Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

BrainNet: A Multi-Person Brain-to-Brain Interface for Direct Collaboration Between Brains

Linxing Jiang, Andrea Stocco, Darby M. Losey, Justin A. Abernethy, Chantel S. Prat, Rajesh P. N. Rao
doi: https://doi.org/10.1101/425066
Linxing Jiang
1University of Washington, Paul G. Allen School of Computer Science & Engineering, Seattle, WA 98195, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Andrea Stocco
2University of Washington, Department of Psychology, Seattle, WA 98195, USA
3University of Washington, Institute for Learning and Brain Sciences, Seattle, WA 98195, USA
4University of Washington, Center for Neurotechnology, Seattle, WA 98195, USA
5University of Washington, Institute for Neuroengineering, Seattle, WA 98195, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Darby M. Losey
6Carnegie Mellon University, Program in Neural Computation, Pittsburgh, PA 15213, USA
7Carnegie Mellon University, Center for the Neural Basis of Cognition, Pittsburgh, PA 15213, USA
8Carnegie Mellon University, Machine Learning Department, Pittsburgh, PA 15213, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Justin A. Abernethy
2University of Washington, Department of Psychology, Seattle, WA 98195, USA
3University of Washington, Institute for Learning and Brain Sciences, Seattle, WA 98195, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Chantel S. Prat
2University of Washington, Department of Psychology, Seattle, WA 98195, USA
3University of Washington, Institute for Learning and Brain Sciences, Seattle, WA 98195, USA
4University of Washington, Center for Neurotechnology, Seattle, WA 98195, USA
5University of Washington, Institute for Neuroengineering, Seattle, WA 98195, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Rajesh P. N. Rao
1University of Washington, Paul G. Allen School of Computer Science & Engineering, Seattle, WA 98195, USA
4University of Washington, Center for Neurotechnology, Seattle, WA 98195, USA
5University of Washington, Institute for Neuroengineering, Seattle, WA 98195, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Preview PDF
Loading

ABSTRACT

We present BrainNet which, to our knowledge, is the first multi-person non-invasive direct brain-to-brain interface for collaborative problem solving. The interface combines electroencephalography (EEG) to record brain signals and transcranial magnetic stimulation (TMS) to deliver information noninvasively to the brain. The interface allows three human subjects to collaborate and solve a task using direct brain-to-brain communication. Two of the three subjects are designated as “Senders” whose brain signals are decoded using real-time EEG data analysis. The decoding process extracts each Sender’s decision about whether to rotate a block in a Tetris-like game before it is dropped to fill a line. The Senders’ decisions are transmitted via the Internet to the brain of a third subject, the “Receiver,” who cannot see the game screen. The Senders’ decisions are delivered to the Receiver’s brain via magnetic stimulation of the occipital cortex. The Receiver integrates the information received from the two Senders and makes a decision using an EEG interface about either turning the block or keeping it in the same position. A second round of the game provides an additional chance for the Senders to evaluate the Receiver’s decision and send feedback to the Receiver’s brain, and for the Receiver to rectify a possible incorrect decision made in the first round. We evaluated the performance of BrainNet in terms of (1) Group-level performance during the game; (2) True/False positive rates of subjects’ decisions; (3) Mutual information between subjects. Five groups, each with three human subjects, successfully used BrainNet to perform the Tetris task, with an average accuracy of 81.25%. Furthermore, by varying the information reliability of the Senders by artificially injecting noise into one Sender’s signal, we investigated how the Receiver learns to integrate noisy signals in order to make a correct decision. We found that Receivers are able to learn which Sender is more reliable based solely on the information transmitted to their brains. Our results raise the possibility of future brain-to-brain interfaces that enable cooperative problem solving by humans using a “social network” of connected brains.

Footnotes

  • ↵* prestonj{at}cs.washington.edu

  • ↵+ rao{at}cs.washington.edu

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
Back to top
PreviousNext
Posted September 26, 2018.
Download PDF
Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
BrainNet: A Multi-Person Brain-to-Brain Interface for Direct Collaboration Between Brains
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
BrainNet: A Multi-Person Brain-to-Brain Interface for Direct Collaboration Between Brains
Linxing Jiang, Andrea Stocco, Darby M. Losey, Justin A. Abernethy, Chantel S. Prat, Rajesh P. N. Rao
bioRxiv 425066; doi: https://doi.org/10.1101/425066
Digg logo Reddit logo Twitter logo Facebook logo Google logo LinkedIn logo Mendeley logo
Citation Tools
BrainNet: A Multi-Person Brain-to-Brain Interface for Direct Collaboration Between Brains
Linxing Jiang, Andrea Stocco, Darby M. Losey, Justin A. Abernethy, Chantel S. Prat, Rajesh P. N. Rao
bioRxiv 425066; doi: https://doi.org/10.1101/425066

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Bioengineering
Subject Areas
All Articles
  • Animal Behavior and Cognition (3477)
  • Biochemistry (7316)
  • Bioengineering (5294)
  • Bioinformatics (20189)
  • Biophysics (9972)
  • Cancer Biology (7698)
  • Cell Biology (11243)
  • Clinical Trials (138)
  • Developmental Biology (6416)
  • Ecology (9912)
  • Epidemiology (2065)
  • Evolutionary Biology (13271)
  • Genetics (9347)
  • Genomics (12544)
  • Immunology (7667)
  • Microbiology (18928)
  • Molecular Biology (7415)
  • Neuroscience (40870)
  • Paleontology (298)
  • Pathology (1226)
  • Pharmacology and Toxicology (2125)
  • Physiology (3138)
  • Plant Biology (6836)
  • Scientific Communication and Education (1268)
  • Synthetic Biology (1891)
  • Systems Biology (5295)
  • Zoology (1083)