The feedback dynamics of brain-computer interfaces in a distributed processing environment
Files
Date
2022-01-04
Contributor
Advisor
Department
Instructor
Depositor
Speaker
Researcher
Consultant
Interviewer
Narrator
Transcriber
Annotator
Journal Title
Journal ISSN
Volume Title
Publisher
Volume
Number/Issue
Starting Page
Ending Page
Alternative Title
Abstract
This paper describes a distributed paradigm for human brain-computer interfaces that can incorporate machine learning-directly stimulus feedback to the subject. Specifically, we use OpenBCI hardware and software to capture real-time EEG (Electroencephalography) waveforms from a subject on a host ''client" computer and stream them to another ''server" computer which could perform complex analyses on the waveforms prior to sending commands back to the OpenBCI interface directing alterations to the stimulus. In addition to describing the conceptual system framework, we present here the test results quantifying the closed-loop system latencies under various conditions. Quantifying latency in any feedback control loop (in this case, one that actually contains the human subject's brain) is vital since excess latency can destabilize a system.
Description
Keywords
IT Architectures and Implementations in Healthcare Environments, adaptive framework, brain, brain-computer interface, eeg
Citation
Extent
10 pages
Format
Geographic Location
Time Period
Related To
Proceedings of the 55th Hawaii International Conference on System Sciences
Related To (URI)
Table of Contents
Rights
Attribution-NonCommercial-NoDerivatives 4.0 International
Rights Holder
Local Contexts
Email libraryada-l@lists.hawaii.edu if you need this content in ADA-compliant format.