Cargando…

Optimizing Computer–Brain Interface Parameters for Non-invasive Brain-to-Brain Interface

A non-invasive, brain-to-brain interface (BBI) requires precision neuromodulation and high temporal resolution as well as portability to increase accessibility. A BBI is a combination of the brain–computer interface (BCI) and the computer–brain interface (CBI). The optimization of BCI parameters has...

Descripción completa

Detalles Bibliográficos
Autores principales: LaRocco, John, Paeng, Dong-Guk
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7020695/
https://www.ncbi.nlm.nih.gov/pubmed/32116625
http://dx.doi.org/10.3389/fninf.2020.00001
_version_ 1783497792502628352
author LaRocco, John
Paeng, Dong-Guk
author_facet LaRocco, John
Paeng, Dong-Guk
author_sort LaRocco, John
collection PubMed
description A non-invasive, brain-to-brain interface (BBI) requires precision neuromodulation and high temporal resolution as well as portability to increase accessibility. A BBI is a combination of the brain–computer interface (BCI) and the computer–brain interface (CBI). The optimization of BCI parameters has been extensively researched, but CBI has not. Parameters taken from the BCI and CBI literature were used to simulate a two-class medical monitoring BBI system under a wide range of conditions. BBI function was assessed using the information transfer rate (ITR), measured in bits per trial and bits per minute. The BBI ITR was a function of classifier accuracy, window update rate, system latency, stimulation failure rate (SFR), and timeout threshold. The BCI parameters, including window length, update rate, and classifier accuracy, were kept constant to investigate the effects of varying the CBI parameters, including system latency, SFR, and timeout threshold. Based on passively monitoring BCI parameters, a base ITR of 1 bit/trial was used. The optimal latency was found to be 100 ms or less, with a threshold no more than twice its value. With the optimal latency and timeout parameters, the system was able to maintain near-maximum efficiency, even with a 25% SFR. When the CBI and BCI parameters are compared, the CBI’s system latency and timeout threshold should be reflected in the BCI’s update rate. This would maximize the number of trials, even at a high SFR. These findings suggested that a higher number of trials per minute optimizes the ITR of a non-invasive BBI. The delays innate to each BCI protocol and CBI stimulation method must also be accounted for. The high latencies in each are the primary constraints of non-invasive BBI for the foreseeable future.
format Online
Article
Text
id pubmed-7020695
institution National Center for Biotechnology Information
language English
publishDate 2020
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-70206952020-02-28 Optimizing Computer–Brain Interface Parameters for Non-invasive Brain-to-Brain Interface LaRocco, John Paeng, Dong-Guk Front Neuroinform Neuroscience A non-invasive, brain-to-brain interface (BBI) requires precision neuromodulation and high temporal resolution as well as portability to increase accessibility. A BBI is a combination of the brain–computer interface (BCI) and the computer–brain interface (CBI). The optimization of BCI parameters has been extensively researched, but CBI has not. Parameters taken from the BCI and CBI literature were used to simulate a two-class medical monitoring BBI system under a wide range of conditions. BBI function was assessed using the information transfer rate (ITR), measured in bits per trial and bits per minute. The BBI ITR was a function of classifier accuracy, window update rate, system latency, stimulation failure rate (SFR), and timeout threshold. The BCI parameters, including window length, update rate, and classifier accuracy, were kept constant to investigate the effects of varying the CBI parameters, including system latency, SFR, and timeout threshold. Based on passively monitoring BCI parameters, a base ITR of 1 bit/trial was used. The optimal latency was found to be 100 ms or less, with a threshold no more than twice its value. With the optimal latency and timeout parameters, the system was able to maintain near-maximum efficiency, even with a 25% SFR. When the CBI and BCI parameters are compared, the CBI’s system latency and timeout threshold should be reflected in the BCI’s update rate. This would maximize the number of trials, even at a high SFR. These findings suggested that a higher number of trials per minute optimizes the ITR of a non-invasive BBI. The delays innate to each BCI protocol and CBI stimulation method must also be accounted for. The high latencies in each are the primary constraints of non-invasive BBI for the foreseeable future. Frontiers Media S.A. 2020-02-07 /pmc/articles/PMC7020695/ /pubmed/32116625 http://dx.doi.org/10.3389/fninf.2020.00001 Text en Copyright © 2020 LaRocco and Paeng. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
LaRocco, John
Paeng, Dong-Guk
Optimizing Computer–Brain Interface Parameters for Non-invasive Brain-to-Brain Interface
title Optimizing Computer–Brain Interface Parameters for Non-invasive Brain-to-Brain Interface
title_full Optimizing Computer–Brain Interface Parameters for Non-invasive Brain-to-Brain Interface
title_fullStr Optimizing Computer–Brain Interface Parameters for Non-invasive Brain-to-Brain Interface
title_full_unstemmed Optimizing Computer–Brain Interface Parameters for Non-invasive Brain-to-Brain Interface
title_short Optimizing Computer–Brain Interface Parameters for Non-invasive Brain-to-Brain Interface
title_sort optimizing computer–brain interface parameters for non-invasive brain-to-brain interface
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7020695/
https://www.ncbi.nlm.nih.gov/pubmed/32116625
http://dx.doi.org/10.3389/fninf.2020.00001
work_keys_str_mv AT laroccojohn optimizingcomputerbraininterfaceparametersfornoninvasivebraintobraininterface
AT paengdongguk optimizingcomputerbraininterfaceparametersfornoninvasivebraintobraininterface