Abstract
Non-invasive electroencephalogram (EEG) based brain-computer interface (BCI) users aim to achieve three-dimensional (3D) control using only brain signals. Motion trajectory prediction (MTP) is a method that may be used for translating imagined 3D movement into virtual limb control. This process requires the capture of actual kinematic of limb motion trajectory in an experimental setup to perform MTP. Virtual reality (VR) allows for natural, embodied virtual limb feedback and has the potential to create improved experimental BCI training paradigms through an increased presence in applications such as reach target tasks. Here, results are presented from a novel experimental setup and pilot study involving two subjects attempting to control 3D movement of virtual limbs using imagined 3D movement and show that overall, both subjects were able to achieve some level of control with one session achieving a correlation r=0.39 ±0.131, p
| Original language | English |
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| Title of host publication | 2022 IEEE International Conference on Metrology for Extended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE) |
| Publisher | IEEE Xplore |
| Pages | 697-702 |
| Number of pages | 6 |
| ISBN (Electronic) | 978-1-6654-8574-6, 978-1-6654-8573-9 |
| ISBN (Print) | 978-1-6654-8575-3 |
| DOIs | |
| Publication status | Published online - 5 Dec 2022 |
| Event | IEEE International Conference on Metrology for Extended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE) - Rome, Italy Duration: 26 Oct 2022 → 28 Oct 2022 |
Publication series
| Name | 2022 IEEE International Workshop on Metrology for Extended Reality, Artificial Intelligence and Neural Engineering, MetroXRAINE 2022 - Proceedings |
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Conference
| Conference | IEEE International Conference on Metrology for Extended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE) |
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| Abbreviated title | MetroXRAINE |
| Country/Territory | Italy |
| City | Rome |
| Period | 26/10/22 → 28/10/22 |
Bibliographical note
Funding Information:ACKNOWLEDGMENT This research is supported by the Spatial Computing and Neurotechnology Innovation Hub (SCANi-Hub) at the Intelligent Systems Research Centre (ISRC), Ulster University and the Department for Employment (DfE) Higher Education Capital fund and PhD studentship programme. We are grateful for access to the Tier 2 High Performance Computing resources provided by the Northern Ireland High-Performance Computing (NI-HPC) facility funded by the UK Engineering and Physical Sciences Research Council (EPSRC), Grant Nos. EP/T022175/ and EP/W03204X/1. DC is grateful for the UKRI Turing AI Fellowship 2021-2025 funded by the EPSRC (grant number EP/V025724/1). Both participants are also kindly thanked for their time and effort.
Publisher Copyright:
© 2022 IEEE.
Funding
Funding Information: ACKNOWLEDGMENT This research is supported by the Spatial Computing and Neurotechnology Innovation Hub (SCANi-Hub) at the Intelligent Systems Research Centre (ISRC), Ulster University and the Department for Employment (DfE) Higher Education Capital fund and PhD studentship programme. We are grateful for access to the Tier 2 High Performance Computing resources provided by the Northern Ireland High-Performance Computing (NI-HPC) facility funded by the UK Engineering and Physical Sciences Research Council (EPSRC), Grant Nos. EP/T022175/ and EP/W03204X/1. DC is grateful for the UKRI Turing AI Fellowship 2021-2025 funded by the EPSRC (grant number EP/V025724/1). Both participants are also kindly thanked for their time and effort. Publisher Copyright: © 2022 IEEE.
Keywords
- Brian-computer interface
- motor imagery
- Virtual reality
- Virtual environmnet
- 3D BCI
- Embodiment
- Spatial
- Presence
- Upper Limb
- Kinematic
- Visual Feedback
- Motion trajectory prediction
- Motor Imagery
- Virtual Environment
- Brain-Computer Interface
- Motion Trajectory Prediction.
- Virtual Reality
