Unimanual versus bimanual motor imagery classifiers for assistive and rehabilitative brain computer interfaces

Vuckovic, A. , Pangaro, S. and Putri, F. (2018) Unimanual versus bimanual motor imagery classifiers for assistive and rehabilitative brain computer interfaces. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 26(12), pp. 2407-2415. (doi: 10.1109/TNSRE.2018.2877620)

Abstract

Bimanual movements are an integral part of everyday activities and are often included in rehabilitation therapies. Yet electroencephalography (EEG) based assistive and rehabilitative brain computer interface (BCI) systems typically rely on motor imagination (MI) of one limb at the time. In this study we present a classifier which discriminates between uni-and bimanual MI. Ten able bodied participants took part in cue based motor execution (ME) and MI tasks of the left (L), right (R) and both (B) hands. A 32 channel EEG was recorded. Three linear discriminant analysis classifiers, based on MI of L-B, B-R and B--L hands were created, with features based on wide band Common Spatial Patterns (CSP) 8-30 Hz, and band specifics Common Spatial Patterns (CSPb). Event related desynchronization (ERD) was significantly stronger during bimanual compared to unimanual ME on both hemispheres. Bimanual MI resulted in bilateral parietally shifted ERD of similar intensity to unimanual MI. The average classification accuracy for CSP and CSPb was comparable for L-R task (73±9% and 75±10% respectively) and for L-B task (73±11% and 70±9% respectively). However, for R-B task (67±3% and 72±6% respectively) it was significantly higher for CSPb (p=0.0351). Six participants whose L-R classification accuracy exceeded 70% were included in an on-line task a week later, using the unmodified offline CSPb classifier, achieving 69±3% and 66±3% accuracy for the L-R and R-B tasks respectively. Combined uni and bimanual BCI could be used for restoration of motor function of highly disabled patents and for motor rehabilitation of patients with motor deficits.

• Item Type: Articles
• Additional Information: This work was supported in part by the Indonesian Government PhD scholarship.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Putri, Finda and Pangaro, Miss Sara and Vuckovic, Dr Aleksandra
• Authors: Vuckovic, A., Pangaro, S., and Putri, F.
• College/School: College of Science and Engineering > School of Engineering > Biomedical Engineering
• Journal Name: IEEE Transactions on Neural Systems and Rehabilitation Engineering
• Publisher: IEEE
• ISSN: 1534-4320
• ISSN (Online): 1558-0210
• Published Online: 25 October 2018
• Copyright Holders: Copyright © 2018 IEEE
• First Published: First published in IEEE Transactions on Neural Systems and Rehabilitation Engineering 26(12): 2407-2415
• Publisher Policy: Reproduced in accordance with the publisher copyright policy