Conference paper
A Hybrid MI-SSVEP based Brain Computer Interface for Potential Upper Limb Neurorehabilitation: A Pilot Study
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.
| Language: | English |
|---|---|
| Publisher: | IEEE |
| Year: | 2019 |
| Pages: | 1-6 |
| Proceedings: | 7th International Winter Conference on Brain-Computer Interface |
| ISBN: | 1538681161 , 153868117X , 153868117x , 9781538681169 and 9781538681176 |
| ISSN: | 25727672 |
| Types: | Conference paper |
| DOI: | 10.1109/IWW-BCI.2019.8737333 |
| ORCIDs: | Puthusserypady, Sadasivan |
Brain Computer Interface (BCI) Common Spatial Patterns (CSP) Electroencephalogram (EEG) Hybrid Motor Imagery (MI) Neurorehabilitation Steady State Visually Evoked Potential (SSVEP)
EEG data Electrodes Electroencephalography MI classification accuracy MI-based multiclass BCI paradigms Microsoft Windows SSVEP classification accuracy Stroke (medical condition) Support vector machines Task analysis Visualization brain signal brain-computer interfaces common spatial pattern filter electroencephalography hybrid BCI performance hybrid BCI system hybrid MI-SSVEP based brain computer interface medical signal processing motor imagery task neurophysiology patient rehabilitation power spectral density resting state sensorimotor rhythms signal classification spatial filters spectral analysis steady state visual evoked potential support vector machine classifier support vector machines upper limb neurorehabilitation user intention visual evoked potentials
