Developing Next-generation Brain Sensing Technologies - A Review.

Neural engineering Sensors

Journal

IEEE sensors journal
ISSN: 1530-437X
Titre abrégé: IEEE Sens J
Pays: United States
ID NLM: 101212357

Informations de publication

Date de publication:
2019
Historique:
entrez: 3 3 2020
pubmed: 1 1 2019
medline: 1 1 2019
Statut: ppublish

Résumé

Advances in sensing technology raise the possibility of creating neural interfaces that can more effectively restore or repair neural function and reveal fundamental properties of neural information processing. To realize the potential of these bioelectronic devices, it is necessary to understand the capabilities of emerging technologies and identify the best strategies to translate these technologies into products and therapies that will improve the lives of patients with neurological and other disorders. Here we discuss emerging technologies for sensing brain activity, anticipated challenges for translation, and perspectives for how to best transition these technologies from academic research labs to useful products for neuroscience researchers and human patients.

Identifiants

pubmed: 32116472
doi: 10.1109/jsen.2019.2931159
pmc: PMC7047830
mid: NIHMS1548448
doi:

Types de publication

Journal Article

Langues

eng

Subventions

Organisme : Intramural NIST DOC
ID : 9999-NIST
Pays : United States

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Auteurs

Jacob T Robinson (JT)

Department of Electrical and Computer Engineering, Rice University, Houston, TX 77005, USA.
Department of Bioengineering, Rice University, Houston, TX 77005, USA.
Department of Neuroscience, Baylor College of Medicine, Houston, TX 77030, USA.

Eric Pohlmeyer (E)

John Hopkins University Applied Physics Laboratory, Laurel, MD 20723, USA.

Malte C Gather (MC)

SUPA, School of Physics & Astronomy, University of St Andrews, St Andrews KY16 9SS Scotland, UK.

Caleb Kemere (C)

Department of Electrical and Computer Engineering, Rice University, Houston, TX 77005, USA.
Department of Bioengineering, Rice University, Houston, TX 77005, USA.

John E Kitching (JE)

Time and Frequency Division, NIST, 325 Broadway, Boulder, Colorado 80305, USA.

George G Malliaras (GG)

Electrical Engineering Division, Department of Engineering, University of Cambridge, Cambridge CB3 0FA, UK.

Adam Marblestone (A)

MIT Media Lab, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA.

Kenneth L Shepard (KL)

Department of Electrical Engineering, Columbia University, New York, NY 10027, USA.

Thomas Stieglitz (T)

Institute of Microsystem Technology, Laboratory for Biomedical Microtechnology, D-79110 Freiburg, Germany.
Cluster of Excellence BrainLinks-BrainTools, University of Freiburg, 79110 Freiburg, Germany.
Bernstein Center Freiburg, University of Freiburg, 79104 Freiburg, Germany.

Chong Xie (C)

Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712, USA.

Classifications MeSH