dataesr/scientific-paragraphs-categorization huggingface dataset:
As the independent evaluation data are recorded on a different day than the training sessions, EEG signals of the subjects may change significantly from the training data to the evaluation data. This test aims at evaluating the robustness of the methods to non-stationary signals. In this test, the classifier is parameterized from the selected training session using the BP features from the corresponding (ω * × τ * ). The ω * bandpass-filtered EEG segments with the same time length as τ * (i. e. , T) are obtained from each entire single trial of testing data via a 0. 2 s step sliding window to generate continuous classification outputs (see Figure  8 ). According to the BCI competition requirement, the classification performances in the session-to-session transfers are measured by kappa coefficient  [32] : κ = (Acc -P e )/(1 -P e ) ( 18 ) where P e is the chance level for agreement (i.
e. P e = 0. 5 for two-class problems, so here κ = 2Acc -1). Thus, a larger κ value indicates a better classification performance. The mean kappa value over all subjects of the dataset is denoted by κ. For this dataset, six BCI groups have reported their results of session-to-session transfers in the BCI competition  [30]. We provide information on the methods of the first three winners in Table  3 , since their classification results are better than the other three. The first place winner used FBCSP  [17] , which we have briefly introduced in the introduction section. Except our method based on TFDF  [18] , no one has generated better results http://asp. eurasipjournals. com/content/2014/1/38 Figure  6  ERD/ERS for subject 6. These time-frequency maps display significant ERD (red) and ERS (blue) for subject 6 (a typical example) in BCI competition IV IIb.
The areas in the rectangles are the time-frequency ROI selected by the proposed methods based on TFDF (solid line) and F score (dashed line), respectively. than FBCSP on this dataset until now  [30]. The second place winner employed common spatial subspace decomposition (CSSD) with frequency band and time segment selections  [30]. The third place winner applied CSP on spectrally filtered neural time series prediction preprocessing (NTSPP) signals  [30]. Note that these methods involved frequency and/or time optimization process(es). The results of session-to-session transfers for all methods are provided in Table  4. The κ values and the number of electrodes (#E) used in the classification are given. TFDF generates the best mean κ value ( κ = among all methods in the independent evaluation.
In fact, the performances for subject 3 are much poorer than those for the other subjects for all methods, so that results averaged over all subjects might not be representative. Both time-frequency criteria (TFDF and F score) yield better performances than the third place winner ( κ = 0. 46, both p < 0. 01) and those obtained by broad band (8 to 30 Hz) EEG segments (0. 5 to 2. 5 s) with CSP ( κ = 0. 41, both p = 0. 01) and without CSP ( κ = 0. 53, both not significant by p > 0. 05, even if TFDF and F score outperform it for seven and five out of nine subjects, respectively). Thus, both criteria are promising for seeking optimal time-frequency patterns to improve classification performance of BCIs based on a few bipolar channels. As all of the first three BCI competition winners have used all three bipolar channels (C3, Cz, and C4) provided by the dataset, our methods not only generate good performances but also use less channels, which indicates that they may also be helpful for channel reduction.
Electrode reduction for dataset IIa In this dataset, the time-frequency ROIs are estimated by the two criteria, respectively, for nine possible channel combinations of C3 and C4 (see Figure  5, C3-C4: a-a, a-p,  a-l, p-a, p-p, p-l, l-a, l-p, l-l ). The scatter plot of classification accuracies (Acc) obtained by using the TFDF vs. those obtained by using the F score in time-frequency optimization for all channel combinations and all subjects is shown http://asp. eurasipjournals. com/content/2014/1/38 (F) Scatter plot of Acc obtained by broad band (8 to 30 Hz) EEG in the fixed time segment (0. 5 to 2. 5 s) with CSP filtering vs. those obtained by the same EEG but without CSP filtering. For the points above the diagonal in each scatter plot, the method in y-axis outperforms the method in x-axis in the cross-validation on the corresponding training session.
2-s step sliding window to generate continuous classification outputs (see Figure  8 ). As this study focuses on the two-class (right vs. left hand) problem, it is difficult to compare with BCI competition winners' results (reported based on a four-class problem including tongue and feet motor imagery data) on this dataset. Here, we compared the results obtained by our method with those obtained by FBCSP a  [17] , sparse CSP (SCSP)  [33] , and classic CSP, respectively. Note that FBCSP is believed to be an effective method that well solves the frequency and/or time optimization  [14] , which has achieved the best classification performance on at least two datasets including dataset IIa in BCI competition IV  [30]. SCSP is an optimized CSP that selects the least number of channels in CSP-based classification under a constraint of classification accuracy.
SCSP has generated better performances than other channel reduction methods (based on the usual Fisher ratio, mutual information, SVM, CSP coefficients) and the regularized CSP (RCSP) on BCI competition IV dataset IIa for the right vs. left hand problem (for details, see  [33] ). The comparisons of classification results and the number of electrodes (#E) used in classification between different methods are given in Table  6. As other researchers provided their classification results as classification accuracy values (Acc, defined in section 'Improving classification performance for dataset IIb') for the right vs. left hand problem on this dataset, we also provide Acc values for the sake of comparison. Table  6  shows that all methods generate better mean performances than the classical CSP algorithm with all 22 monopolar channels (mean classification accuracy, Acc = 77.
26%), indicating the interest of time-frequency selection and electrode reduction. Our method based on F score (Acc = 79. 67%) yields slightly better results than FBCSP (Acc = 79. 17%) and SCSP (Acc = 79. 07%) but using far less electrodes on this dataset: our method used only the two bipolar channels C3 and C4 (equivalent to four monopolar channels); FBCSP used all 22 monopolar channels, and SCSP used 8. 55 monopolar channels in average  [33]. Further examination of individual results shows that our method based on F score generates the  best Acc for most subjects (four subjects), indicating that it is the most effective on this dataset. Although the mean classification result of our method based on TFDF (Acc = 78. 00%) is slightly lower than those of FBCSP and SCSP, the differences are not statistically significant (p > 0.
Discussion A possible widespread use of BCI is limited by many issues, such as the inter-subject variability and the number of electrodes used. Individual differences of brain pattern will deteriorate the performance of BCI when using a general parameter setting, such as features from a broad frequency band (8 to 30 Hz), for all subjects. In this paper, subject-specific time-frequency characteristics are captured by the proposed method to solve this problem, so as to increase the inter-subject robustness. By this subject-specific time-frequency optimization, our method improves the performance of BCI, in particular when only a few channels of data are available. As our method is applied with only two bipolar channels, it also reduces the number of electrodes required in a BCI system. In the proposed method, two alternative criteria, TFDF and F score, are proposed for measuring the discrimination power of each possible time-frequency region.
The F score is a data-driven criterion and easy to compute. As an improvement to the Fisher criterion, which is typically used to measure the discriminative power of a single feature in BCI  [20] , the F score provides an effective measure for evaluating the discriminative power of a group of features (a multi-dimensional feature vector), in particular for time-frequency selection in motor imagerybased BCI. As the F score does not require any prior knowledge of neurophysiology, it might be possible to extend its applications on other problems outside the BCI field. The comparison between the two criteria shows that the F score generated better cross-validation performances than the TFDF on both datasets (see Figures  7E  and 9 ). As the F score tends to select the time-frequency region by minimizing the overlapping area between two classes (see Figure  3 ), it is not surprising to have these results when the testing and training data are from the same session and recorded during the same day.
However, as we mentioned, a real BCI problem can be more complicated than the cross-validation within one session. When the testing and training data are recorded in two separate days, the unpredictable data evaluation may happen due to slight shift of electrode positions and impedances and possible changes in the subject's motivation level  [25]. This phenomenon gives TFDF a chance to outperform the F score for some subjects (such as subjects 1, 3, and 6 on dataset IIb and subject 4 on dataset IIa), since TFDF selects the time-frequency region not just based on the statistical distribution of features but more on task-relevant ERD, whose frequency characteristics may not change a lot between different days for the same subject. Although TFDF and F score alternatively outperformed each other on two different datasets, both of them generated better individual performances than the state-ofthe-art methods in most subjects for both datasets as we mentioned in the 'Experimental results' session.
Thus, generally speaking, our method, either based on TFDF or F score, is robust to session-to-session transfers. As a result, the training data only need to be collected one time for learning the subject-specific time-frequency region and training the classifier, and then the parameters can be used on the same subject for a long-term on-line classification. The time for collecting the training data, on the one hand, depends on the amount of training data required for well describing the different classes; on the other hand, it is affected by the time needed for skin preparation and electrode placing. As the proposed method uses less electrodes than other methods, it will Comparison between using four electrodes and using three electrodes on dataset IIa. This figure shows the comparison between performances of using four electrodes (two bipolar channels) and using three electrodes (two monopolar channels using Cz as common reference) for TFDF and F score, respectively, in session-to-session transfers on dataset IIa.
Acknowledgements This work was partially supported by grants from  China Scholarship Council and Orange Labs. Authors would like to thank  Dr. O. Kyrgyzov  ( Whist Lab, France ) for some useful discussions,  Ms. M. Arvaneh  and  Dr. K. K. Ang  ( I 2 R BCI Lab in A*STAR, Singapore ) for providing the results of FBCSP and helpful suggestions in experimental comparison.
Both programs were run in MatlabR2007b (MathWorks). We used a continuous carry-over design  (Aguirre, 2007)  to measure the effects of one stimulus upon the next using a first-order serially balanced sequence of stimuli known as type-1-index-1  (Nonyane and Theobald, 2007). Our carry-over sequence was defined by eight items: the seven morph steps of a continuum (always within identity) plus one silent null event. One complete carry-over sequence with eight items comprises 65 stimuli. In this balanced sequence, each stimulus is preceded and followed by every other stimulus an equal number of times. Thus, every item is repeated eight times per sequence (except for the first item, which is repeated nine times; for a sample sequence of eight items, see http://www. bioss. ac. uk/people/cmt/seqlist.
T2*-weighted functional scans were acquired using an interleaved ascending sequence consisting of 32 slices of 3 mm thickness (0. 3 mm gap) with an in-plane resolution of 3 ϫ 3 ϫ 3 (FOV ϭ 1260°), an acquisition matrix of 70 ϫ 70, and a flip angle of 77°. The three runs of the experimental scan (TR ϭ 2 s, TE ϭ 30 ms) consisted of 446 volumes each, while the voice localizer (TR ϭ 2 s; TE ϭ 30 ms) scan consisted of one run of 310 volumes and allows reliable identification of the temporal voice areas by contrasting vocal with nonvocal sounds. All MRI data were analyzed using SPM8 (Wellcome Department of Cognitive Neurology, University College London, http://www. fil. ion. ucl. ac. uk/spm/). Preprocessing of the data consisted of anterior commissure-posterior commissure alignment of the anatomical images (and application of the orientation change to all functional images acquired in the same session).
Cytofluorometry Cells were washed and maintained in PBS for cell counting and subsequent staining. For intracellular staining, a PBS/BSA/ saponin solution was used to dilute both primary and secondary antibodies. Cells were then evaluated by flow cytometry in a FACSCalibur or a FACSCanto II device (Becton Dickinson, San Jose, USA); analyses were done using a CellQuest or FACSDiva software (Becton-Dickinson).
Real-time quantitative polymerase chain reaction (RQ-PCR) Following mRNA isolation and cDNA synthesis, RQ-PCR was done with TaqMan Universal PCR Master Mix (Applied Biosystems, Foster City, USA) in a 7900HT Fast Real Time PCR System and analyzed in SDS 2. 3 software (Applied Biosystems). Primers and probes were designed using the Primer Express software (Applied Biosystems). NRP2 sense: TGCCCA-GCTACGACATGGA; NRP2 anti-sense: CAATCTCTCCG-GAACGTCCTT; NRP2 probe CCAGATTGTGTTCGAGG-GAGTGATAGGG; SEMA3F sense: TCGCCCCAAGC-CACTGT; SEMA3F anti-sense: AGCGGTCCTCTGCAC-GAAT; SEMA3F probe: CAGCGAGATCCTGGTGACCGGC; S1P 1 sense: GGCTCTCCGAACGCAACTT; S1P 1 anti-sense: CAGGCTTTTTGTGTAGCTTTTCC; S1P 1 probe: TTTCC-GAGGCCCTCTCCAGCCA; ABL sense: TGGAGATAA-CACTCTAAGCATAACTAAAGGT; ABL anti-sense: GATG-TAGTTGCTTGGGACCCA; ABL probe: CCATTTTTGGTT-TGGGCTTCACACCATT.
Acknowledgments We thank all participants in the  LL03 ,  GRAALL2003  and  GRAALL2005  trials for collecting and providing data and samples; the  Necker Children's Hospital  for providing the thymus samples;  Corinne Garcia-Cordier  and  Meriem Garfa  ( ReneDescartes University Medical School, Paris, France ) for assistance with flow cytometry and confocal analysis respectively.
INVERSION EXAMPLE To illustrate how real datasets can be inverted and different response models compared, we take the data of one subject from  Iglesias et al. (2013). This consists of 320 inputs u and responses y. Our perceptual model is the three-level HGF for binary outcomes without sensory noise, and a first choice of decision model is the unit-square sigmoid of Equation  44. Using the HGF Toolbox (http://www. translationalneuromodeling. com/tapas), we specify the following priors (mean, variance) in appropriately transformed spaces: μ (0 ) 2 : (0, 1), σ 2 : (0, 1), μ (0) 3 : (1, 0), σ (0) 3 : (0, 1), κ : (0, 2) ω : ( -4, 0), ϑ : (0, 2), and ζ : (48, 1). The variance of 0 on μ (0)  3 and ω fixes these parameters to 1 and -4, respectively. The spaces for σ (0)  i and ζ were log-transformed while κ was estimated in a logit-transformed space with upper bound 6, and ϑ was estimated in logit-transformed space with upper bound 0.
005, 0, 9), and ζ (log, 48, 1). The prior mean of the response variable ζ was chosen relatively high to provide shrinkage on the estimation of decision noise. This procedure was repeated for four different optimization methods which are commonly used but possess different properties with regard to computational efficiency and robustness to getting trapped in local extrema: 1. Nelder-Mead simplex algorithm (NMSA), 2. Gaussian process-based global optimization (GPGO), 3. Variational Bayes (VB), 4. Markov Chain Monte Carlo estimation (MCMC). In brief, NMSA  (Nelder and Mead, 1965 ) is a popular local optimization algorithm which is implemented, for example, in the fminsearch function of Matlab. VB also optimizes locally (by gradient descent); for details see  Bishop (2006, p. 461ff ). For our simulation study, we used VB as implemented in the DAVB toolbox, available at http://goo.
To facilitate such practical applications, we have developed a software toolbox based on Matlab that is freely available for downloading as part of the TAPAS collection at http://www. translationalneuromodeling. org/tapas/. The HGF toolbox is specifically tailored to the implementations of discrete time filtering models, as opposed to the DAVB toolbox, which is mostly aimed at inverting dynamic models in continuous time. The HGF toolbox implements most of the models described in this article, plus some additional ones, and will be the focus of a forthcoming article. We further this to get p x (k) i , x (k) \i , χ|μ (1. k) 1 = n j=1 p x (k) j |x (k-1) j , x (k) \j , χ p x (k-1) j |μ (1. k-1) 1 dx (k-1) j p x (k) j |x (k-1) j , x (k) \j , χ = N x (k) j ; x (k-1) j , t (k) exp κ j x (k) j+1 + ω j (B4) p x (k-1) j |μ (1.
ACKNOWLEDGMENTS The authors gratefully acknowledge support by the  UCL-MPS Initiative on Computational Psychiatry and Ageing Research (Christoph D. Mathys) , the  René and Susanne Braginsky Foundation (Klaas E. Stephan) , the  Clinical Research Priority Program "Multiple Sclerosis" (Klaas E. Stephan) , and the  Wellcome Trust  ( Karl J. Friston ).
Data and Image Treatment and Statistical Analysis Image treatment and quantitation were performed using NIH ImageJ  (Schneider et al. , 2012). Data were compiled using Microsoft Excel software and fitted using SigmaPlot or IgorPro software. Curves are compiled from at least five independent experiments, in which experimental points are at least duplicates. Data are presented as mean values and SEM (error bars). Constants are provided with the error of the fits. The treatment groups were compared by one-way ANOVA. Differences were considered significant if p < 0. 05 (*) and p < 0. 01 (**).
ACKNOWLEDGMENTS This work was supported by the  University of Nice-Sophia Antipolis , the  ANR  ( JCJC SVSE1 NHEint ), and the  CNRS. N. M. was funded by the  Basileus EMECW  project. We thank  Drs. Jacques Barhanin  ( LP2M CNRS-UMR 7370 ),  Bruno Antonny  (IPMC,  CNRS-UMR 7275 ), and  Mireille Cormont  ( C3M INSERM-U 1065 ) for fruitful discussions,  Dr. Ellen Van Obberghen-Schilling  ( IBV CNRS-UMR 7277 ) for critical reading of the manuscript, and  Fabien Labbal  ( LP2M CNRS-UMR 7370 ) for assistance in cell culture.
Time-varying loudness and spectral centroid were derived from the time-varying specific loudness of the sound signals, as computed according to the model of  Glasberg and Moore (2002). Time-varying periodicity and pitch were computed using the Praat software  (Boersma & Weenink, 2009). For each of the sequences, we computed 12 different measures of low-level dissimilarity based on the application of each of 3 different operators on each of the 4 time-varying features. For each of the low-level time-varying features, median dissimilarities were computed as follows: (i) extract the median of the time-varying feature for each of the sounds in the sequence or pair; (ii) compute a matrix of within-sequence pairwise dissimilarities defined as the absolute difference of the median feature between each pair of sounds within the sequence; (iii) average the pairwise dissimilarities within the sequence to hold an overall score of within-sequence dissimilarity of medians.
The matrix of cross-correlation dissimilarities computed between each pair of sounds within the sequence was averaged in order to yield a sequence-specific measure of cross-correlation dissimilarity. No averaging was necessary for the pairs presented in Experiment 2 because they comprised only two sounds. We then assessed the effects of the experimental factors on the within-sequence/pair low-level dissimilarity in each of the Experiments. We did not adopt a multivariate approach (e. g. , MANOVA) because of the low number of data points, i. e. , sound sequences, within each cell of the experimental designs  (Tabachnick & Fidell, 2007). We instead carried out analyses on a score of overall low-level dissimilarity computed  by collapsing information across the 12 acoustical dissimilarities. To this purpose, we: (i) rank-transformed each of the 12 dissimilarities; (ii) normalized each of them between 0 (lowest dissimilarity) and 1 (highest dissimilarity); (iii) averaged the 12 ranked and normalized dissimilarities to yield one final score of low-level dissimilarity.
fMRI data acquisition Participants were scanned with a Siemens 3T Trio scanner, using a Siemens CP head coil. Sound sequences were presented through MRI-compatible electrodynamic headphones (MR-Confon Starter F system with Peltor Optime H510A earmuffs) at a comfortable level. For both experiments, the echo time was 30 msec, and the TR was 10 sec, composed of a 2-sec acquisition time and an 8-sec silent period during which sound sequences were presented against a silent background. During Experiment 1, sequence playback began at the end of the volume acquisition, i. e. , ISI ¼ TR. During Experiment 2, the onset of the sound sequence was jittered between 1. 5 and 2. 5 sec after the end of the volume acquisition. Each brain volume contained 28 slices of 3. 2 mm thickness (inter-slice gap ¼. 64 mm) in an axial orientation along the direction of the temporal lobe, providing near full-brain coverage.
fMRI data analysis Analyses were carried out using SPM8 and custom Matlab code. Functional images were slice-time corrected to the onset of the first slice and spatially realigned using a 6-parameter affine transformation. The registration procedure considered images from both Experiments 1 and 2. For each of the participants, high-resolution T 1 images were co-registered to the average functional image and segmented into gray matter, white matter and cerebrospinal fluid  (Ashburner & Friston, 2005). Diffeomorphic Anatomical Registration using Exponentiated Lie algebra (DARTEL,  Ashburner, 2007)  was used to register gray and white matter probability maps for the different individuals and derive a common gray-matter template. An affine transformation was finally estimated to normalize the DARTEL template to MNI space.
For both experiments, the first step of the pipeline for the analysis of the functional images involved fitting a GLM to the data for each participant and estimating the average BOLD response in each of the cells of the experimental design. To this purpose, the first-level GLMs included one conditionspecific regressor for each of the cells of the experimental design (12 and 9 condition-specific regressors for Experiment 1 and 2, respectively). Regressors for each of the experimental conditions were obtained by convolving a boxcar function modeling the presentation of the condition-specific sequences with the canonical hemodynamic response function (HRF). For both experiments, the GLM also included: (1) headmotion parameters estimated during the spatial realignment step; (2) an intercept term modeling activation during the implicit silent baseline condition; (3) a standard high-pass filter (cutoff ¼ 1/128 Hz).
For Experiment 2, the first level GLMs also included condition-specific parametric modulators modeling the effects of response correctness on the difference between trials from the same condition, and one additional condition-specific regressor for missing-answer trials. For this experiment, condition-specific activation levels were estimated by considering both correct-and incorrect-response trials (see e. g. , VanRullen, 2011; for a discussion of potential analysis biases associated with trial-selection procedures based on behavioral responses). Temporal dependencies between functional images from the same participant were accounted with a standard AR(1) autoregressive model. Firstlevel models were fit to unsmoothed native-space EPI data within a gray matter analysis mask [p(gray matter) !.