Low-Cost Assessment of User eXperience Through EEG Signals

EEG signals are an important tool for monitoring the brain activity of a person, but equipment, expertise and infrastructure are required. EEG technologies are generally expensive, thus few people are normally able to use them. However, some low-cost technologies are now available. One of these is OPENBCI, but it seems that it is yet to be widely employed in Human-Computer Interaction. In this study, we used OPENBCI technology to capture EEG signals linked to brain activity in ten subjects as they interacted with two video games: Candy Crush and Geometry Dash. The experiment aimed to capture the signals while the players interacted with the video games in several situations. The results show differences due to the absence/presence of sound; players appear to be more relaxed without sound. In addition, consistent analysis of the EEG data, meCue 2.0 and SAM data showed high consistency. The evidence demonstrates that interesting results are able to be gathered based on low-cost EEG (standard) signal-based technologies

CALIFA, the Calar Alto Legacy Integral Field Area survey: I. Survey presentation

We present here the Calar Alto Legacy Integral Field Area (CALIFA) survey, which has been designed to provide a first step in this direction.We summarize the survey goals and design, including sample selection and observational strategy.We also showcase the data taken during the first observing runs (June/July 2010) and outline the reduction pipeline, quality control schemes and general characteristics of the reduced data. This survey is obtaining spatially resolved spectroscopic information of a diameter selected sample of $\sim600$ galaxies in the Local Universe (0.005< z <0.03). CALIFA has been designed to allow the building of two-dimensional maps of the following quantities: (a) stellar populations: ages and metallicities; (b) ionized gas: distribution, excitation mechanism and chemical abundances; and (c) kinematic properties: both from stellar and ionized gas components. CALIFA uses the PPAK Integral Field Unit (IFU), with a hexagonal field-of-view of \sim1.3\sq\arcmin', with a 100% covering factor by adopting a three-pointing dithering scheme. The optical wavelength range is covered from 3700 to 7000 {\AA}, using two overlapping setups (V500 and V1200), with different resolutions: R\sim850 and R\sim1650, respectively. CALIFA is a legacy survey, intended for the community. The reduced data will be released, once the quality has been guaranteed. The analyzed data fulfill the expectations of the original observing proposal, on the basis of a set of quality checks and exploratory analysis. We conclude from this first look at the data that CALIFA will be an important resource for archaeological studies of galaxies in the Local Universe.Comment: 32 pages, 29 figures, Accepted for publishing in Astronomy and Astrophysic

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency–Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research

A novel virtual motor rehabilitation system for Guillain-Barré syndrome. Two single case studies

[EN] Introduction: This article is part of the Focus Theme of Methods of Information in Medicine on "New Methodologies for Patients Rehabilitation". Objectives: For Guillain-Barre patients, motor rehabilitation programs are helpful at the onset to prevent the complications of paralysis and in cases of persistent motor impairment. Traditional motor rehabilitation programs may be tedious and monotonous, resulting in low adherence to the treatments. A Virtual Motor Rehabilitation system has been tested in Guillain-Barre patients to increase patient adherence and to improve clinical results. Methods: Two people with Guillain-Barre performed 20 rehabilitation sessions. We tested a novel system based on Motor Virtual Rehabilitation in three periods of time (baseline evaluation, final evaluation, and follow-up. In the training program, the participants carried out a specific treatment using the Active Balance Rehabilitation system (ABAR). The system is composed of customizable virtual games to perform static and dynamic balance rehabilitation. Results: Significant improvements in clinical results were obtained by both participants, with significant results in the static balance clinical test of the Anterior Reach test in the standing position and unipedal stance time. Other significant results were found in dynamic balance clinical tests in the Berg Balance Scale test and the 30-second Sit-to-Stand test. With regard to acceptance of the system, both patients enjoyed the experience, and both patients thought that this system was helpful for their rehabilitation. Conclusions: The results show that Virtual Motor Rehabilitation for Guillain-Barre patients provides clinical improvements in an entertaining way.The authors would like to thank all of the clinical specialists and patients of San Jose Hospital for participating in the present study. We specially thank Carmen Aula-Valero for her time in the training program, and Jose-Antonio Lozano-Quilis and Hermenegildo Gil-Gomez for their suggestions in the design of the ABAR system. This contribution was partially funded by the Fundacion Antonio Gargallo ("Ayudas financiadas por la Obra Social de Ibercaja de proyectos de investigacion 2013", proyecto 2013/B001).Albiol-Perez, S.; Forcano-García, M.; Muñoz-Tomás, M.; Manzano-Fernández, P.; Solsona-Hernández, S.; Mashat, MA.; Gil-Gómez, J. (2015). A novel virtual motor rehabilitation system for Guillain-Barré syndrome. Two single case studies. Methods of Information in Medicine. 54(2):127-134. https://doi.org/10.3414/ME14-02-0002S127134542Asbury AK, Cornblath DR. Assessment of current diagnostic criteria for Guillain-Barré syndrome. Ann Neurol 1990; 27 Suppl: S21-24McGrogan, A., Madle, G. C., Seaman, H. E., & de Vries, C. S. (2008). The Epidemiology of Guillain-Barré Syndrome Worldwide. 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Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset