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

Ciaran McGeady; Aleksandra Vuckovic; Sadasivan Puthusserypady

doi:10.1109/IWW-BCI.2019.8737333

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 proceeding › Article in proceedings › Research › peer-review

237 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 language	English
Title of host publication	Proceedings of 2019 7th International Winter Conference on Brain-Computer Interface
Number of pages	6
Publisher	IEEE
Publication date	2019
ISBN (Print)	978-1-5386-8116-9
DOIs	https://doi.org/10.1109/IWW-BCI.2019.8737333
Publication status	Published - 2019
Event	7th International Winter Conference on Brain-Computer Interface - High 1 Resort, Jeongseon, Korea, Republic of Duration: 18 Feb 2019 → 20 Feb 2019 http://brain.korea.ac.kr/bci2019/

Conference

Conference	7th International Winter Conference on Brain-Computer Interface
Location	High 1 Resort
Country	Korea, Republic of
City	Jeongseon
Period	18/02/2019 → 20/02/2019
Internet address	http://brain.korea.ac.kr/bci2019/

Keywords

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

Access to Document

10.1109/IWW-BCI.2019.8737333

08737333Accepted author manuscript, 869 KB

Fingerprint Dive into the research topics of 'A Hybrid MI-SSVEP based Brain Computer Interface for Potential Upper Limb Neurorehabilitation: A Pilot Study'. Together they form a unique fingerprint.

Cite this

@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{\textquoteright}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",

note = "7th International Winter Conference on Brain-Computer Interface, BCI 2019 ; Conference date: 18-02-2019 Through 20-02-2019",

url = "http://brain.korea.ac.kr/bci2019/",

}

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 proceeding › Article in proceedings › Research › peer-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

T2 - 7th International Winter Conference on Brain-Computer Interface

Y2 - 18 February 2019 through 20 February 2019

ER -