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.
This is a thriving time for brain-oriented research. Global efforts, involving academia, industry and governments are now undergoing to advance neurotechnologies and basic research, while some of these new technologies are reaching levels of maturity that may allow their standardization and commercialization in nearby times. Nevertheless, outstanding challenges need to be overcome to translate these findings into improved knowledge about how the brain works and, possible clinical and real-life applications. This publication is another effort of the IEEE Brain initiative on bringing the community together and fostering activities towards these goals.
We look forward to your active participation. We welcome contributed articles describing recent breakthroughs in research, primers on methods of interests, or report recent events such as conferences or workshops. Feel free to contact the editor for any question concerning the topic or content of your article.
Managing Editor:
Ricardo Chavarriaga, <ricardo.chavarriaga@epfl.ch>
Center for Neuroprosthetics, EPFL, Switzerland
See Author Guidelines & Submission Information
Managing Editor: Ricardo Chavarriaga, ricardo.chavarriaga@epfl.ch • @chavarriagaBCI
Center for Neuroprosthetics, EPFL, Switzerland
Ricardo Chavarriaga (IEEE member) is a senior researcher at the Center for Neuroprosthetics of the École Polytechnique Fédérale de Lausanne (EPFL), Switzerland. He holds a B.Sc. degree in Electronics Engineering from the Pontificia Universidad Javeriana Cali, Colombia and a PhD in computational neuroscience from EPFL. He co-chairs the IEEE SMC technical committee in BMI systems, and is part of the steering committee of the IEEE Brain Initiative.
His research focuses on robust brain-machine interfaces and multimodal human-machine interaction. Specifically, the decoding of cortical potentials that convey information about the user’s cognitive processes. Furthermore, He investigates on how the exploitation of such processes can be integrated with shared control principles and hybrid approaches for BMI control of complex devices.
