AbstractBrain-computer interface (BCI) research targets movement-free communication between the user and an electronic device using information encoded in the electrophysiological activity of the brain without involving neuromuscular pathways. In BCIs, voluntary modulation of the sensorimotor rhythms (SMRs) is the most common approach for controlling objects in real and virtual spaces using task-related multi-class classification (MC) of electroencephalography (EEG), where information about direction of movement or imagined movement of the limb is not explicitly utilized, but instead different limbs are used to attempt direction control.
The work presented in this thesis is aimed at decoding the 3D trajectory of imagined arm movements from EEG, to establish the utility of non-invasive signals for controlling virtual limb(s) or limb prostheses in a more natural way. There is a growing body of evidence that decoding of (executed) arm movement trajectories from EEG is possible, but one of the major research questions in non-invasive BCI research is: Can 3D trajectories associated with imagined 3D limb movements be decoded or predicted from EEG? To date, only a few studies have attempted to address this research question. This PhD thesis builds on the methodology to address this research question through a series of offline and online experiments and has resulted in novel supporting evidence and new methodology resulting in three main contributions.
Contribution 1: Slow cortical potentials (SCPs) in the low delta (0-2Hz) band have, predominantly, been found to encode the trajectory of limb movements when using techniques such as multiple linear regression (mLR). This thesis presents a comparative analysis indicating that band power of mu (8-12Hz) and beta (12-28Hz) EEG oscillation encode more information from the 3D trajectory of arm movements compared to the SCPs, and that mLR and standard band-pass filtered EEG potentials may occlude information in higher frequency component thus resulting in SCP predominance. Contribution 2: The thesis shows for the first time that an assumed 3D trajectory of imagined arm movements may be decoded from the band power of mu (8-12Hz), beta (12-28Hz), and low gamma (28-40Hz) oscillations and SCPs provide less information to enable decoding of imagined 3D arm movement trajectories. Contribution 3: The final contribution of this thesis, to the best of the author's knowledge, is the first attempt at real-time control of two virtual arms using 3D trajectories of imagined arm movement decoded from EEG.
|Date of Award||Jun 2019|
|Supervisor||Nazmul Siddique (Supervisor) & Damien Coyle (Supervisor)|
- Brain Computer Interface
- Arm movement trajectory
- multiple linear regression