Martin Iñiguez de Onzoño: Characterization and Classification of Finger Movement-Related Brain Activity using Optically Pumped Magnetometers

BCCN Berlin / Technische Universität Berlin

Abstract

Stroke significantly impairs upper limb function, directly affecting quality of life. Brain- Computer Interfaces (BCIs) processing brain signals to control external devices offer promising rehabilitation solutions. However, the spatial resolution limitations of the standard recording technique, electroencephalography (EEG), hinder the decoding of brain activity.

This research explores the use of magnetoencephalography (MEG) with Optically Pumped Magnetometers (OPMs), which provide higher spatial resolution, focusing on the potential for discriminating finger movements. By investigating the capabilities of OPM-MEG through frequency analysis, spatial filtering, and deep learning, this thesis aims to advance BCI technology, focusing on decoding finger movement-related neural activity.

Initial findings in this study hinted at potential for binary classification, particularly in the beta band (12.5Hz to 30Hz). Frequency-based analysis, focused on that band, underscored the difficulty in achieving reliable discrimination via visualization. Using more complex approaches, a notable outcome was achieved with a Low-Frequency Convolutional Neural Network (LF-CNN), revealing a classification accuracy of 76% between two specific finger movements. This result is comparable to state-of-the-art findings using EEG. Multi-class discrimination among five fingers, on the other hand, proved to be challenging since the differences in brain activity were too subtle.

The study shows that although OPMs provide high spatial resolution, their effectiveness in complex BCI tasks such as fine-grained finger movement discrimination remains limited. This highlights the necessity for additional research and the potential benefits of integrating OPM data with other modalities to improve classification performance.

 

Additional Information

Master Thesis Defense

 

Organized by

Prof. Dr. med. Surjo R. Soekadar   & Prof. Dr. Livia De Hoz