Abstract
| 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 | |
| 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 |
Keywords
- Brain Computer Interface (BCI)
- Electroencephalogram (EEG)
- Steady State Visually Evoked Potential (SSVEP)
- Common Spatial Patterns (CSP)
- Motor Imagery (MI)
- Hybrid
- Neurorehabilitation
Cite this
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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
ER -