly valuable in non-summer months because oceanographic conditions differ the most from those of other areas surveyed in those months.