dataesr/scientific-paragraphs-categorization huggingface dataset:
Two-dimensional sample entropy (SampEn 2D ) is a recently developed method in the field of information theory for evaluating the regularity or predictability of images. SampEn 2D , though powerful, has two key limitations: 1) SampEn 2D values are undefined for small-sized images; and 2) SampEn 2D is computationally expensive for several real-world applications. To overcome these drawbacks, we introduce the two-dimensional dispersion entropy (DispEn 2D ) measure. To evaluate the ability of DispEn 2D , in comparison with SampEn 2D , we use various synthetic and real datasets. The results demonstrate that DispEn 2D distinguishes different amounts of white Gaussian and salt and pepper noise. The periodic images, compared with their corresponding synthesized ones, have lower DispEn 2D values. The results for Kylberg texture dataset show the ability of DispEn 2D to differentiate various textures.
Although the results based on DispEn 2D and SampEn 2D for both the synthetic and real datasets are consistent in that they lead to similar findings about the irregularity of images, DispEn 2D has three main advantages over SampEn 2D : 1) DispEn 2D , unlike SampEn 2D , does not lead to undefined values; 2) DispEn 2D is noticeably quicker; and 3) The coeffi-.
This occurs because a small change in amplitude value may vary the order relations among amplitudes  [13] , even when the signal-to-noise ratio (SNR) of a signal is high (for more information, please see Figure  9  in  [13] ). To overcome the shortcomings of PerEn 1D and SampEn 1D , DispEn 1D has been very recently introduced as a fast and powerful technique to quantify the irregularity of signals  [7]. The dependency of DispEn 1D on a number of straightforward signal processing concepts via a set of synthetic time series and three real publicly-available datasets was previously evaluated. The results showed that the DispEn 1D technique noticeably outperforms PerEn 1D in terms of detection of dynamics of signals  [7]. Also, the results demonstrated that DispEn 1D is sensitive to changes in frequency, simultaneous amplitude and frequency, noise power, and noise bandwidth.
Another advantage of SampEn 2D is its invariance to rotation and translation  [22]. Moreover, two-dimensional PerEn (PerEn 2D ) was also proposed as an extension of its one-dimensional entropy counterpart  [23, 24, 25]. Thus, a generalization of the complexity-entropy causality plane to 2D maps was developed. PerEn 2D was able to detect different kinds of two-dimensional patterns  [23]. To take advantages of the performance of DispEn 1D over SampEn 1D and PerEn 1D  [7] , we introduce here two-dimensional DispEn (DispEn 2D ), as an extension of DispEn 1D. In this paper, we evaluate DispEn 2D on synthetic images and Brodatz and Kylberg publicly-available texture datasets, as well as on a real dataset of histological cardiac images. We show that the main advantages of the proposed DispEn 2D are: i) DispEn 2D , unlike SampEn 2D , does not result in undefined values for small images; ii) DispEn 2D is noticeably faster than SampEn 2D ; and iii) DispEn 2D leads to more stable results than SampEn 2D.
Synthetic Datasets 3. 1. 1. Texture Image with Additive Noise To evaluate the dependency of DispEn 2D on two-dimensional white Gaussian noise (WGN 2D ) and salt and pepper noise (SPN 2D ; sparsely occurring white and black pixels), we employed Lenna as a standard widely-used image, sized 256 × 256 pixels, shown in Figure  1. After normalizing the image in the range 0 to 1, we added different levels of uniform WGN 2D with mean (variance) equals to 0. 01 (0. 01), 0. 05 (0. 05), and 0. 09 (0. 09). We also added SPN 2D with different noise density values of 0. 01, 0. 05, and 0. 09 to the reference normalized image.
Real Datasets 3. 2. 1. Kylberg Texture Dataset We also used a subset of the Kylberg texture dataset. We selected 10 groups of images, each one includes 1000 samples, representing fabrics and surfaces of rotated images, namely floor1, floor2, scarf1, scarf2, rug1, rice1, screen1, ceiling1, blanket1, and canvas1  [31]. One sample, sized 576 × 576 pixels, of each of them is depicted in Figure  4. The dataset is publicly available at http://www. cb. uu. se/ ~gustaf/texture. For more information, please refer to  [31].
Brodatz Image Dataset The DispEn 2D method was also compared with SampEn 2D on the Brodatz grayscale texture album  [33]. This dataset is composed of 112 grayscale images representing a large variety of natural textures. This album is now widely used in the literature (see, e. g. ,  [34, 35, 36] ). From this album we extracted 9 groups of images, as already performed in another study dealing with SampEn 2D  [22]. From each group, we used one arbitrary sample image sized 128 × 128 pixels, as shown in Figure  5.
Stained cross-sections were captured using light microscopy (Leica DM5500B; Leica Microsystem, Wetzlar, Germany) at ×40 magnification. For each heart, one image from the septum of the left ventricle was selected for posterior analysis. During images acquisition, it was observed that histological tissue processing was deficient, which resulted in poor quality images. This fact makes this dataset particularly interesting to the present study, creating a scenario where it is possible to evaluate the performance of the two-dimensional entropy measures (DispEn 2D and SampEn 2D ) for the classification of low quality images obtained from different sources, i. e. pathological (MI) and non pathological (SHAM) groups. All the histological images were originally sized 2048 × 1536 pixels and RGB-colored.
To reduce the computational time, the entire dataset was downsampled to 1024 × 768 pixels with a linear interpolation and converted to a 8-bit grayscale representation prior to the analyzes. Figure  6  shows one representative image from each group (SHAM and MI). Comparisons of entropy values between groups in the cardiac histological image dataset were performed with Mann-Whitney rank sum test due to the small sample size. Significance was set at P < 0. 05.
Results and Discussion In this Section, we assess the ability of DispEn 2D to characterize different kinds of synthetic and real datasets described in Section 3. Note that for simplicity, we set m = [2, 2] and c = 5 for DispEn 2D in all simulations below, even though the ranges 3 ≤ c ≤ 8 and [1, 1] ≤ [m, m] ≤  [5, 5]  lead to similar findings (data not shown).
Synthetic Datasets 4. 1. 1. Texture Image with Additive Noise The two-dimensional entropy values obtained from the Lenna image with different amounts of WGN 2D and SPN 2D are shown in Table  1. They reveal that the addition of WGN 2D of larger mean and variance leads to higher entropy values for both the DispEn 2D and SampEn 2D methods. There is an overlap between the SampEn 2D values with mean and variance 0. 05 and Table  1 : Mean value and standard deviation (40 realizations) of DispEn 2D and SampEn 2D computed from the reference image (Lenna, Figure  1 ) on which different levels of bidimensional white Gaussian noise (WGN 2D ) and salt and pepper noise (SPN 2D ) were added. We used m = [2, 2] for and c = 5 for DispEn 2D. The parameters were set to m = [2, 2] for both the methods, c = 5 for DispEn 2D and r = 0.
Real Datasets 4. 2. 1. Kylberg Texture Dataset To assess the ability of DispEn 2D to be used as a feature extraction method for images or textures, we used the popular publicly-available Kylberg Texture Dataset. The DispEn 2D values for the selected Kylberg texture groups are demonstrated in Table  3. The results illustrate that there are no overlaps between entropy values of the ten selected groups, showing that DispEn 2D may be a useful metric to distinguish different patterns of fabrics and sur-.
Brodatz Image Dataset Results for the Brodatz dataset are depicted in Figure  8. The parameters values for both the entropy approaches are equal to those previously used, i. e. c = 5 (DispEn 2D ) and r = 0. 24 of image standard deviation (SampEn 2D ). Interestingly, the orders of the entropy values obtained by DispEn 2D and SampEn 2D of the images are quite similar. The difference between DispEn 2D and SampEn 2D was found only for the orders assigned with the three lowest entropy values. Moreover, both measurements show relative consistency. Relative consistency implies that, if SampEn 2D (m1, r1 )(S) > SampEn 2D (m1, r1)(T ), then SampEn 2D (m 2 , r 2 )(S) > SampEn 2D (m 2 , r 2 )(T ). Likewise, if DispEn 2D (m 1 , c 1 )(S) > DispEn 2D (m 1 , c 1 )(T ), then DispEn 2D (m 2 , c 2 )(S) > DispEn 2D (m 2 , c 2 )(T ) [3].
Cardiac Histological Images from Rats The results obtained by DispEn 2D and SampEn 2D for the cardiac histological image dataset are depicted in Figure  9. In general, the pixel patterns of cardiac fibers from SHAM animals present more irregularity (higher entropy) when compared to MI animals. The SampEn 2D of the SHAM animals presented a tendency to higher values compared to the MI, although no statistical significance was found for m = [1, 1], m =  [2, 2] , or m =  [3, 3]. DispEn 2D led to higher values for the SHAM compared to the MI for m = [2, 2] and m =  [3, 3]. The lack of difference for m = [1, 1] is not surprising as m = [1, 1] was identified not to be a good choice for DispEn 2D. Therefore, we can say that DispEn 2D is a valuable measurement to distinguish the SHAM to MI. Even though the DispEn 2D curves of the SHAM and MI are closer compared to the respective SampEn 2D curves, one must be aware that DispEn 2D has a much lower standard deviation (or CV, see Tables  5  and 6 ).
Therefore, the scaling of DispEn 2D and SampEn 2D are different and must not be directly compared. The Hedges' g effect size  [40]  was also employed to quantify the differences between the results for SHAM vs. MI. The Hedges' g test shows the difference between the means of two groups, divided by the weighted average of standard deviations for these two groups. The differences, illustrated in Table  4 , show that the highest effect size is obtained by DispEn 2D with m =  [3, 3] , suggesting the advantage of this method over SampEn 2D. Results with the cardiac histological dataset give strength to the assumption that irregularity may be a valuable source of information to distinguish the dynamics of images  [22]. Even if the present dataset includes low quality images, DispEn 2D was able to differentiate pathological from non pathological conditions.
Computational Time Figure  11  shows the computational time (in seconds) to calculate DispEn 2D and SampEn 2D in the Brodatz dataset, as a function of c or r, and different values of m. The time to compute DispEn 2D increases exponentially with c and is very sensitive to m. In contrast, the computational time of SampEn 2D increases linearly with r and is much less sensitive to m. However, the time required to calculate DispEn 2D is markedly lower than the time required to compute SampEn 2D. In the conditions of Figure  8 , in case m =  [3, 3] , the computation of DispEn 2D (c = 5) takes around 45 s, whereas that of SampEn 2D (r = 0. 24) takes from roughly 250 to 500 s (approximately 8 times higher computational time). The computational time is an important limitation of SampEn 2D that DispEn 2D notably outperforms.
Conclusion The aim of this study was to develop DispEn 2D based on Shannon's definition of entropy and the recently introduced dispersion patterns to quantify the irregularity or uncertainty of images. We evaluated the DispEn 2D method on synthetic and real datasets. The study done here has the following implications for the estimation of images' irregularity. First, DispEn 2D distinguished different amounts of WGN 2D and SPN 2D and discriminated the periodic images from their corresponding synthesized ones. Moreover, DispEn 2D was found to be a powerful feature extraction method to detect the patterns of images from different kinds of Kylberg textures. Furthermore, DispEn 2D detected different degrees of irregularity of MIX 2D. Also, the results from the Brodatz dataset showed the stability of DispEn 2D -based results.
In addition, DispEn 2D , unlike SampEn 2D , significantly discriminated the pathological from non pathological conditions in a dataset composed of low quality images. Finally, DispEn 2D has three key advantages over SampEn 2D : 1) DispEn 2D , unlike SampEn 2D , does not result in undefined values; 2) DispEn 2D is noticeably faster; and 3) DispEn 2D -based values are noticeably more stable than those obtained by SampEn 2D based on the coefficient of variation test.
We present a simple, reproducible analysis pipeline applied to resting-state magnetoencephalography (MEG) data from the Open MEG Archive (OMEGA). The data workflow was implemented with Brainstorm, which like OMEGA is free and openly accessible. The proposed pipeline produces group maps of ongoing brain activity decomposed in the typical frequency bands of electrophysiology. The procedure is presented as a technical proof of concept for streamlining a broader range and more sophisticated studies of resting-state electrophysiological data. It also features the recently introduced extension of the brain imaging data structure (BIDS) to MEG data, highlighting the scalability and generalizability of Brainstorm analytical pipelines to other, and potentially larger data volumes.
, 2006). Here we provide the proof of technical concept for a basic data analysis pipeline designed with Brainstorm  (Tadel et al. , 2011)  to extract group frequency-specific power analysis of regional source activity estimated with MEG, of healthy participants in the resting-state. We propose this pipeline as foundation to more sophisticated approaches and derivations, such as the extraction of restingstate network activity (e. g. ,  Florin and Baillet, 2015). Brainstorm is a free, open-source application developed in Matlab and Java for multimodal electrophysiology and imaging. The resting-state MEG data was obtained from the Open MEG Archive, OMEGA, a free repository of MEG data  (Niso et al. , 2016). OMEGA is organized according to MEG-BIDS, a recent extension of the Brain Imaging Data Structure [BIDS 1  ;  (Niso et al.
, 2018) ]. Brainstorm can directly import data from BIDS-organized data volumes. OMEGA contains multimodal data from 220 participants, for a total of 300 resting-state MEG recordings: 182 from healthy controls, 38 from patient volunteers (ADHD, chronic pain, etc. ) as well as the anatomical T1-weighted MRI (T1w-MRI) volumes of all participants. The present software pipeline is to demonstrate feasibility and reproducibility of the approach on the entirety of OMEGA, with generalizability to any other BIDS-organized data repository [see other MEG-BIDS data resources listed by  Niso et al. (2018) ]. The procedure produces maps of the regional power distribution of spontaneous brain activity in the typical frequency bands of electrophysiology: delta (2-4 Hz), theta (4-8 Hz), alpha (8-12 Hz), beta (15-30 Hz), gamma1 (30-80 Hz), and gamma2 (80-150 Hz).
MEG and MRI Data From the Open MEG Archive The OMEGA  (Niso et al. , 2016)   4  is a collaborative effort to build and share a free MEG data repository. A unique aspect of OMEGA is that the resource is open-ended in the sense that its framework is designed for continued data aggregation, from interested investigators across the MEG community. In addition to MEG and T1w-MRI, OMEGA features demographic and questionnaire data. Basic demographic information include age, gender, handedness, and education. Additional non-identifying demographic characteristics include spoken languages, general health, alcohol consumption and smoking habits, sleep quality, chronic pain, and years of musical education and practice. For demonstration purposes, we used a subset of the data from 5 OMEGA healthy participants (2 females, 27+/-5 y.
o. ), which is directly available from the open neuroimaging repository OpenNeuro. org 5 10. 5 GB. To demonstrate generalizability beyond MEG-BIDS organized data, we also provide supplementary online material in the form of the same pipeline applied to MEG data from the Human Connectome Project  (Larson-Prior et al. , 2013) ; see Brainstorm tutorial  6. The 5 individual MEG datasets were collected from participants sitting upright, keeping their eyes open on a fixation cross for 5 min. No task instructions were provided except to refrain from producing eye movements and to remain awake. The data was acquired with a CTF MEG system at a single site (Montreal Neurological Institute, McGill University), after approval from the institutional research ethics board and from participants consenting to have their anonymized data shared via OMEGA.
The MEG sensor array consisted of 275 axial gradiometers with 26 MEG reference sensors, located in a 3-layer magnetically shielded room. Sampling rate was 2400 Hz with a hardware anti-aliasing low-pass filter at 600 Hz. CTF 3rd-order gradient compensation was also applied. Bipolar electrocardiogram (ECG) and vertical and horizontal electrooculogram (EOG) data was collected on all subjects. Empty-room recordings (2-min duration or more) collected around each individual sessions were also retrieved from OMEGA to estimate the empirical noise statistics used in source modeling. The individual head shapes, anatomical landmarks and fiducial points were collected during sessions and retrieved from the OMEGA sample dataset ( *. pos files). Fiducial points marked the location of three head position indicator (HPI) coils placed on the subject's head: one on the forehead, one on the right, and the left mastoids.
HPI coils are to track head position under the MEG helmet. Anatomical landmarks consisted of nasion and left/right preauricular points (NAS, LPA, and RPA, respectively) marked to facilitate geometrical co-registration between MEG sensor locations and structural MRI data. Finally, the locations of about 100 scalp points on hard parts of the head (away from soft tissues such as neck, cheeks, and mouth) were also digitized. They were used to refine cross-modal MEG/MRI co-registration, as explained below. All digitized points were collected using a Polhemus Fastrak device, driven by Brainstorm. For anonymization purposes, T1w MRI images were defaced using free, open-source software (Face Masking 7  ;  Milchenko and Marcus, 2013). Scalp and cortical components were segmented and their envelopes were triangulated with Freesurfer 5.
A Note on MEG-BIDS BIDS is a community-driven emerging standard for the organization of neuroimaging data. It was designed originally for structural and functional MRI (fMRI)  (Gorgolewski et al. , 2016). BIDS is based on a principled hierarchical folder structure, where folders contain data and extracted metadata of key study parameters documented in text-based human and machine readable formats. We and collaborators recently contributed an extension of BIDS to MEG  (Niso et al. , 2018). Similar efforts are being pursued for scalp EEG and basic electrophysiology. MEG-BIDS facilitates data management and the design, sharing and transfer of analysis pipelines with well-distributed and -documented software applications [e. g. , Brainstorm  (Tadel et al. , 2011) , FieldTrip  (Oostenveld et al.
, 2011) , MNE  (Gramfort et al. , 2014) , and SPM  (Litvak et al. , 2011) ]. Brainstorm automatically imports (potentially large) MEG-BIDS datasets into its data management system, with the following folder/file organization for the OMEGA sample used in the present report: ds000247/ • sub-000X/: Raw data for subject with ID code 000X. • ses-0001/: Here, only one session per subject. sub-000X_ses-0001_scans. tsv: tab-separated text file listing the MEG recordings and corresponding acquisition dates. anat/: Anatomical MRI scans for subject 000X (not used if /derivatives/freesurfer/sub-000X/ses-0001 is available). sub-000X_T1w. nii. gz: compressed T1w MRI data in Nifti format. meg/: Raw MEG recordings. sub-000X_ses-0001_task-rest_run-01_meg. ds: Single run of MEG data. • derivatives/: Contains elements not considered as raw data.
" We then proceeded to importing the MEG-BIDS dataset directly, by selecting the menu item "File > Load protocol > Import BIDS dataset > Select the folder sample_omega. " We acknowledged all suggested default values during the import process e. g. , the decimation of FreeSurfer cortical surface down to 15,000 vertices. Once this step was completed, the OMEGA sample of 5 participants was directly imported into Brainstorm's data management system, including the associated empty-room recordings (Figure  1 ). The coordinates of the NAS/LPA/RPA anatomical landmarks are contained in the MEG-BIDS data package, in both MEG and MRI spaces ( * _T1w. json files). Brainstorm uses these coordinates and the digitized head shape to automatically refine MEG-MRI co-registration using rigid-body transformations that minimize the distance of these points to the scalp surface automatically extracted from the structural MRI data by Brainstorm.
This is performed during multimodal data importation and registration into Brainstorm's database. Note that the MRI defacing procedure preserved the location of all fiducials points and of the scalp geometry. It is key to ascertain that the subjects' head was well aligned under the MEG sensor array. To that purpose, we dragged and dropped the recordings from all subjects (excluding subemptyroom) into Brainstorm's Process1 box, and clicked on the "Run" button. We then selected from the process menu "Import anatomy > Remove head points, Z = 0. " Finally, we added another process "Import anatomy > Refine registration. " To verify the quality of the registration procedure, we rightclicked on "CTF channels > MRI registration > Check" for each of the 5 participants. Step 2: Pre-processing of MEG Data Some online signal processing was applied at the time of MEG acquisition (i.
e. , anti-aliasing low-pass filter below 600 Hz, CTF 3rd-order gradient compensation). Participant specific * _meg. json and * _channels. tsv files contain the details specific to each session and subject. Signal contamination from the environment (e. g. , powerline, mechanical vibrations, etc. ) or caused by participants (head and body movements, including breathing, eye blinks and saccades, heartbeats, muscle tension, and ferromagnetic prostheses) was evaluated and attenuated via the following good-practice preprocessing procedures  (Gross et al. , 2013). We first reviewed the frequency contents of raw signals with power spectral density (PSD) estimates of MEG sensor signals. To do so, we switched to the functional view of the protocol (second button above Brainstorm's database explorer).
Muscle contamination was prominent in subject sub-0004: it was corrected using "Artifacts > SSP generic: All file, 0 ms, 40-300 Hz, Use existing SSP, PCA, " with selection of SSP1 and SSP2. We reviewed the first 100 s of data for detecting bad segments in all subjects, with no further data rejection performed. The SSP/ICA cleaning procedure described here is an example designed for this specific dataset, with an emphasis on removing eye-and heart-related artifacts. For other types of experiments, acquisition devices, noise configurations or scientific questions, it might require adaptations  (Gross et al. , 2013). No preprocessing is a valid option: e. g. , if the 1-4 Hz frequency band is of no interest in a study, correcting for eye blinks might not be necessary. Step 3: MEG Source Modeling This section describes noise, head and source modeling to produce time-resolved maps of cortical currents in all participants  (Baillet, 2017).