Prospectively identifying risks and controls for advanced brain-computer interfaces: A Networked Hazard Analysis and Risk Management System (Net-HARMS) approach
The introduction of advanced digital technologies continues to increase system complexity and introduce risks, which must be proactively identified and managed to support system resilience. Brain-computer interfaces (BCIs) are one such technology; however, the risks arising from broad societal use of the technology have yet to be identified and controlled. This study applied a structured systems thinking-based risk assessment method to prospectively identify risks and risk controls for a hypothetical future BCI system lifecycle. The application of the Networked Hazard Analysis and Risk Management System (Net-HARMS) method identified over 800 risks throughout the BCI system lifecycle, from BCI development and regulation through to BCI use, maintenance, and decommissioning. High-criticality risk themes include the implantation and degradation of unsafe BCIs, unsolicited brain stimulation, incorrect signals being sent to safety-critical technologies, and insufficiently supported BCI users. Over 600 risk controls were identified that could be implemented to support system safety and performance resilience. Overall, many highly-impactful BCI system safety and performance risks may arise throughout the BCI system lifecycle and will require collaborative efforts from a wide range of BCI stakeholders to adequately control. Whilst some of the identified controls are practical, work is required to develop a more systematic set of controls to best support the design of a resilient sociotechnical BCI system.
Authors
- Gemma J. M. Read (ORCID: https://orcid.org/0000-0003-3360-812X)
- Brandon J. King (ORCID: https://orcid.org/0000-0001-6039-5495)
- Paul M. Salmon (ORCID: https://orcid.org/0000-0001-7403-0286)
Institutions
- Twitter (United States) (US)
- University of the Sunshine Coast (AU)
Publication Details
- Journal
- Applied Ergonomics
- Published
- 2024-09-12
- DOI
- https://doi.org/10.1016/j.apergo.2024.104382
- Citations
- 6
- Primary Topic
- EEG and Brain-Computer Interfaces
- Type
- article
- Field-Weighted Citation Impact
- 2.06
Funders
- Australian Government