Meditation as an effective BCI training protocol for controlling wheeled robots
David Duarte, Cabral Lima, Priscila M. V. Lima, Rubens Lacerda Queiroz, Rubens Turci
F1000Research July 4, 2023 DOI: 10.12688/f1000research.138057.1 (opens in new tab)
Study at a glance
AI-extracted from the abstract| Characteristics | Case-control study Peer reviewed |
|---|---|
| Sample size | 30 |
| Population | Meditators and non-meditators controlling a simulated wheeled robot via a BCI system |
| Intervention | Mindfulness meditation |
| Topics | Meditation |
| Citations | 2 |
| Key findings | Meditators controlled the simulated wheeled robot better than non-meditators: 30% faster in straight-ahead moves, 66% versus 27% accuracy in stop-and-go moves, and 40% versus 7% accuracy in rectangular moves. The authors recommend combining mindfulness meditation with standard BCI training protocols. |
Abstract
Background: Brain-Computer Interface (BCI) systems allow the use of electroencephalographic activities to control devices. As such, it can be an important tool for disabled people since using a BCI does not involves any muscular stimulation. Despite its great potential as an assistive technology, BCI systems are yet scarcely available outside scientific contexts. The main reason is that BCI is still not accurate enough for real world situations. In this paper, we investigated if mindfulness meditation can help BCI users to perform better, particularly on controlling wheeled robots. Controlling wheeled robots must be seen as an important BCI research issue, since disabled people can take the best advantages from assistive wheeled robots.
Methods: Case-control study with 30 subjects, meditators and non-meditators, who controlled a simulated wheeled robot using a BCI system.
Results: In straight-ahead moves, the robot was 30% faster when controlled by meditators. In stop-and-go moves, the meditators controlled the robot with an accuracy of 66% while the non-meditators’ accuracy was 27%. In rectangular shape moves, meditators also performed better than non-meditators, with an accuracy of 40% against 7% of non-meditators.
Conclusion: The results show that meditators performed better than non-meditators. As such, we recommend combining mindfulness meditation with standard BCI training protocols for better control of wheeled robots.
Comparable studies
Other observational and cohort studies on meditation, most cited first.
| Study | Year | Design | Participants |
|---|---|---|---|
| Using Self-Report Assessment Methods to Explore Facets of Mindfulness Undergraduate students | 2006 | Observational cohort | |
| Effectiveness of a meditation-based stress reduction program in the treatment of anxiety disorders Patients with generalized anxiety disorder or panic disorder with or without agoraphobia | 1992 | Observational cohort | n = 22 |
| Meditation experience is associated with increased cortical thickness Participants with extensive Insight meditation experience and matched controls | 2005 | Observational cohort | n = 20 |
| Meditation experience is associated with differences in default mode network activity and connectivity Experienced meditators and matched meditation-naive controls | 2011 | Observational cohort | |
| Long-term meditators self-induce high-amplitude gamma synchrony during mental practice Long-term Buddhist practitioners and controls | 2004 | Observational cohort |
Citations in the library
Cites 4
- Short-term meditation training improves attention and self-regulation Proceedings of the National Academy of Sciences October 11, 2007
- Effect of mindfulness meditation on brain-computer interface performance. Consciousness and Cognition 2014
- Improving the performance of brain-computer interface through meditation practicing. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference 2008
- Meditation states and traits: EEG, ERP, and neuroimaging studies. Psychological Bulletin March 2006