Online 3D Motion Decoder BCI for Embodied Virtual Reality Upper Limb Control: A Pilot Study

Niall McShane; Attila Korik; Karl McCreadie; D.K. Charles; Damien Coyle

doi:10.1109/MetroXRAINE54828.2022.9967577

Online 3D Motion Decoder BCI for Embodied Virtual Reality Upper Limb Control: A Pilot Study

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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
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	https://doi.org/10.1109/MetroXRAINE54828.2022.9967577
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

Conference

Conference	IEEE International Conference on Metrology for Extended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE)
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.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

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

Access to Document

10.1109/MetroXRAINE54828.2022.9967577

Cite this

McShane, N., Korik, A., McCreadie, K., Charles, D. K., & Coyle, D. (2022). Online 3D Motion Decoder BCI for Embodied Virtual Reality Upper Limb Control: A Pilot Study. In 2022 IEEE International Conference on Metrology for Extended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE) (pp. 697-702). (2022 IEEE International Workshop on Metrology for Extended Reality, Artificial Intelligence and Neural Engineering, MetroXRAINE 2022 - Proceedings). IEEE Xplore. Advance online publication. https://doi.org/10.1109/MetroXRAINE54828.2022.9967577

McShane, Niall ; Korik, Attila ; McCreadie, Karl et al. / Online 3D Motion Decoder BCI for Embodied Virtual Reality Upper Limb Control: A Pilot Study. 2022 IEEE International Conference on Metrology for Extended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE). IEEE Xplore, 2022. pp. 697-702 (2022 IEEE International Workshop on Metrology for Extended Reality, Artificial Intelligence and Neural Engineering, MetroXRAINE 2022 - Proceedings).

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title = "Online 3D Motion Decoder BCI for Embodied Virtual Reality Upper Limb Control: A Pilot Study",

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",

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",

author = "Niall McShane and Attila Korik and Karl McCreadie and D.K. Charles and Damien Coyle",

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: {\textcopyright} 2022 IEEE.; IEEE International Conference on Metrology for Extended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE), MetroXRAINE ; Conference date: 26-10-2022 Through 28-10-2022",

year = "2022",

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language = "English",

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series = "2022 IEEE International Workshop on Metrology for Extended Reality, Artificial Intelligence and Neural Engineering, MetroXRAINE 2022 - Proceedings",

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McShane, N , Korik, A , McCreadie, K , Charles, DK & Coyle, D 2022, Online 3D Motion Decoder BCI for Embodied Virtual Reality Upper Limb Control: A Pilot Study. in 2022 IEEE International Conference on Metrology for Extended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE). 2022 IEEE International Workshop on Metrology for Extended Reality, Artificial Intelligence and Neural Engineering, MetroXRAINE 2022 - Proceedings, IEEE Xplore, pp. 697-702, IEEE International Conference on Metrology for Extended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE), Rome, Italy, 26/10/22. https://doi.org/10.1109/MetroXRAINE54828.2022.9967577

Online 3D Motion Decoder BCI for Embodied Virtual Reality Upper Limb Control: A Pilot Study. / McShane, Niall ; Korik, Attila ; McCreadie, Karl et al.
2022 IEEE International Conference on Metrology for Extended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE). IEEE Xplore, 2022. p. 697-702 (2022 IEEE International Workshop on Metrology for Extended Reality, Artificial Intelligence and Neural Engineering, MetroXRAINE 2022 - Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Online 3D Motion Decoder BCI for Embodied Virtual Reality Upper Limb Control: A Pilot Study

AU - McShane, Niall

AU - Korik, Attila

AU - McCreadie, Karl

AU - Charles, D.K.

AU - Coyle, Damien

N1 - 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.

PY - 2022/12/5

Y1 - 2022/12/5

N2 - 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

AB - 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

KW - Brian-computer interface

KW - motor imagery

KW - Virtual reality

KW - Virtual environmnet

KW - 3D BCI

KW - Embodiment

KW - Spatial

KW - Presence

KW - Upper Limb

KW - Kinematic

KW - Visual Feedback

KW - Motion trajectory prediction

KW - Motor Imagery

KW - Virtual Environment

KW - Brain-Computer Interface

KW - Motion Trajectory Prediction.

KW - Virtual Reality

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DO - 10.1109/MetroXRAINE54828.2022.9967577

M3 - Conference contribution

SN - 978-1-6654-8575-3

T3 - 2022 IEEE International Workshop on Metrology for Extended Reality, Artificial Intelligence and Neural Engineering, MetroXRAINE 2022 - Proceedings

SP - 697

EP - 702

BT - 2022 IEEE International Conference on Metrology for Extended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE)

PB - IEEE Xplore

T2 - IEEE International Conference on Metrology for Extended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE)

Y2 - 26 October 2022 through 28 October 2022

ER -

McShane N , Korik A , McCreadie K , Charles DK , Coyle D. Online 3D Motion Decoder BCI for Embodied Virtual Reality Upper Limb Control: A Pilot Study. In 2022 IEEE International Conference on Metrology for Extended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE). IEEE Xplore. 2022. p. 697-702. (2022 IEEE International Workshop on Metrology for Extended Reality, Artificial Intelligence and Neural Engineering, MetroXRAINE 2022 - Proceedings). Epub 2022 Dec 5. doi: 10.1109/MetroXRAINE54828.2022.9967577

Online 3D Motion Decoder BCI for Embodied Virtual Reality Upper Limb Control: A Pilot Study

Abstract

Publication series

Conference

Bibliographical note

Funding

UN SDGs

Keywords

Access to Document

Other files and links

Fingerprint

Impact of decoding algorithms, embodiment, and user factors on BCI-driven 3D limb trajectory control and learning

Cite this

Online 3D Motion Decoder BCI for Embodied Virtual Reality Upper Limb Control: A Pilot Study

Abstract

Publication series

Conference

Bibliographical note

Funding

UN SDGs

Keywords

Access to Document

Other files and links

Fingerprint

Student theses

Impact of decoding algorithms, embodiment, and user factors on BCI-driven 3D limb trajectory control and learning

Cite this