Mark R. Costa

fNIRS: A new modality for brain activity-based biometric authentication

There is a rapidly increasing amount of research on the use of brain activity patterns as a basis for biometric user verification. The vast majority of this research is based on Electroencephalogram (EEG), a technology which measures the electrical activity along the scalp. In this paper, we evaluate Functional Near-Infrared Spectroscopy (fNIRS) as an alternative approach to brain activity-based user authentication. fNIRS is centered around the measurement of light absorbed by blood and, compared to EEG, has a higher signal-to-noise ratio, is more suited for use during normal working conditions, and has a much higher spatial resolution which enables targeted measurements of specific brain regions. Based on a dataset of 50 users that was analysed using an SVM and a Naïve Bayes classifier, we show fNIRS to respectively give EERs of 0.036 and 0.046 when using our best channel configuration. Further, we present some results on the areas of the brain which demonstrated highest discriminative power. Our findings indicate that fNIRS has significant promise as a biometric authentication modality.

Embodiment and Embodied Cognition

Progressive embodiment and the subsequent enhancement of presence have been important goals of VR researchers and designers for some time (Biocca, 1997). Consequently, researchers frequently explore the relationship between increasing embodiment and presence yet rarely emphasize the ties between their work and other work on embodiment. More specifically, we argue that experiments manipulating or implementing visual scale, avatar customization, sensory enrichment, and haptic feedback, to name a few examples, all have embodiment as their independent variable. However, very few studies explicitly frame their work as an exploration of embodiment. In this paper we will leverage the field of Embodied Cognition to help clarify the concept of embodiment.

Emergence of collaboration networks around large scale data repositories: a study of the genomics community using GenBank

The advent of large data repositories and the necessity of distributed skillsets have led to a need to study the scientific collaboration network emerging around cyber-infrastructure-enabled repositories. To explore the impact of scientific collaboration and large-scale repositories in the field of genomics, we analyze coauthorship patterns in NCBIs big data repository GenBank using trace metadata from coauthorship of traditional publications and coauthorship of datasets. We demonstrate that using complex network analysis to explore both networks independently and jointly provides a much richer description of the community, and addresses some of the methodological concerns discussed in previous literature regarding the use of coauthorship data to study scientific collaboration.

Measuring Situational Awareness Aptitude Using Functional Near-Infrared Spectroscopy

Attempts have been made to evaluate people’s situational awareness (SA) in military and civilian contexts through subjective surveys, speed, and accuracy data acquired during SA target tasks. However, it is recognized in the SA domain that more systematic measurement is necessary to assess SA theories and applications. Recent advances in biomedical engineering have enabled relatively new ways to measure cognitive and physiological state changes, such as with functional near-infrared spectroscopy (fNIRS). In this paper, we provide a literature review relating to SA and fNIRS and present an experiment conducted with an fNIRS device comparing differences in the brains between people with high and low SA aptitude. Our results suggest statistically significant differences in brain activity between the high SA group and low SA group.

Research networks in data repositories

This paper reports our ongoing work investigating the structural features of scientific collaboration based on metadata collected from a scientific data repository (SDR). The background literature is reviewed in supporting our claim that metadata collected from SDRs offer a complimentary data source to traditional publication metadata collected from digital libraries. Methodological considerations are discussed in association with using metadata from SDRs, including author name disambiguation and data parsing. Initial findings show that the network has some unique macro-level structural features while also in agreement with existing networks theories. Challenges due to inconsistent metadata quality control procedures are also discussed in an attempt to reinforce claims that metadata should be designed to support both domain specific retrieval and evaluation and assessment needs.

Big data, big metadata and quantitative study of science: A workflow model for big scientometrics: Big Data, Big Metadata and Quantitative Study of Science: A Workflow Model for Big Scientometrics

Large cyberinfrastructure‐enabled data repositories generate massive amounts of metadata, enabling big data analytics to leverage on the intersection of technological and methodological advances in data science for the quantitative study of science. This paper introduces a definition of big metadata in the context of scientific data repositories and discusses the challenges in big metadata analytics due to the messiness, lack of structures suitable for analytics and heterogeneity in such big metadata. A methodological framework is proposed, which contains conceptual and computational workflows intercepting through collaborative documentation. The workflow‐based methodological framework promotes transparency and contributes to research reproducibility. The paper also describes the experience and lessons learned from a four‐year big metadata project involving all aspects of the workflow‐based methodologies. The methodological framework presented in this paper is a timely contribution to the field of scientometrics and the science of science and policy as the potential value of big metadata is drawing more attention from research and policy maker communities.

The dynamics of social capital in scientific collaboration networks

Scientific collaboration is considered a cornerstone of 21st Century Science and a springboard for economic prosperity. At a more basic level, it is also considered to be a fundamental part of the development of scientific human capital. From that perspective, scientific collaboration is facilitated through social capital, which is acquired through an investment cycle. Through a series of collaborative interactions, scientists move to positions within collaboration networks, which in turn creates future opportunities for collaboration. Prior research has either focused on the positioning of scientists within their respective networks, or scientists’ network activity and career stage, but few studies have looked at the dynamics of positioning due to the presence, timing, and sequencing of collaborative interactions on future opportunities. The reason for this gap was the intractability of the problem. In this paper, a research proposal is outlined that will attempt to address this deficiency by employing machine learning to identify patterns in the career trajectories of scientists in a research community.

Using the EEG Error Potential to Identify Interface Design Flaws

There are a number of limitations to existing usability testing methods, including surveys, interviews, talk-alouds, and participant observations. These limitations include subject bias, poor recall, and inability to capture fleeting events, such as when a UI functions or behaves in a manner that contradicts user expectations. One possible solution to these problems is to use electrophysiological indicators to monitor user interaction with the UI. We propose using event related potentials (ERP), and the error potential (ErrP) more specifically, to capture moment-to-moment interactions that lead to violations in user expectations. An ERP is a response generated in the brain to stimuli, while the ErrP is a more specific signal shown to be elicited by subject error. In this experiment we monitored subjects using a 10-channel electroencephalogram (EEG) as they completed a range of simple web browsing tasks. However, roughly 1/3 of the time subjects were confronted with poor UI design features (e.g., broken links). We then used statistical and machine learning techniques to classify the data and found that we were able to accurately identify the presence of error potentials. Furthermore, the ErrP was present when the subjects encountered a UI design flaw, but only during the more ‘overt’ examples of our design flaws. Results support our hypothesis that ERPs and ErrPs, can be used to identify UI design flaws for a variety of systems, from web sites to video games.

Impact factor inflation: measuring the gatekeeper effect in scientific literature

This research seeks to measure the prevalence of Impact Factor inflation in two areas of science by comparing preprints archived in Arxiv.org to their respective versions published in peer-reviewed journals.

xR-Based Systems for Mindfulness Based Training in Clinical Settings

Chronic and acute stress are persistent and troubling health concerns for many people and military veterans in particular. Clinicians are increasingly turning to mindfulness techniques to provide people with the skills they need to self-manage that stress. However, training and getting people to adhere to the practice is difficult. In this paper, we talk about a virtual reality based system designed specifically to help veterans learn mindfulness-based stress reduction techniques.