Miracle-dz/UniFAR-FTUs huggingface dataset:
Introducing a Motivating Training Study Design to Compare auditory and tactile Streaming-based P300 BCIs

: Since neurodegenerative diseases or brain injuries causing locked-in-syndrome (LIS) might lead to loss of vision, different approaches of vision-independent BCIs were developed, for example P300 BCIs using auditory or tactile stimuli. However, BCI-inefficiency has been reported in these approaches as well, with high workload proposed as an underlying problem. In this contribution, a motivating training study design is introduced to compare auditory with tactile P300 BCIs using a streaming-based approach of stimulus presentation that was proposed as a relatively low-workload alternative to classic approaches of sequential stimulus presentation. First performance results of N = 6 healthy participants were examined case-wise – all participants were able to use at least one BCI version successfully. The preliminary results indicate high motivation and show that there is no superior modality per se but individual preferences in stimulus modalities. This should be considered for future research with healthy as well as LIS users.
"When does it work ?" : An exploratory analysis of Transfer Learning for BCI

: Transfer Learning is a critical topic of research in the BCI ﬁeld. Its goal is to reuse data gathered in a previous session ( source session) in order to reduce, or completely bypass, calibration in a new session ( target session). Although many methods have been proposed to tackle this problem, little is known about what characteristics of the datasets should be taken into account in order to ensure good performance. In this paper, we perform an exploratory analysis to study the inﬂuence of some simple descriptors of the source and target datasets over the classiﬁcation scores obtained with Transfer Learning. We observe that the discriminability of the data points in the target session plays an important role in determining how well the Transfer Learning will work, as opposed to that of the source session, which has no statistically signiﬁ-cant role in most cases.
Movement Related cortical potentials during Hand Opening and Closing in als patients

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease, which leads to the progressive loss of muscle control. In Denmark, approximately 150 people are diagnosed with ALS each year. The ensuing burden on related health care costs increases substantially as patients lose the ability to perform tasks of everyday living. Brain-computer interface (BCI) systems provide an approach to allow these patients some interaction with their environment. In this ongoing study we investigate whether the movement-related cortical potential (MRCP) extracted from electroencephalography (EEG) signals can be used for BCI control of an assistive glove in ALS patients. To this aim, the BCI needs to detect the intention of the subject to open and close the hand. The MRCP is a slow negative drift that commences 2 seconds prior to movement onset and contains features that differ between different types of movement, making it an ideal signal modality for multi-dimensional BCI control. Preliminary analysis from three ALS patients reveal a classification accuracy above 85% for the best channel between movement and rest conditions. However, classification accuracy between movement types (hand opening/closing), was lower (>65% for the best channel). Combining MRCP detection, with methods that allow using a single input (brain switch) to select multiple commands, may thus be a viable solution for this patient group.
Intracortical Activity decoding of motor imagery based on Deep Convolutional Neural Network: a Pilot Study

An intracortical activity decoding algorithm based on convolutional neural network (CNN) is proposed for classifying the primate intracortical activity of motor imagery under a series of movement tasks. An intracortical brain-computer interface allows the subjects to continuously drive a mobile robot using its brain activity from the motor cortex. Under the leave-one-day-out cross validation, the CNN-based algorithm achieves reliable classification performance in all the four movement tasks, including, Forward, Left, Right, and Stop. Experimental results indicate that the Cohen’s kappa of the two subjects are both above 0.4. Additionally, the comparison shows that the CNN-based method is significantly better than the linear discriminant analysis (LDA) which is a state-of-the-art decoding approach.
Training effects of a tactile BCI for wheelchair control

The tactile modality for brain-computer interfaces (BCIs) is rarely used because of its limitations in speed and accuracy. However, non-visual BCI paradigms are of great interest as potentially feasible options for patients with limited gaze control. For the present study, 5 participants navigated a virtual wheelchair through a 3D apartment using a 4-class tactile BCI in 5 identical sessions. Mean P300 amplitude, mean difference between target and non-target (MD), and information transfer rate (ITR) were calculated for all sessions. Descriptively, amplitudes at Fz increased with training, MD or other electrode positions showed no obvious changes. Mean ITR during the fifth session was more than twice as high (8.7 bits/min) as compared to the first (4.0 bits/min). Our preliminary results suggest that a previous study by Herweg and colleagues [1] can be replicated with respect to a training effect. However, we could not yet achieve ITRs as high as in [1]. We are currently increasing our sample size to 15 participants.
An expectation-based EEG marker for the selection of moving objects with gaze

: The use of an EEG expectation-related component, the expectancy wave (E-wave), in brain-machine interaction was proposed more than 50 years ago, but active exploration of this possibility has started only recently, in the context of developing passive brain-computer interfaces for the enhancement of gaze interaction. We report, for the first time, the results of a systematic experimental study that revealed an EEG marker for selecting intentionally an object among other moving objects using smooth pursuit eye movements. This marker appeared to have the same nature as the E-wave previously observed in the EEG accompanying the selection of static objects with gaze fixations. A convolutional neural network classified the intentional and spontaneous smooth pursuit eye movements with average ROC AUC 0.69±0.13 (M±SD). These results suggest that the E-wave might be robust enough to serve, after further improvement of the methodology, as the basis of hybrid eye-brain-computer interfaces applied for selection in dynamically changing visual environments.
Transfer Learning for Biomedical Named Entity Recognition with BioBERT

We apply a transfer learning approach to biomedical named entity recognition and compare it with traditional approaches (dictionary, CRF, BiLTSM). Specifically, we build models for adverse drug reaction recognition on three datasets. We tune a pre-trained transformer model, BioBERT, on these datasets and observe the absolute F1-score improvements of 6.93, 10.46 and 13.31. This shows that, with a relatively small amount of annotated data, transfer learning can help in specialized information extraction tasks.
Generating "Who Wants to Be a Millionaire?" Questions Sets Automatically from Wikidata

Quiz shows and apps have enjoyed great popularity in recent years, which increases the demand for fresh question sets. We investigate how to derive such sets automatically from the Wikidata knowledge graph. Utilizing the graph, node connectivity and diversity, we propose measures to identify appealing wrong answers and rate the difficulty of a question, which is a prerequisite to compose a full question set. First results align quite well with human perception of the questions.
The effect of Performance Expectancy and Achievement Motive in a P300 based brain-Computer Interface

We investigated the effect of performance expectancy and achievement motive on P300 BCI performance. Thirty-eight participants were separated in two groups according to their achievement motive and classified as either avoiding failure (AF) or approaching success (AS). Participants were presented with three different matrices in the colors red, green and blue and were told that spelling would be difficult, moderately difficult or easy depending on the color. We hypothesized AS participants to perform best and to show highest P300 amplitudes in a spelling condition perceived to be moderately difficult. AF participants were hypothesized to perform worst and to show lowest P300 amplitudes in the spelling condition perceived to be moderately difficult. Participants spelled six fiveletter words in each perceived difficulty condition. We found highest P300 amplitudes in the easy condition irrespective of achievement motive; however, no differences concerning performance in percent correct responses. Even though we could not find an effect of performance expectancy on the behavioral level, we did show that performance expectancy does affect BCI performance on the physiological level.
CODEC - Detecting Linear Correlations in Dense Clusters using coMAD-based PCA

The coMAD (co-median absolute deviation) is a measure for the joint median of two random variables. Previous experiments have shown that a coMAD-based PCA is more robust towards noise and outliers, yielding eigenvectors which represent linear correlation better than its covariance-based competitors. In this preliminary work we introduce CODEC COrrelations in DEnse Clusters a method for detecting linear correlations in dense clusters utilizing a coMAD-based PCA. The idea of CODEC is intriguingly simple: first a density-based clustering is performed using the well established clustering method DBSCAN. Then on each of the clusters PCA is performed. Instead of using the covariance matrix we use the coMAD matrix as a basis for performing PCA.
Information Extraction for Semi-structured Email Corpora

Information extraction is a requirement for enhanced IR techniques. To surpass rigid extraction rules in wrappers based on XPath or CSS selectors, we present a new extraction method extending the FleXPath method that was used for structured XML retrieval in INEX. We expand this method to work with semi-structured HTML and present a case study and a short evaluation based on a corpus of emails from scientific publishers.
Semi-automatic Semantic Enrichment of Personal Data Streams

Current information technologies allow people to acquire personal data related to their health, lifestyle, behavior, and activities, often using wearable and mobile devices. Personal data management technologies have emerged recently, in order to cope with the requirements of this type of data, ranging from personal clouds to self-storage solutions. Pryv.io is a comprehensive solution for managing this particularly sensible type of data streams, focusing both on data privacy and decentralization. In this paper, we describe SemPryv, a system aiming at providing a semantization mechanism for enriching personal data streams with standardized specialized vocabularies from third-party providers. It relies on third providers of semantic concepts, and includes rule-based mechanisms for facilitating the semantization process. A full implementation of SemPryv has been produced, pluggable to the existing Pryv.io platform, showing the feasibility of the approach.
Using Word Embeddings for Recommending Datasets based on Scientific Publications

In scholarly search systems, computing recommendations of the same type, for example, additional publications when reading a particular publication, is a well-approached problem. However, suggesting items from another type, e.g., research data when reading a publication, is rarely covered in scholarly recommendations. In this position paper, we employ word embeddings to approach the problem of such cross-domain recommendations in scientific search systems, more specifically, recommending research data based on publications. Besides various metadata, publication and research dataset entries comprise textual metadata (e.g. title, abstract), which allows to detect similar entries using word embeddings. We illustrate first results, major problems and possible solutions when using word embeddings for recommending datasets based on publications.
Juggling first year student experience and institutional change: An Australian experience

First year students experience a range of tensions as they negotiate their new roles as university students, particularly in their first six months of study. This paper will describe how one Australian, research intensive, University engaged in a quality change process through the development and implementation of a strategic teaching and learning project. The overarching aim of the project was to implement specific, student-centred initiatives to create a learning environment which would improve first year students' experiences. Vignettes of Faculty-based initiatives will be used to illustrate how first year students' issues were addressed to improve the overall student experience.
RatVec: A General Approach for Low-dimensional Distributed Vector Representations via Rational Kernels

We present a general framework, RatVec, for learning vector representations of non-numeric entities based on domain-specific similarity functions interpreted as rational kernels. We show competitive performance using k -nearest neighbors in the protein family classification task and in Dutch spelling correction. To promote re-usability and extensibility, we have made our code and pre-trained models available at https://github.com/ratvec.
Clustering Trend Data Time-Series through Segmentation of FFT-decomposed Signal Constituents

When we are given trend data for different keywords, scientists may want to cluster them in order to detect specific terms which exhibit a similar trending. For this purpose the periodic regression on each of the time-series can be performed. We ask in this work: What if we not simply cluster the regression models of each time-series, but the periodic signal constituents? The impact of such an approach is twofold: first we would see at a regression level how similar or dissimilar two timeseries are regarding their periodic models, and secondly we would be able to see similarities based on single signal constituents between different time-series, containing the semantic that although time-series may be different on a regression level, they may be similar on an constituent level, reflecting other periodic influences. The results of this approach reveal commonalities between time series on a constituent level that are not visible in first place, by looking at their plain regression models.
Presentation speeds for a N400-based BCI

: The N400 is an ERP sensitive to the semantic content of a stimulus, in relation to the active mental context of a subject. By repeatedly presenting stimuli (i.e., probe words), we can thus infer information about this context through decoding of this relatedness response. This can form the basis of a, so called, semantic Brain Computer Interface, allowing us to directly identify the concept on a users mind without spelling out the letters. The usability of such a BCI depends on how much information can be extracted from a single presentation, but also on how fast stimuli can be presented. Here we report results from a pilot study of an experimental design that aims to determine the effect of the time between such probes on the amplitude of this N400. The preliminary results show that an N400 can still be detected across subjects, even with stimuli presented at nearly twice the rate (1.7x) used in one of our previous studies.
Can suggestive hypnosis be used to improve the BCI performance ?

: Improving the user performance is one of the major issues in the Brain-Computer Interface (BCI) ﬁeld. In this study, the following question is asked: can hypnosis enhance the performance of motor imagery based BCI? In-deed, hypnotic inductions using Ericksonian suggestions can make the user feel simultaneously more relaxed and more focused on the mental task, and therefore could be used to increase performance in BCI. The aim of this study was to evaluate the impact of an hypnotic condition on the ERD/ERS patterns during a kinesthetic motor imagery (KMI) task. To investigate this issue, 19 right-handed healthy subjects performed a KMI of the right hand during two randomized sessions: in normal and hypnotic conditions. We analyzed the event-related (De)-synchronization (ERD/ERS) over the motor cortex and compared the BCI accuracy during both conditions. Our results suggest that the state of hypnosis reduces the ERD phase in the sensorimotor bands, assuming a change in the activation of the motor cortex during the hypno-tized state and thus a worst detection of the kinesthetic motor imagery.
Different aspects regarding the surveillance of surgical site infections

• A submitted manuscript is the version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website. • The final author version and the galley proof are versions of the publication after peer review. • The final published version features the final layout of the paper including the volume, issue and page numbers.
Vegetable intake in Australian children and adolescents: the importance of consumption frequency, eating occasion and its association with dietary and sociodemographic factors

Abstract Objective: We aimed to profile vegetable consumption and its association with dietary and sociodemographic factors. Design: Secondary analysis of a nationally representative nutrition survey. ‘Vegetables’ refers to non-discretionary ‘vegetables and legumes/beans’ as defined by the Australian Dietary Guidelines (ADG). Prevalence of vegetable consumption, frequency of intake, proportion meeting ADG recommendations, most popular food groups, intake at each reported eating occasion, and the profile of high and low vegetable consumers (based on the median servings) were determined. Setting: Australian 2011–2012 National Nutrition and Physical Activity Survey. Participants: Children and adolescents aged 2 to 18 years (n 2812). Results: Vegetables were consumed by 83·0% (95% CI 81·6, 84·4%) of participants, but the median vegetable servings was less than a third of the ADG recommendations. ‘Leaf and stalk vegetables’ and ‘potatoes’ were the most popular vegetable-dense food groups at lunch and dinner, respectively. Sixty-four percent had vegetables once a day, and predominantly at dinner. Vegetable frequency was positively associated with daily vegetable servings and variety. Participants who consumed vegetables twice a day generally had vegetables at both lunch and dinner and had nearly double the servings (2·6, sd 1·9) of those who consumed them once (1·5, sd 1·5). High vegetable consumers were older, had higher total energy, but lower discretionary energy intake and were less likely to be at risk of metabolic complications. Conclusion: Increasing the frequency of vegetable consumption may assist with increasing daily vegetable servings. A focus on consuming vegetables at lunch may assist with increasing both total servings and variety.
Brain-derived neurotrophic factor stimulation of T-type Ca2+ channels in sensory neurons contributes to increased peripheral pain sensitivity

A cross-pathway kinase cascade involving T-type Ca2+ channels may underlie some forms of neuralgia. Treating neuralgias The trigeminal ganglion (TG) is a cluster of neurons near the temporal bone that provides nociceptive sensation in the head and is associated with various pain disorders, including migraine and trigeminal neuralgia. Increased expression of the neurotrophic factor BDNF and its receptor TrkB is implicated in the heightened sensitivity of the TG. Wang et al. uncovered a cross-pathway kinase cascade through which BDNF-TrkB sensitizes the TG to mechanical and inflammatory stimuli. The pathway triggered the opening of a subtype of Ca2+ channels, and the consequent pain-associated behavior in rats was diminished by intra-TG injection with respective channel blockers or kinase inhibitors. These findings may be relevant to patients that suffer from TG-associated neuralgias and may lead to the development of effective therapies. Although brain-derived neurotrophic factor (BDNF) is implicated in the nociceptive signaling of peripheral sensory neurons, the underlying mechanisms remain largely unknown. Here, we elucidated the effects of BDNF on the neuronal excitability of trigeminal ganglion (TG) neurons and the pain sensitivity of rats mediated by T-type Ca2+ channels. BDNF reversibly and dose-dependently enhanced T-type channel currents through the activation of tropomyosin receptor kinase B (TrkB). Antagonism of phosphatidylinositol 3-kinase (PI3K) but not of its downstream target, the kinase AKT, abolished the BDNF-induced T-type channel response. BDNF application activated p38 mitogen-activated protein kinase (MAPK), and this effect was prevented by inhibition of PI3K but not of protein kinase A (PKA). Antagonism of either PI3K or p38 MAPK prevented the BDNF-induced stimulation of PKA activity, whereas PKA inhibition blocked the BDNF-mediated increase in T-type currents. BDNF increased the rate of action potential firing in TG neurons and enhanced the pain sensitivity of rats to mechanical stimuli. Moreover, inhibition of TrkB signaling abolished the increased mechanical sensitivity in a rat model of chronic inflammatory pain, and this effect was attenuated by either T-type channel blockade or knockdown of the channel Cav3.2. Together, our findings indicate that BDNF enhances T-type currents through the stimulation of TrkB coupled to PI3K-p38-PKA signaling, thereby inducing neuronal hyperexcitability of TG neurons and pain hypersensitivity in rats.
Caring in nursing classics : an essential resource

This is a core resource for nursing educators and students at all levels who seek fundamental information on the art and science of caring. The text is comprised of 36 classic book chapters and journal articles written by leaders in the caring arena. Co-published with the Watson Science Caring Institute, it will also be a primary resource for students attending WSCI programs and for in-service education programs, especially in hospitals with or seeking Magnet status. The text includes a digital teacher's guide with power points for key concepts, multiple choice questions for each chapter, and covers a variety of nursing settings with corresponding reflective questions. Questions range from simple to complex, and relate to the requirements of students at different academic levels. The book is organised into nine topics which address the evolution of caring scholarship, systematic reviews of the concept of caring, theoretical perspectives, seminal research studies, research designs and methods, practice models and theoretical frameworks for the integration of caring within contemporary hospital-based practice environments, caring within a community health environment, leadership and administrative issues, and the future of caring science. Key Features: Presents the seminal literature on caring Co-published with WSCI Structured thematically, with section introductions and summations Provides reflective/critical thinking questions per chapter and per academic level For use in baccalaureate, graduate, doctoral level, and in-service education and as a core resource for WSCI programs Includes digital teacher's guide