Sensorimotor Rhythm as Control Signal in EEG-Based Brain-Computer Interfaces

Master thesis (2023)

Authors

M.E. Bol Mechanical Engineering

Contributors

M.L. van de Ruit Biomechatronics & Human-Machine Control - Mechanical, Maritime and Materials Engineering (mentor)
Y.B. Eisma Human-Robot Interaction - Mechanical, Maritime and Materials Engineering (graduation committee member)
Faculty
Mechanical Engineering, Mechanical Engineering
Copyright: © 2023 Marije Bol
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Published Date

09-11-2023

Language

English

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Faculty

Mechanical Engineering

Abstract

Background: A brain-computer interface (BCI) is a system that enables humans to control a computer by their brain signals. This can be achieved by modulating the sensorimotor rhythm (SMR) through both motor execution and motor imagery. The potential enhancement of spinal reflexes and motor control through SMR training is attributed to the hypothesis that activity-dependent brain plasticity guides spinal plasticity during motor skill learning. However, the signal processing needed for conversion from raw brain signals to a robust control signal is challenging. The recorded electroencephalogram (EEG) signals are contaminated by multiple unknown sources and suffer from inter-subject variability, complicating the development of the BCI.
Objective: To obtain a robust control signal the study 1) investigated the relation between event-related desynchronization (ERD) and mechanical stretch reflex size in the flexor carpi radialis across four muscle pre-loads consisting of 0%, 5%, 25% and 40% of maximum voluntary contraction (MVC), 2) investigated the ability of three offline signal processing paradigms in distinguishing between periods of rest and activity using EEG data associated with motor execution and motor imagery, 3) built a pseudo-online signal processing paradigm to simulate real-time signal processing based on a single trial and a continuous data stream.
Method: Mechanical stretch perturbations were applied to the wrist under four percentages of MVC during motor execution and imagery conditions in six healthy subjects. The data anal- ysis encompassed signal processing techniques including pre- processing with a large Laplacian filter, feature extraction through autoregressive modelling (AR), power spectral density (PSD), or discrete wavelet transform (DWT), and classification using linear discriminant analysis (LDA).
Results: Mechanical stretch reflex sizes and ERD amplitude significantly increased with increasing percentage of MVC for motor execution trials. For motor imagery trials, no significant correlation was found between the stretch reflex size and ERD amplitude. The offline signal processing paradigms resulted in classification accuracies of 73.55% (PSD), 71.96% (DWT) and 57.13% (AR). The classification accuracies significantly increased with increasing percentage of MVC. The pseudo-online paradigm resulted in a mean classification accuracy of 51.38%.
Conclusions: The EEG-based BCI shows potential for enhancing the functional recovery of patients with motor disorders. The findings demonstrate that feature extraction methods PSD and DWT could effectively distinguish between periods of rest and activity in motor execution data. Nevertheless, for the intended application, including real-time processing based on single trial motor imagery data, BCI performance should be improved. Future research should focus on motor imagery EEG data encompassing motor imagery training and feedback on motor imagery performance.