A Hybrid MI-SSVEP based Brain Computer Interface for Potential Upper Limb Neurorehabilitation: A Pilot Study

Ciaran McGeady, Aleksandra Vuckovic, Sadasivan Puthusserypady

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

100 Downloads (Pure)

Abstract

This pilot study implements a hybrid BCI system in an effort to deduce the effects of measuring more than one brain signal in a motor imagery (MI) task. In addition to sensorimotor rhythms (SMRs), a steady state visual evoked potential (SSVEP) was introduced to acquire additional information relating to user intention. A common spatial pattern (CSP) filter followed by a support vector machine (SVM) classifier were used to distinguish between MI and the resting state. The power spectral density (PSD) was used to classify the SSVEP. Results from online simulations of EEG data collected from 10 able-bodied participants showed that the hybrid BCI’s performance achieved a classification accuracy of 77.3±8.2%, with an SSVEP classification accuracy of 94.4±3.5%, and MI classification accuracy of 80.9±8.1%, an improvement upon purely MI-based multi-class BCI paradigms.
Original languageEnglish
Title of host publicationProceedings of 2019 7th International Winter Conference on Brain-Computer Interface
Number of pages6
PublisherIEEE
Publication date2019
ISBN (Print)978-1-5386-8116-9
DOIs
Publication statusPublished - 2019
Event7th International Winter Conference on Brain-Computer Interface - High 1 Resort, Jeongseon, Korea, Republic of
Duration: 18 Feb 201920 Feb 2019
http://brain.korea.ac.kr/bci2019/

Conference

Conference7th International Winter Conference on Brain-Computer Interface
LocationHigh 1 Resort
CountryKorea, Republic of
CityJeongseon
Period18/02/201920/02/2019
Internet address

Keywords

  • Brain Computer Interface (BCI)
  • Electroencephalogram (EEG)
  • Steady State Visually Evoked Potential (SSVEP)
  • Common Spatial Patterns (CSP)
  • Motor Imagery (MI)
  • Hybrid
  • Neurorehabilitation

Cite this

McGeady, C., Vuckovic, A., & Puthusserypady, S. (2019). A Hybrid MI-SSVEP based Brain Computer Interface for Potential Upper Limb Neurorehabilitation: A Pilot Study. In Proceedings of 2019 7th International Winter Conference on Brain-Computer Interface IEEE. https://doi.org/10.1109/IWW-BCI.2019.8737333
McGeady, Ciaran ; Vuckovic, Aleksandra ; Puthusserypady, Sadasivan. / A Hybrid MI-SSVEP based Brain Computer Interface for Potential Upper Limb Neurorehabilitation: A Pilot Study. Proceedings of 2019 7th International Winter Conference on Brain-Computer Interface . IEEE, 2019.
@inproceedings{895b3872d5704c679355ae3fb26895b1,
title = "A Hybrid MI-SSVEP based Brain Computer Interface for Potential Upper Limb Neurorehabilitation: A Pilot Study",
abstract = "This pilot study implements a hybrid BCI system in an effort to deduce the effects of measuring more than one brain signal in a motor imagery (MI) task. In addition to sensorimotor rhythms (SMRs), a steady state visual evoked potential (SSVEP) was introduced to acquire additional information relating to user intention. A common spatial pattern (CSP) filter followed by a support vector machine (SVM) classifier were used to distinguish between MI and the resting state. The power spectral density (PSD) was used to classify the SSVEP. Results from online simulations of EEG data collected from 10 able-bodied participants showed that the hybrid BCI’s performance achieved a classification accuracy of 77.3±8.2{\%}, with an SSVEP classification accuracy of 94.4±3.5{\%}, and MI classification accuracy of 80.9±8.1{\%}, an improvement upon purely MI-based multi-class BCI paradigms.",
keywords = "Brain Computer Interface (BCI), Electroencephalogram (EEG), Steady State Visually Evoked Potential (SSVEP), Common Spatial Patterns (CSP), Motor Imagery (MI), Hybrid, Neurorehabilitation",
author = "Ciaran McGeady and Aleksandra Vuckovic and Sadasivan Puthusserypady",
year = "2019",
doi = "10.1109/IWW-BCI.2019.8737333",
language = "English",
isbn = "978-1-5386-8116-9",
booktitle = "Proceedings of 2019 7th International Winter Conference on Brain-Computer Interface",
publisher = "IEEE",
address = "United States",

}

McGeady, C, Vuckovic, A & Puthusserypady, S 2019, A Hybrid MI-SSVEP based Brain Computer Interface for Potential Upper Limb Neurorehabilitation: A Pilot Study. in Proceedings of 2019 7th International Winter Conference on Brain-Computer Interface . IEEE, 7th International Winter Conference on Brain-Computer Interface, Jeongseon, Korea, Republic of, 18/02/2019. https://doi.org/10.1109/IWW-BCI.2019.8737333

A Hybrid MI-SSVEP based Brain Computer Interface for Potential Upper Limb Neurorehabilitation: A Pilot Study. / McGeady, Ciaran; Vuckovic, Aleksandra; Puthusserypady, Sadasivan.

Proceedings of 2019 7th International Winter Conference on Brain-Computer Interface . IEEE, 2019.

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

TY - GEN

T1 - A Hybrid MI-SSVEP based Brain Computer Interface for Potential Upper Limb Neurorehabilitation: A Pilot Study

AU - McGeady, Ciaran

AU - Vuckovic, Aleksandra

AU - Puthusserypady, Sadasivan

PY - 2019

Y1 - 2019

N2 - This pilot study implements a hybrid BCI system in an effort to deduce the effects of measuring more than one brain signal in a motor imagery (MI) task. In addition to sensorimotor rhythms (SMRs), a steady state visual evoked potential (SSVEP) was introduced to acquire additional information relating to user intention. A common spatial pattern (CSP) filter followed by a support vector machine (SVM) classifier were used to distinguish between MI and the resting state. The power spectral density (PSD) was used to classify the SSVEP. Results from online simulations of EEG data collected from 10 able-bodied participants showed that the hybrid BCI’s performance achieved a classification accuracy of 77.3±8.2%, with an SSVEP classification accuracy of 94.4±3.5%, and MI classification accuracy of 80.9±8.1%, an improvement upon purely MI-based multi-class BCI paradigms.

AB - This pilot study implements a hybrid BCI system in an effort to deduce the effects of measuring more than one brain signal in a motor imagery (MI) task. In addition to sensorimotor rhythms (SMRs), a steady state visual evoked potential (SSVEP) was introduced to acquire additional information relating to user intention. A common spatial pattern (CSP) filter followed by a support vector machine (SVM) classifier were used to distinguish between MI and the resting state. The power spectral density (PSD) was used to classify the SSVEP. Results from online simulations of EEG data collected from 10 able-bodied participants showed that the hybrid BCI’s performance achieved a classification accuracy of 77.3±8.2%, with an SSVEP classification accuracy of 94.4±3.5%, and MI classification accuracy of 80.9±8.1%, an improvement upon purely MI-based multi-class BCI paradigms.

KW - Brain Computer Interface (BCI)

KW - Electroencephalogram (EEG)

KW - Steady State Visually Evoked Potential (SSVEP)

KW - Common Spatial Patterns (CSP)

KW - Motor Imagery (MI)

KW - Hybrid

KW - Neurorehabilitation

U2 - 10.1109/IWW-BCI.2019.8737333

DO - 10.1109/IWW-BCI.2019.8737333

M3 - Article in proceedings

SN - 978-1-5386-8116-9

BT - Proceedings of 2019 7th International Winter Conference on Brain-Computer Interface

PB - IEEE

ER -

McGeady C, Vuckovic A, Puthusserypady S. A Hybrid MI-SSVEP based Brain Computer Interface for Potential Upper Limb Neurorehabilitation: A Pilot Study. In Proceedings of 2019 7th International Winter Conference on Brain-Computer Interface . IEEE. 2019 https://doi.org/10.1109/IWW-BCI.2019.8737333