Signal Generation, Acquisition, and Processing in Brain Machine Interfaces: A Unified Review

Brain machine interfaces (BMIs), or brain computer interfaces (BCIs), are devices that act as a medium for communications between the brain and the computer. It is an emerging field with numerous applications in domains of prosthetic devices, robotics, communication technology, gaming, education, an...

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Bibliographic Details
Published inFrontiers in neuroscience Vol. 15; p. 728178
Main Authors Salahuddin, Usman, Gao, Pu-Xian
Format Journal Article
LanguageEnglish
Published Switzerland Frontiers Research Foundation 13.09.2021
Frontiers Media S.A
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Summary:Brain machine interfaces (BMIs), or brain computer interfaces (BCIs), are devices that act as a medium for communications between the brain and the computer. It is an emerging field with numerous applications in domains of prosthetic devices, robotics, communication technology, gaming, education, and security. It is noted in such a multidisciplinary field, many reviews have surveyed on various focused subfields of interest, such as neural signaling, microelectrode fabrication, and signal classification algorithms. A unified review is lacking to cover and link all the relevant areas in this field. Herein, this review intends to connect on the relevant areas that circumscribe BMIs to present a unified script that may help enhance our understanding of BMIs. Specifically, this article discusses signal generation within the cortex, signal acquisition using invasive, non-invasive, or hybrid techniques, and the signal processing domain. The latest development is surveyed in this field, particularly in the last decade, with discussions regarding the challenges and possible solutions to allow swift disruption of BMI products in the commercial market.
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Edited by: Hang Su, Fondazione Politecnico di Milano, Italy
This article was submitted to Neuroprosthetics, a section of the journal Frontiers in Neuroscience
Reviewed by: Kevin J. Otto, University of Florida, United States; Gert Pfurtscheller, Graz University of Technology, Austria; Longbin Zhang, Royal Institute of Technology, Sweden
ISSN:1662-4548
1662-453X
1662-453X
DOI:10.3389/fnins.2021.728178