Skip to main content
DORAS
DCU Online Research Access Service
Login (DCU Staff Only)
An open source multi-modal data-acquisition platform for experimental investigation of blended control of scale vehicles

Redmond, Peter, Fleury, Andrew and Ward, Tomás E. ORCID: 0000-0002-6173-6607 (2022) An open source multi-modal data-acquisition platform for experimental investigation of blended control of scale vehicles. In: IEEE International Conference on Metrology for Extended Reality, AI and Neural Engineering, 26-28 Oct 2022, Rome. (In Press)

Full text available as:

[img]
Preview
PDF - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
2MB

Abstract

Currently many autonomous vehicles require a per- son to monitor the system and take over when an unexpected or unusual event occurs. A person may be able to monitor multiple such vehicles but a question arises as to how many, and how to measure the cognitive requirements. Brain Computer Interfaces (BCI) operating passively could aid in assisting remote operators in such tasks but there is as yet significant research to be undertaken before such technology becomes robust and effective. To this end we describe a platform for acquisition of multi-modal data for passive hybrid Brain Computer Inter- face (phBCI) development purposes. The open source system integrates electroencephalography (EEG), computer vision and a wearable inertial measurement unit (IMU) along with time- stamped event markers for a subject engaged in a set of driving-related tasks as applied to blended control of multiple vehicles. The vehicular control task is realised both with graded complexity simulations and physical scale autonomous vehicles. This platform has the following significant advantages: reduced experimental variability due to data acquisition system implementation decisions; ease of reproduction of experiments through shareable configuration information; and acceleration of open science dataset accumulation. Consequently this freely available open source platform has the potential to enhance the reproducibility of passive hybrid BCI experimental research.

Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Refereed:Yes
Uncontrolled Keywords:Brain Computer Interfaces (BCI); autonomous vehicles
Subjects:Computer Science > Artificial intelligence
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Research Initiatives and Centres > INSIGHT Centre for Data Analytics
Publisher:IEEE
Copyright Information:© 2022 The Authors
Funders:Transpoco Ltd., Science Foundation Ireland (Grant Nos. SFI/12/RC/2289 P2 and18/SP/5942)
ID Code:27879
Deposited On:25 Oct 2022 11:53 by Tomas Ward . Last Modified 20 Jan 2023 16:40

Downloads

Downloads per month over past year

Archive Staff Only: edit this record

  • Student Email
  • Staff Email
  • Student Apps
  • Staff Apps
  • Loop
  • Disclaimer
  • Privacy
  • Contact Us