dataesr/scientific-paragraphs-categorization huggingface dataset:
2) Physiological synchrony Similarly to  [16] , we computed the Wavelet Detrended Cross-Correlation (WDCC) at Lag 0 to quantify the degree of synchronization between ECG time-series to assess the degree of physiological synchrony between the Pilot and JTAC within each couple. Mean WDCC values at Lag 0 were computed for each condition (coop, false coop, no coop) and each dyad (Pilot-JTAC synchrony), and analyzed with a one-way ANOVA with the cooperation condition as within-subject factor. G. Cerebral activity 1) Data Processing EEG data were recorded continuously at 512 Hz using 2 BioSemi Active2 systems (BioSemi, Amsterdam) with 64 active Ag-AgCl scalp electrodes positioned according to the International 10/20 system, and band-pass filtered between [0-104]Hz. During the experiment, electrode offsets were kept under 20 mV as recommended by the manufacturer to ensure high signal quality.
Preprocessing was performed using EEGLAB (V14. 1. 2 [22] ) and MATLAB (The Mathwork Inc. , v. 2019a). Data were first re-referenced offline to the algebraic average of all electrodes using the Prep pipeline function  [23] , high-pass filtered (1 Hz), and noisy portions of data were removed using the artifact subspace reconstruction (ASR) algorithm  [24]  with the clean asr function (default settings). Individualized frequency bands were computed to determine the frequency windows for delta, theta, alpha, beta and gamma bands for each participant. To do so, we computed the peak alpha frequency and shifted the other frequency band accordingly (see  [25]. 2.
ACKNOWLEDGMENT This work (Hyperscan project) was funded by  Advanced study Man Machine Teaming  funded by  DGA (French MoD)  ) and the  Chair for Neuroadaptive Technology (ANITI). We would like to thank  J. -S. de Visme ,  P. Mazoyer  and  D. Hauret  (Thales),  L. Dargent  ( Dassault Aviation ) for their support, expertise and relevant advice.
Data analysis. To calculate attention bias to threat (ABT), the proportion of time looking at the neutral and threat stimuli (out of total time looking onscreen) was calculated separately for each Neutral-Threat trial, with the neutral proportion then subtracted from the threat proportion. The equivalent approach was used to calculate attention bias to LPS stimuli, i. e. positive stimuli (ABP). A linear mixed model was then utilized to investigate potential differences between rearing groups and attention bias type at the trial level, with group (mother-reared or peer-reared), condition (threat or LPS), and their interaction included as fixed effects, and subject-specific intercepts as a random effect. Social rank (randomized Elo-ratings) was also included as a covariate, and z-scored for analysis.
Funding This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie  grant agreement No. 841210  (AnxNPS); the  Fondation de France  (No. 00079331 ); the  National Institute of Child Health and Human Development  ( P01HD064653 ); and the  Agence Nationale de la Recherche  ( ANR-19-CE37-0023 ).
The question is whether students will continue to maintain their level of EGI after EE. This research provides evidence that EGI, coupled with a high tendency to act during EE or high EII  (Klapper & Neergaard, 2017) , can contribute to sustained EGI following the program. Data were collected at two time points-during an EE course (time 1) and one year after its completion (time 2)-for a sample of 412 participants from Vietnam. Partial least squares path modeling (PLS-SEM) was then used to evaluate the differences in path estimators of EGI1 to EGI2 between two groups (low EII1 and high EII1). The context of this research is Vietnam, which has a nascent entrepreneurship education system. Students are not encouraged to take an active learning approach or adopt critical thinking. The Vietnamese culture appreciates stability and embraces conflict avoidance.
Like all research, this study has limitations that provide future research opportunities. Due to limited time and access, data on the treatment groups (students enrolled in a compulsory introductory EE course) were collected on only two occasions (t1 and t2). Thus, the study was unable to identify the exact patterns of changes in students' EI over a period. Because the study also showed that EI is a process that unfolds over time, further research could employ time series data on various control and treatment groups to provide further evidence. Qualitative methods (e. g. , interpretative phenomenological analysis; Smith & Shinebourne, 2012) focusing on the significance of individuals' lived experiences can also be combined to provide a richer description of the process through which students make sense of, form, and preserve their intentions both before and during entrepreneurial behavior.
P-values were corrected using the Benjamini and Hochberg 51 method to control for multiple comparisons. Statistical analyses were performed using lme4 package  52  , and lmerTest package  53  , while data visualization was realized using ggplot2 package  54  and sjPlot package  55  , in the R environment  56. The stem data (18. 75 Hz) and the suffix data (12. 50 Hz) were analysed separately. In addition, inter-modulation (IM) analyses were carried out to capture the interaction between the neural responses to stems and suffixes in the current data. The truly suffixed (TS) condition was treated as the reference (baseline) and relative parameters estimated for the pseudo (PS) and the non-suffixed (NS) condition, and then the pseudo suffixed condition was treated as the reference and relative parameters estimated for the non-suffixed condition (i.
g. , the stem farm and the suffix -er), leads to faster visual word processing as was evidenced by the IM trajectory of the truly suffixed condition (see Fig. 3 ). In contrast to suffixes, stems elicit more robust orthographic form representations that are less affected by semantic feedback, presumably because the majority of stems are free morphemes (i. e. , also occur in isolation). Indeed, Beyersmann and Grainger  33  argue that affixes, but not stems (with the exception of bound stems, e. g. flate in deflate), are marked as 'bound' at the level of the orthographic lexicon, which sends an early signal to the reading system that the lexico-semantic context is needed to complete the evaluation of the input letter string. This thus provides an explanation for why the present SSVEP data revealed a semantic activation boost for suffixes, but not for stems.
Apparatus and stimuli. Participants were seated 56 cm from the monitor in a dimly-lit, electrically isolated room. Stimuli were generated using OpenSesame 3. 0  65  and PsychoPy 66 and presented on a 17-in. cathode ray tube monitor (resolution 800 × 600 pixels, refresh rate 75 Hz). We used 153 French words selected from the Lexique database  67  , which were identical to the ones previously used in a lexical decision task  68. The 153 words included 51 French pseudo-suffixed target words (e. g. , couette [engl. blanket], consisting of the pseudo-stem cou [engl. neck] and the pseudo-affix -ette). Pseudo-suffixed target words were selected such that the whole word never shared any semantic relationship with the embedded pseudo-stem. In addition, we used 51 truly suffixed target words (e. g. , pochette [engl.
The left panel of this figure was created using FieldTrip (https:// www. field tript oolbox. org/)  72  , the right panel using MATLAB (https:// www. mathw orks. com/ produ cts/ matlab. html). www. biose mi. com). Three extra electrodes were used to monitor eye movements and blinks (two placed at the outer canthi of the eyes, one placed below the left eye). Other two extra electrodes were used for an off-line rereferencing (placed behind ears on mastoid bone). For the EEG analysis, we used EEGLAB  70  , ERPLAB  71  and Fieldtrip 72 toolboxes for Matlab and Matlab customized functions (Matlab 2014, The Mathworks). Data were re-referenced off-line to the average of left and right mastoid electrodes, bandpass filtered from 5 to 100 Hz and then segmented to include 200 ms before and 2000 ms after stimulus onset.
Epoched data were normalized based on a prestimulus period of 200 ms, and then evaluated according to a sample-by-sample procedure to remove noisy sensors that were replaced using spherical splines. Likewise, EEG epochs that contained data samples exceeding threshold (100 uV) were excluded on a sensor-by-sensor basis, including horizontal and vertical eye channels. On average, 1. 28% of the data were interpolated and 3. 77% of the data rejected. In addition, ten items (6. 29% of the data) were removed, because the embedded stem (e. g. , calc in calcaire) did not correspond to the orthographic form of the corresponding real word (e. g. , calque). The 10 removed items included 1 non-suffixed, 4 truly suffixed, and 5 pseudo-suffixed items. We ensured that the three sets of targets words were still matched following item removal on written word frequency, spoken word frequency, number of letters, number of phonemes, number of syllables, orthographic neighborhood, phonological neighborhood, ending length and uniqueness point.
In addition, the RESS boosts the signal-to-noise ratio (SNR) of SSVEPs that can be difficult to isolate from the ongoing activity in cognitive paradigms that require short presentation time. Two spatial filters were computed separately for each participant (i. e. , one filter for 18. 75 Hz and another for 12. 50 Hz) by pooling data across all three conditions (i. e. , TS, PS and NS), to avoid overfitting. The two filters were computed over the window [0-1000] to restrict the analysis to the spatial locations most active during the early stages of visual word recognition (see also below). The analysis was restricted to the fundamental frequency (e. g. , F = 18. 75 Hz) rather than higher harmonics (i. e. , integer multiples of the fundamental frequency F, such as F = 37. 5 Hz) because the response was clearly stronger at that frequency.
50 Hz are more diffuse than for the 18. 75 Hz input. For the latter (see left panel of Fig. 5) the most active locations were confined to the occipital area, i. e. , the visual cortex, where alpha activity is the prominent frequency. On the contrary, the spatial locations for 12. 50 Hz input were more diffuse and included parietal/central areas where alpha activity tends to be less prominent. The left panel of this figure was created using FieldTrip (https:// www. field tript oolbox. org/) 72  , the right panel using MATLAB (https:// www. mathw orks. com/ produ cts/ matlab. html).
Acknowledgements The study was supported by grants from the  European Commission  (grants  PIEF-GA-2013-622882  and  H2020-EU. 1. 2. 2. -FET Proactive grant 951910 ) to IPS. It also benefited from support from the  French National Agency for Research  ( ANR-15-FRAL-0003-01 ,  ANR-16-CONV-0002 ) and the  Excellence Initiative of Aix-Marseille University  (A*MIDEX). EB was supported by a  Discovery Early Career Researcher Award (DECRA)  from the  Australian Research Council  ( DE190100850 ). JG was supported by  ERC  Grant  742141.
Introduction: Alcohol use disorder (AUD) ranks among the leading causes of decrements in disability-adjusted lifeyears. Long-term exposure to alcohol leads to an imbalance of activity between frontal cortical systems and the striatum, thereby enhancing impulsive behaviours and weakening inhibitory control. Alternative therapeutic approaches such as non-invasive and invasive brain stimulation have gained some momentum in the field of addictology by capitalizing on their ability to target specific anatomical structures and correct abnormalities in dysfunctional brain circuits. Materials and methods: The current review, covers original peer-reviewed published research on the use of brain stimulation methods for the rehabilitation of AUD. A broad and systematic search was carried out on four electronic databases: NCBI PubMed, Web of Science, Handbooks and the Cochrane Library.
Materials & methods We completed a search on three electronic databases: NCBI PubMed, the Web of Science, Handbooks and the Cochrane Library. Articles in English and French language were both accepted. The following search terms (MeSH) were used on each database: "Alcohol" OR "Alcoholism", AND "Stimulation" OR "Transcranial magnetic stimulation" OR "Brain stimulation" OR "Transcranial Direct Current Stimulation" OR "Transcranial electrical stimulation" OR "Deep Brain Stimulation", OR "Cranial electrical stimulation" OR "Craving", and clusters of the former free terms, combined in multiple search strategies. On NCBI PubMed, this search was refined with 'original article' and 'human'. No restrictions on age, gender, article type, publication outlet or date were implemented for the search. Combinations of these different terms were tested in order to compare results.
Methods Adult CF patients were recruited from the Reims University Hospital Department of pulmonary medicine (France) and were included in the Research and Innovation in Chronic Inflammatory Respiratory Diseases (RINNOPARI) cohort. This study was performed in accordance with the declaration of Helsinki and approved by the Ethics Committee (Comité de Protection des Personnes-Dijon EST I, No. 2016-A00242-49) and was registered on clinicaltrials. gov (NCT02924818, date of registration: October 5, 2016). Adult CF patients referred to our center between January 2017 and June 2019 were considered for inclusion in this study. All patients received detailed information about the methods used and gave their written consent. The RINNOPARI project is a general project designed to study clinical, laboratory, functional, morphological, histological and microbiological characteristics in patients with chronic inflammatory lung diseases, including CF.
Patient demographic and clinical characteristics Demographic data (age, sex), body mass index (BMI), comorbidities, smoking status, alcohol, coffee, tea, energy drink consumption and treatments were systematically recorded. Patients filled in self-administered questionnaires on cough (Cough And Sputum Assessment Questionnaire: CASA-Q), dyspnea (modified Medical Research Council dyspnea scale: mMRC), pain (Numeric Rating Scale: NRS), insomnia (Insomnia Severity Index scale), sleep quality (Pittsburgh Sleep Quality Index), daytime sleepiness (Epworth Sleepiness scale: ES), snoring (Berlin Questionnaire), circadian rhythm (Morningness-Eveningness Questionnaire: MEQ), anxiety and depression (Hospital Anxiety and Depression Scale: HAD). Patients were asked about the presence or absence of restless legs syndrome according to the International Restless Legs Syndrome consensus criteria  [24] , the severity of which was assessed by the International Restless Legs Syndrome scale (IRLS).
Statistical analyses Data are expressed as median and range for quantitative variables and as number and percentage for qualitative variables. Comparisons between insomniac and non-insomniac patients were performed using Fisher's exact test for categorial data. As the Kolmogorov test showed that distributions are not normal, quantititative data were analyzed using a non-parametric Wilcoxon-Mann-Whitney test to assess significance between different conditions. A Spearman test was used to study the correlation between ISI and HAD scores. For all analyses, a two-sided p value < 0. 05 was considered significant. XLSTAT software (version 2019. 1. 3, Addinsoft company, Paris, France) was used to analyze and reformat data.
Methods Participants. Sample size was determined a priori based on past studies of metacognitive sensitivity  25, 30  , and constrained by participant availability. We initially aimed for a target sample size of 40 participants, and our initial sample (i. e. before outlier rejection) consisted of 37 healthy volunteers. We tested our hypothesis using multilevel regression models (see below;  50  , for which it remains difficult to perform classic power calculations, mostly because of the multiple sources of variance that must be taken into account  51  ). Because the study did not concern biomedical research and no medical intervention was performed, it was, according to French law, waived from getting an approval by an external ethical committee. Yet, an internal review board, the 'Comité de pilotage du Laboratoire d'Economie Expérimentale de Paris (LEEP)' , assessed and approved the nature of the study and procedures, which preserved the full anonymity of the participants with respect to experimental data collection.
Participants' mean Padua inventory score (mean = 57. 6, sd = 28. 8) was similar to Sanavio et al. 's study with non-clinical participants' (mean = 53. 6, sd = 27. 7 for male, mean = 62. 5, sd = 29. 2 for female)  36. Statistical models. Models were implemented using the lme4 57 package in R version 3. 4. 1  58. Regression coefficients (b) and their statistics (z-scores and P-values), calculated using the Wald method  57  , as well as bootstrapped 95% confidence intervals, are reported (see Table  S1  & S2). All predictors were standardized. For each regressor in the models, we checked for multicollinearity using the variance inflation factor (VIF; Shieh and Fouladi 59 ). A VIF smaller than 3 indicates that collinearity is not an issue  60. ℳodel #1 (ℳ 1 ). The model relates trial-by-trial fluctuations of the number of checks to trial-by-trial changes in RDM difficulty, as well as trait-level predictors including implicit and explicit metacognitive sensitivities (i.
Acknowledgements A. B. is supported by a fellowship from the  Agence Régionale de Santé Île-de-France. M. M. is supported by the  Fondation Bettencourt Schueller. K. N. was supported by the  European Research Area Network (ERA-NET) NEU-RON  ( JTC 2013  Grant "TYMON";  Coord. L. Mallet ) and  Fondation pour la Recherche Médicale  ( FRM grant : "TORPIDO" DPP20151033971; Coord: L. Mallet). The authors wish to thank  Maxim Frolov  and the  Laboratoire d'Economie Expérimentale de Paris (LEEP)  for their help in recruiting the participants.
Physiological Signal Non-stationarity and Classification Methodology Mental workload estimation using EEG has several advantages, although time has a notable effect on it. This phenomenon is mostly known as non-stationarity effect of EEG  (Raza et al. , 2019). In particular, the EEG signal even from same person can present different behavior with time. An instability in the raw wave form is noted by  Shakhnarovich et al. (2007)  in experiments spanning from hours to days. Alongside, large variations in signal amplitude was noted by  Suner et al. (2005)  within a day of recordings. Therefore, it eventually increases the difficulty in EEG-based mental workload classification as detailed by  Roy et al. (2013 Roy et al. ( , 2016b)). In their studies, the accuracy using a Linear Discriminant Analysis on spectral features from a task-irrelevant 800-ms window decreased from 60 to 50% when training and testing datasets were separated with respect to time.
The main S&R mission task was composed of three sub-tasks: a detection and identification one, a working memory one, and a flying sub-task as detailed below: • Detection and identification sub-task: simulated the process of UAV requests in the form of pop-ups in the U-track application (see Figure  2  screen 4, which corresponds to Area of Interest (AOI) 4. 2 in Figure  4 ). A beep sound initiated the requests and the human pilot had to search if there was any human present in a group of 9 gray scale images (extracted from the Norb database  LeCun et al. , 2004) , and had to answer with Yes or No. This was the highest priority sub-task, i. e. , of priority level 1. The detection and identification request was an event-based sub-task with 15 identifications to be performed for each experimental condition.
Data Acquisition Physiological and behavioral data from the participants were recorded thanks to three Lab Streaming Layer (LSL) streams that were created and published in a local network to be effectively recorded with LabRecorder, an LSL-based software 2. • The first LSL stream was devoted to electroencephalography (EEG), electro-oculography (EOG) and electrocardiogram (ECG) recordings. The EEG Biosemi system was used at 2,048 Hz, with 32 electrodes positioned following the standard 10/20 layout. Additionally, two electrodes were positioned below the right eye and at the outer canthi of the right eye to perform the EOG recordings. Lastly, two electrodes were placed on the participants' torso to perform the ECG recordings: 1 on the plexus and one on the left 5th intercostal. • The second LSL stream was used to collect data from the Tobii glasses eye-tracker (ET).
These selected timestamps were then used to get closest timestamps from the ET stream. Each epoch was then fed to their respective sub-pipelined for further processing, as detailed below: • EEG epochs were processed using python's MNE library 3. Each 6-s epoch was first passed through a band-pass filter for filtering out frequencies below 1 Hz and above 40 Hz, and then downsampled to 512 Hz. Ocular artifacts were automatically removed based on an Independent Component Analysis (ICA, fastica algorithm) using EOG channels as reference signals. Then, an average re-referencing was applied to the cleaned data. Cleaned epochs were then used to extract PSD features using the multitaper method. The extracted features are the absolute power in the following bands: θ (4-8 Hz), α (8-12 Hz), β (12-30 Hz), and γ (30-45 Hz).
• ECG 6-s epochs were passed through a high-pass filter (above 1 Hz), then peak detection was performed and R-R intervals were extracted. The Aura-healthcare 4 Python package was used to remove RR-interval outliers using the Malik rule  (Kamath and Fallen, 1995) , and then finally heartrate (HR) and heart-rate variability (HRV) temporal features were extracted using the same package. In particular, HRV is approximated using the Standard Deviation of the Normal-to-Normal (SDNN) RR-intervals. These HR and HRV temporal features were normalized with respect to the previous resting period by subtracting each feature with the average value of this feature in the resting condition. • ET 6-s epochs were first processed to extract fixations and blinks. Using Tobii glasses, the data was sampled at 100 Hz and the samples that were missing from continuous data stream were inferred as blinks.
Classification A feature selection approach based on the statistical analyses was used to focus on the most promising features for performing the classification step. Hence, only features for which there was a significant effect of the load condition were kept for the classification stage (see section 2. 7). Each feature vector (sample) considered for classification was extracted from non-overlapping 6-s epochs. To evaluate the impact of features grouped by the sensor used to acquire them, 7 combinations of features were considered: EEG-only features, ECG-only features, ET-only features, EEG and ECG features, EEG and ET features, ECG and ET features, and finally EEG, ECG, and ET features. Moreover, due to technical issues during the acquisition for two participants, the EEG and ECG data of only 14 participants were kept for the analysis, and 13 for the ET data.
Well-known classifiers that can be applied on small datasets were used: k-Nearest Neighbors (kNN), Decision Trees (DT), AdaBoost (AB), Gaussian Naive Bayes (GNB), Linear Discriminant Analyses (LDA), Quadratic Discriminant Analysis (QDA), and Support Vector Machine (SVM). Hence, we chose not to employ highly data demanding classification algorithms like deep learning, but also did not use Riemannian methods that-to our knowledge-are seldom used with recording methods other than EEG. Indeed the main focus here was to perform a multi-modal estimation. The training-test designs and cross-validation methodologies-traditional vs. ecological-that were compared are explained in detail in the following.