dataesr/scientific-paragraphs-categorization huggingface dataset:
2) Data set 2: Motor Imagery-Execution tasks (MIE) data set Sixteen MT-BCI naïve participants were included in this study (aged 22. 31 ± 2. 33)  [23]. EEG signals were recorded using 11 scalp electrodes (FC4, C4, CP4, C6, FC3, C3, CP3, C5, C1, Cz, C2, 10-20 system). The participants completed 10 sessions; with different sessions being recorded on different days. Each session was composed of seven runs. Each run included 10 trials per task. The tasks were left or right hand motor imagination (LHI/RHI), right or left hand motor execution (RHE/LHE -with the other hand than the imagination), and rest. Two types of feedbacks, either visual feedback (5 sessions) or visual and vibrotactile feedback (5 sessions) were used. Here, we used the first 6 sessions of each subject: 4 sessions for training (all with the same feedback), and 2 sessions for testing (one session with each type of feedback, thus with possible non-stationarities), as for the MT data set.
3) Data set 3: CYBATHLON dataset This dataset was recorded from a tetraplegic user during multiple sessions  [35]. The sessions took place on several days at the user's home, an environment with low control on background luminosity, ambient sounds or electromagnetic interference, or in the lab. EEG signals were recorded with 46 active electrodes (FC6, C4, FC4, C6, CP6, T8, FC5, C3, C2, Cz, C1, C5, T7, CP5, FC3, PO4, P2, CP4, P4, CP1, CPz, Pz, CP2, O2, Oz, POz, O1, PO3, P1, P3, CP3, F1, F2, AFz, Fz, FC2, FCz, FC1, F8, F4, AF4, FP2, F7, F3, FP1, AF3, 10-20 system). Our experiments used data from 9 sessions (i. e, all multiclass and labelled sessions), called Se10, Se11, Se12, Se13, Se14, Se15, Se16, Se17, and Se18. Each run of this dataset comprised 10 trials per mental task (MC, LHI, RHI, and REst (RE)).
B. Data pre-processing For all three datasets EEG signals were band-pass filtered in 8-30 Hz using a 4 th order Butterworth filter. The location and length of the extracted epochs varies depending on the experimental setup of each dataset. We extracted windows from the feedback interval, i. e. , 3. 75 second and 4-second window from 1250ms after task cue onset for CYBATHLON and MT dataset respectively. For MIE dataset, 2-seconds windows were extracted for rest trials from 500 ms after the rest onset (an interval without feedback) and for LH/RH Execution/ Imagination from 2250 ms from task stimulus onset (selected from feedback interval). In all datasets, to prevent bias, we extracted the same number of epochs for each class. Besides, we rejected all epochs for which band pass-filtered EEG had amplitude higher/lower than +/-70 µV.
C. Experiments We performed experiments to 1) study possible relationships between channel number and non-stationarity, 2) compare the behavior of different selection criteria across channel numbers, 3) evaluate BCI performance using channel selection, and 4) evaluate our proposed efficiency predictor. As the evaluation criteria, we used the classification accuracy of a Riemannian MDM classifier  [9]  hereafter. In all our experiments, we used the same number of sessions, i. e, first 4 sessions as training set and last 2 sessions as test set, for all subjects of MIE and MT datasets. For CYBATHLON dataset we used sessions Se10-Se13 as training set and sessions Se14-Se18 as test set.
1) Between session non-stationarity effects First, we investigated the effects of between-session nonstationarity on data distribution. Fig. 1. shows a twodimensional representation of data for randomly selected subjects of (a) MIE dataset, (c) MT dataset, and (b) sessions Se14-Se18 of the CYBATHLON dataset. Different colors show different sessions and different shapes show different classes/tasks. We used t-SNE  [24]  to project the data points (EEG covariance) in two-dimensional space. In t-SNE, to consider the non-linear geometry of the space, we used as custom distance, the Riemannian distance (Eq. 2). We set the Perplexity parameter, which determines the effective number of local neighbors of each point  [24] , to the number of epochs extracted from each task in each run (i. e. , 10 in the case of MIE and CYBATHLON datasets and 15 for MT dataset).
Fig. 1. (a)  shows considerable overlapping between data recorded in different sessions, suggesting less non-stationarity effects as compared with (b) and (c), which show considerable shifts between different sessions, suggesting high non-stationarities. Different reasons could explain these differences in betweensession data distribution variations across datasets, e. g. , differences between subjects' ability to control the BCI or differences in the mental tasks used. However, similar patterns between datasets (i. e. , less shift in most subjects of the MIE dataset and more between-session variation in the MT and CYBATHLON dataset) may suggest larger non-stationarity effects with more channels. Indeed, the MIE data set only has a small number of channels  (11) , only located over sensorimotor areas, and little non-stationarity, contrary to the other two which have many more channels (30 and 46), all over the scalp, and display much larger non-stationarities.
To investigate differences in between-session nonstationarity across datasets, we compared the AIV for a whole training dataset versus the median value of AIV across the runs included in this set. For the MIE and MT datasets, the median of the increase in AIV for the training set vs. the AIV for the runs included in the training set is 1. 29% and 24. 96% respectively. For the CYBATHLON dataset the increase is 20. 18%. When keeping the same dimensionality across data sets, e. g. , No. of channels = 11 (when channel are selected based on the AIV criterion) the AIV increase is 20. 18% for the MT dataset and is 16. 64% for the CYBATHLON dataset, i. e. , a smaller non-stationarity than with more channels. The nonetheless larger non-stationarity of these two data sets may also suggest that the channel location spread also matters.
2) Selection Criteria specification To investigate the properties of selection criteria across channel set sizes, we considered the changes in BCI performance in terms classification accuracy across all possible channel set sizes. Here, we thus did not use a stopping criterion. plotted only 8 subjects (see Fig. 3. for average of 16 subjects and Table III for all subjects' performance). Due to the optimal channel subset size being highly subject specific, the average accuracy curve, which smooths the subjects' accuracy peaks, does not illustrate the actual efficiency of channel selection. The mean and standard deviation of the optimal number of channels for the MT dataset (marked in Fig. 2 ) for AIV and M-GM/V-based channel selection are 20. 66±5. 94 and 15. 66±7. 30 respectively. The dispersion in optimal channel subset size between subjects and based on different criteria confirms the necessity of tuning this parameter for each subject and criterion independently.
For AIV criterion, the fast increase in performance (i. e. , on average for the 10 first removed channels of the MT dataset over all 16 subjects) should be due to removing the channels mostly affected by non-stationarity sources. Using AIV as a selection criterion for small channel set sizes shows a drop in classification accuracy, e. g. , from 66% at peak to 37. 92% for only one channel, averaged over all subjects of MT. This drop in performance is probably due to considering only the variance in the channel selection, which finally keeps a channel subset with the lowest variance. This subset is not necessarily the most discriminative one. Considering the distance between means for M-M, M-M/VP, and M-GM/V criteria could lead to more stability in lower dimensions as compared with AIV (see Fig.
To evaluate the different criteria statistically, we compared the average classification accuracy they obtained over all subjects of the three datasets, when selecting either 20%, 40%, 60% or 80% of all channels in each dataset, using a Two-way repeated measure ANOVA with Tukey correction for Post-Hoc analysis. The ANOVA confirmed the significant difference between criteria (p<0. 001) and the significant interaction between criteria and channel subset size (p< 0. 001). Post-hoc tests showed that M-M/VP and M-GM/V lead to significantly better accuracy than M-M with p< 0. 001 and p < 0. 01 respectively. M-M/VP and M-GM/V are also both significantly superior to AIV (p<0. 001). There is no significant difference between AIV and M-M (p > 0. 05). The difference between M-M/VP and M-GM/V is not significant either (p>0.
3) Evaluation of the proposed stopping criterion In this sub-section, we report the BCI performances obtained when using the proposed stopping criterion to select the nearoptimal channel number, based on Algorithm 2. For the CYBATHLON dataset, applying channel selection based on different selection criteria, using 20 out of 46 channels, could lead to up to 5. 83% of improvement in test classification accuracy, as compared with using all channels (Table  I ). For the MT dataset (Table  II  -see Appendix E and Table  E. I for the subject-specific results), applying the repeated measure ANOVA to compare test classification accuracy after channel selection based on different criteria and for all channels confirmed the significant difference between the means of different approaches (p<0. 005).
Applying post-hoc analysis with Tukey correction confirms the significant superiority of AIV (with an average number of channels over subjects of about 22 out of 30) as compared with all channels with p<0. 005 and the significant superiority of other criteria as compared with all channels with p<0. 05. The performance difference between the criteria at near-optimal channel subset size is not significant. Applying repeated measure ANOVA on test classification accuracy after channel selection using different criteria on the MIE dataset (Table  III  -see Appendix E and Table  E. II for the subject-specific results) led to non-significant loss of performance (p> 0. 05). Applying post-hoc analysis confirmed that selecting the subsets based on our proposed criteria select a subset without significant loss of performance (p>0.
5 for M-M/VP and M-GM/V with on average; p>0. 1 for M-M and AIV criterion). For MIE dataset, the average number of selected channels over subjects for AIV, M-M/VP, and M-GM/V criteria was about 9 and for M-M was 7. 5 out of 11 channels. To further evaluate our proposed stopping criteria, we also compared it to using leave-one-session out cross-validation accuracy (over the training set) for selecting the optimal channel subset size. With this approach, the channel subset size which led to the maximum training set cross-validation accuracy was selected as the optimal subset size for each subject. We report the result of test classification accuracy averaged over all subject of each dataset in table IV (for more details see Appendix C). Applying one-way repeated measure ANOVA between the accuracies obtained by different selection criteria and all channels over all subjects did not show significant difference between selection by different criteria and all channels neither for the MT dataset (p > 0.
IV. CONCLUSION In this article, we have presented different criteria for channel selection in the Riemannian framework of covariance matrices for BCI application. Our main goal was to consider between session non-stationarity in order to reduce it. We compared the behavior of different criteria for different numbers of channels on three datasets with different levels of non-stationarity. We obtained about 5% improvement in MDM classification accuracy at a near-optimal number of channels, on the CYBATHLON dataset while keeping 20-28 channels (for different criteria) from 46 channels, and a no-significant loss in the case of the MIE dataset with a small number of channels  (11 channels). Channel selection on MT dataset leads to a significant improvement, 3. 1% on average, at a subject-specific near-optimal number of channels.
At the near-optimal number of the channels, the results from M-M are comparable with other criteria. For channel subsets selected based on leave-one session-out cross-validation over training set, M-M showed lower performance as compared with M-M/VP and M-GM/V. -Considering both the discriminative and dispersion information in M-GM/V and M-M/VP criteria led to a nonsignificant loss in classification accuracy while using only a small number of channels, both in datasets with a large or a small number of channels. They can thus be used to reduce BCI setup time (electrodes mounting) and computational complexity, and increase user comfort without significant loss of performance. -Comparing different criteria across different channel subsets over different datasets with different high and low nonstationarity effects confirmed the statistically significant superiority in classification accuracy after channel selection of our proposed criteria over M-M.
For channel subsets with small channel numbers, AIV and M-M show statistically significantly lower performance as compared to M-M/VP and M-GM/V. In the subsets with higher number of channels, the difference between criteria is non-significant. The differences when comparing M-M/VP vs M-GM/V and AIV vs M-M are not statistically significant. We thus suggest to use M-M/VP and M-GM/V when reducing channel number in our application. -Selecting a near-optimal number of channels by comparing AIV at run-level and training set-level can reveal significant improvements in accuracy using channel selection in the case of high non-stationarity effects or non-significant loss in the case of lower non-stationarity effects for all criteria. Although using cross-validation over the training set as stopping criterion could lead to superior performances for some subjects, it could not lead to statistically significant improvement when using channel selection when the data suffered from non-stationarity.
3. (a) (b)), as confirmed by statistical tests (see below). Comparing M-M/VP and M-GM/V criteria shows that there is a clear distinguishing pattern. Selection based on the M-GM/V criterion on the MT dataset, on average leads to lower classification accuracy in the last iterations as compared with the M-M/VP criterion (Fig. 3. (a)). Readers interested in how the M-GM/V criteria evolves with the number of selected channels or the channel selection criterion, globally or between pairs of classes, can refer to Appendices A. and B.
illustrates the trend of pair-wise class discrimination averaged over all subjects. Since the most discriminative class is not the same for all subjects, at each channel subset we compute the average over the first most discriminative pair, second-most discriminative pair, and least discriminative pair for each subject of MT dataset. Higher M-GM/V for M-GM/Vbased selection in small channel set size as compared with M-M/VP-based selection coincides with higher discrimination in two more discriminative pairs and more overlapping in less discriminative pairs (Fig. 2. (b)).
Introduction: Hypnotherapy is increasingly used in general medicine in France to manage health problems such as insomnia. There is some evidence to support the efficacy of hypnosis in treating insomnia but this evidence is based on methodologies of various strengths. This review aims to explore the methodological elements employed in hypnotherapy research to manage insomnia. Method: We performed a narrative review of the literature using systematic review methods focusing on treating insomnia with hypnosis. PubMed, Psycinfo, BASE and Cochrane databases and Google scholar were searched. Results: Overall, 25 studies were included consisting of 10 case studies, 11 randomised, controlled trials and 4 pre and post intervention studies. The study designs, intervention, control and comparators were heterogeneous, as were the hypnosis definitions and techniques.
Eligibility and inclusion In total, 208 articles were found; 204 using the search equations and 4 from the grey literature. In total, 55 articles were selected of which 25 were included in the final analysis. Of these, 21 were obtained from the databases and 4 from the grey literature. The full text of 5 articles was not retrieved (Fig. 1 ). All findings for each included study are listed in Appendix 1.
The IDEAL framework would be appropriate research process to evaluate therapeutic hypnosis. Our review was limited by the difficulty obtaining full texts for all articles and references due to their old publication date. Among the 208 articles identified, only 25 were included. This small number suggests our results should be interpreted with caution. The excluded articles often concerned other non-pharmaceutical therapies such as CBT, guided imagery, or relaxation. The included articles were only those defining the therapy used by the term "hypnosis". We are aware that certain other techniques such as guided imagery or relaxation can also induce a hypnotic state, but these techniques were not clearly listed, the indexing of these articles in the databases may be biased. Also, it is possible some studies may not have been included in the MeSH indexation and therefore not identified with our search equation.
Acknowledgment This work was supported by  Rennes General Medicine Department of Rennes, France. This article is supported by the  French network of University Hospitals HUGO  (' Hôpitaux Universitaires du Grand Ouest '). Editorial assistance in the preparation of this article was provided by  Angela Swaine Verdier ,  AURACOISE. Editing  and review of the manuscript was performed by Speak the Speech Consulting. This review is a preliminary step in a research protocol presently underway headed by i) INSERM, CIC-1414, Primary Care Research Team, F-35000 Rennes, France, with  E. Mener ,  Dr J. Poimboeuf ,  Pr F. Naudet , and  Dr A. Chapron , ii) the  Support unit for primary care research GIRCI GO, Pr JY. Le Reste, and iii) Clinical forensic toxicology laboratory CHU Rennes ,  Dr. T. Gicquel  and  Dr.
Sensory analysis Consumer preferences Temporal methods Alcoholic beverage Check-all-that-apply Multiple intakes a b s t r a c t This article describes a dataset providing temporal sensory descriptions and preferences for four lemon-flavoured carbonated alcoholic drinks. The recruited Japanese consumers (97 men, 96 women) corresponded to the target for this kind of drink: aged between 20 and 40 and regular consumers of flavoured alcoholic drinks. They had to consume a whole can of each drink at home, each on a different day. For sips 1, 4 and 7, they had to check from a check-all-that-apply (CATA) list of eight attributes (alcohol, bitter, carbonated, lemon, refreshing, sour, sweet aroma and sweet taste) that were applicable during three periods of perception -"in mouth before swallowing", "immediately after swallowing" and "aftertaste".
Data Description The dataset is provided as an excel file (. xlsx) including 5 sheets. The sheet "Consumers" provides information about the recruited consumers. "Panel" is the panel to which the consumers have been randomly assigned (RET: retrospective temporal description, SIM: simultaneous temporal description). "Consumer" is the 4-character code of the consumer. The "Panel" and "Consumer" columns are reported in each sheet following this one. "Gender" is the gender of the consumer (male or female). "Age" is the age of the consumer (numeric). "Frequency" is the frequency of drinking of flavoured alcoholic drinks of any type (approximately two or three days a week, approximately four or five days a week or almost every day). "Brand" is the brand that they most frequently drank in the past month (P2, P3, P4, P5 or Other).
"Score" is 1 if "Attribute" has been checked and thus considered applicable for "Product" by "Consumer" during "Period", 0 otherwise. The sheet "Hedonic" contains the liking and satisfaction scores of each consumer for the 5 products. "Sip" is the sip position for liking, (1, 4, or 7). "Attribute" is the code of the evaluated hedonic descriptor (Liking for question Q2: "The sip you just drank: How delicious did you think it was?", Satisfaction for question Q3: "After having finished the full can, how is your overall satisfaction?"). "Score" takes a numeric value between 0 and 10 (precision = 0. 01). The sheet "Crackers" contains in column "Answer" the answer to the question Q5: "How many crackers did you eat?" (numeric value between 0 and 12) for each consumer for the 5 products. The answers to the free text questions Q4 ("From the first sip to the last one, have you perceived any change in your sensations") and Q6 ("Please tell us what you felt about this tasting survey") have not been included in the dataset because of the number of missing answers.
EEG recording and data acquisition Continuous EEG recording and data acquisition procedures were similar across research centers. They were performed in a faraday cage for 3 minutes in an awake RS eyes-closed condition, using an EGI Hydrocel Geodesic Sensor Net (HGSN-130) dense array of 128 Ag/AgCl sensors (57) (Electrical Geodesics Inc. , Eugene, OR, USA). Participants were instructed to relax, remain as still as possible, and close their eyes. Impedances were kept under 100 kΩ  (58) , and the EEG channel was referenced to a vertex reference ( xed by the EGI system). EEG data were processed o ine using Netstation 4. 4. 2 (Electrical Geodesics Inc. , Eugene, OR, USA) before they were exported, given that we used EEG processing software developed in our laboratory (see the detailed method in 59).
Functional connectivity within RSNs We performed statistical analyses with sLORETA software to assess between-group differences in lagged phase synchronization within the three RSNs, namely the SMN, DAN, and DMN (Table  3 , Fig. 2  and summary Fig. 4 ). No difference was found for ASD > TD. Within the SMN, we identi ed signi cantly reduced connectivity between the medial frontal gyrus and right precentral gyrus in the ASD group relative to the TD group. Within the DAN, statistical analyses revealed reduced connectivity in the ASD group relative to the TD group 1) between the right precuneus-superior parietal lobule and both the right middle frontal gyrus and the right inferior parietal lobule, 2) between the left precuneus and both the right middle temporal gyrus and the right inferior frontal gyrus, 3) between the right middle temporal gyrus and the right inferior frontal gyrus, and 4) between the right middle frontal gyrus and the left superior parietal lobule.
80 level  (Cohen, 1988). Participants and design. A total of 240 first-year students of psychology and pedagogic science (n = 52 male and n = 185 female, 3 did not report, mean age 20. 31 years, SD = 3. 19) of a Dutch university participated in a laboratory experiment and were randomly assigned to one of two experimental conditions (i. e. , gain frame, loss frame without cash). Data from four students was removed from the analysis, as they gave an unclear account of their die roll. 7 Procedure and manipulation of frames. The procedure and manipulation of frames were the same as in the gain and the loss-nocash condition of Pre-study 3. Dependent measures. We assessed age, gender and the report of the die roll.
Magnetic resonance neurography (MRN), the MR imaging of peripheral nerves, is clinically used for assessing and monitoring peripheral neuropathies based on qualitative, weighted MR imaging. Recently, quantitative MRN has been increasingly reported with various MR parameters as potential biomarkers. An evidence synthesis mapping the available methodologies and normative values of quantitative MRN of human peripheral nerves, independent of the anatomical location and type of neuropathy, is currently unavailable and would likely benefit this young field of research. Therefore, the proposed scoping review will include peer-reviewed literature describing methodologies and normative values of quantitative MRN of human peripheral nerves. The literature search will include the databases MEDLINE (PubMed), EMBASE (Ovid), Web of Science, and Scopus.