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Original Research ARTICLE Provisionally accepted The full-text will be published soon. Notify me

Front. Neurorobot. | doi: 10.3389/fnbot.2019.00094

Decoding Imagined 3D Arm Movement Trajectories from EEG to Control Two Virtual Arms - A Pilot Study

  • 1Ulster University, United Kingdom
  • 2Holon Institute of Technology, Israel

Background: Realization of online control of an artificial or virtual arm using information decoded from EEG normally occurs by classifying different activation states or voluntary modulation of the sensorimotor activity linked to different overt actions of the subject. However, using a more natural control scheme such as decoding the trajectory of imagined 3D arm movements to move a prosthetic, robotic, or virtual arm has been reported in a limited amount of studies, all using offline feed-forward control schemes.
Objective: In this study, we report the first attempt to realize online control of two virtual arms generating movements towards three targets/arm in 3D space. The 3D trajectory of imagined arm movements was decoded from power spectral density of mu, low beta, high beta, and low gamma EEG oscillations using multiple linear regression. The analysis was performed on a dataset recorded from three subjects in seven sessions wherein each session comprised three experimental blocks: an offline calibration block and two online feedback blocks. Target classification accuracy using predicted trajectories of the virtual arms was computed and compared with results of a filter-bank common spatial patterns (FBCSP) based multi-class classification method involving mutual information (MI) selection and linear discriminant analysis (LDA) modules.
Main results: Target classification accuracy from predicted trajectory of imagined 3D arm movements in the offline runs for two subjects (mean 45%, std 5%) was significantly higher (p<0.05) than chance level (33.3%). Nevertheless, the accuracy during real-time control of the virtual arms using the trajectory decoded directly from EEG was in the range of chance level (33.3%). However, the results of two subjects show that false-positive feedback may increase the accuracy in closed-loop. The FBCSP based multi-class classification method distinguished imagined movements of left and right arm with reasonable accuracy for two of the three subjects (mean 70%, std 5% compared to 50% chance level). However, classification of the imagined arm movement towards three targets was not successful with the FBCSP classifier as the achieved accuracy (mean 33%, std 5%) was similar to the chance level (33.3%). Sub-optimal components of the multi-session experimental paradigm were identified, and an improved paradigm proposed.

Keywords: brain-computer interface (BCI), Imagined 3D arm movements, Online motion trajectory prediction, Multiple linear regression (MLR), Filter-Bank Common Spatial Patterns (FBCSP), Electroencephalography (EEG), Virtual robotic arm

Received: 31 Dec 2018; Accepted: 28 Oct 2019.

Copyright: © 2019 Korik, Sosnik, Siddique and Coyle. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Mr. Attila Korik, Ulster University, Coleraine, BT52 1SA, United Kingdom, korik-a@ulster.ac.uk