2017 – Issue 1

Welcome to BrainInsight

R. Chavarriaga

Welcome to the inaugural issue of BrainInsight, a quarterly online publication of the IEEE brain initiative. This is a space for the IEEE Brain community to share technical information and forward-looking commentary on brain-related research and technologies.

A Long Path Towards Restoring Locomotion After Spinal Cord Injury

RESEARCH
M. Capogrosso, T. Milekovic, G. Courtine

A century of research in spinal cord physiology has demonstrated that the circuits embedded in the lumbar spinal cord of mammals can autonomously produce repetitive patterns of motor activity resembling locomotion [1]. After a spinal cord injury (SCI), however, the neural pathways carrying information between the brain and these spinal circuits, usually located below the injury, are partly or completely interrupted. While the lumbar circuits are intact, this interruption disrupts or abolishes volitional leg movements.

Next Generation Neural Interfaces: Research on Emerging Technologies at Imperial College London

RESEARCH
D. Y. Barsakcioglu, S. Luan, L. Grand, T. G. Constandinou

The era of bioelectronic healthcare is dawning upon us. As electronic systems shrink in size and improve in functionality, we see more and more emerging devices that can track vital signs, such as heart rate and blood pressure, realising the grand vision of highly connected sensor nodes monitoring patients’ health beyond the hospital doors. The real revolution in digital healthcare, however, lies in bringing not only the diagnostics but also the therapy to the patient which requires interfacing the world of electronics with biology.

Network Data on the Statistical Testbench

A New Method for Generating Realistic Null Data Exploiting Underlying Graph Structure with Application to EEG

METHODS
E. Pirondini, A. Vybornova, M. Coscia, and D. Van De Ville

Technological and computational advances are making available large amounts of high-dimensional and rich-structured biomedical data, including brain images and signals. Acknowledging the network structure in our analyses opens a multitude of avenues in investigating “systems level” properties. For instance, computational neuroscience has boosted the interest in modeling and analyzing large datasets using concepts normally applied in networks and graph theories.

On the Need of Standards for Brain-Machine Interface Systems

OPINION
R. Chavarriaga, C. Carey, C. Tom, B. Ash

The field of Brain-Machine Interfacing (BMI) is going through a very exciting period where the state-of- the-art in research is currently being tested on its intended end-users. Evidently, this translation from laboratory proof-of concepts to viable clinical and assistive solutions entails a large set of challenges. Furthermore, the possibility of deploying and commercializing BMI-based solutions requires researchers, manufacturers, and regulatory agencies to ensure these devices comply with well-defined criteria on their safety and effectiveness. In consequence, there is an increased interest on development of appropriate standards for BMI systems.

About BrainInsight

BrainInsight, the IEEE Brain Initiative eNewsletter, is a quarterly online publication, featuring practical and timely information and forward-looking commentary on neurotechnologies. BrainInsight describes recent breakthroughs in research, primers on methods of interests, or report recent events such as conferences or workshops.


Managing Editor

Ricardo Chavarriaga
Center for Neuroprosthetics, EPFL, Switzerland
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