Decoding non-invasive brain activity with novel deep-learning approaches

TL;DR

This thesis explores deep learning methods for decoding non-invasive brain signals like EEG and MEG, focusing on visual stimuli and inner speech, and introduces novel models to handle variability and improve decoding accuracy.

Contribution

It presents new deep learning techniques, including Transformer-based models, for group decoding of brain signals and introduces a high-trial dataset for inner speech analysis.

Findings

Transformer models outperform traditional methods in signal modeling

Decoding inner speech remains highly challenging with mostly negative results

New methods improve handling of inter-subject variability in brain decoding

Abstract

This thesis delves into the world of non-invasive electrophysiological brain signals like electroencephalography (EEG) and magnetoencephalography (MEG), focusing on modelling and decoding such data. The research aims to investigate what happens in the brain when we perceive visual stimuli or engage in covert speech (inner speech) and enhance the decoding performance of such stimuli. The thesis is divided into two main sections, methodological and experimental work. A central concern in both sections is the large variability present in electrophysiological recordings, whether it be within-subject or between-subject variability, and to a certain extent between-dataset variability. In the methodological sections, we explore the potential of deep learning for brain decoding. We present advancements in decoding visual stimuli using linear models at the individual subject level. We then explore how deep learning techniques can be employed for group decoding, introducing new methods to deal with between-subject variability. Finally, we also explores novel forecasting models of MEG data based on convolutional and Transformer-based architectures. In particular, Transformer-based models demonstrate superior capabilities in generating signals that closely match real brain data, thereby enhancing the accuracy and reliability of modelling the brain's electrophysiology. In the experimental section, we present a unique dataset containing high-trial inner speech EEG, MEG, and preliminary optically pumped magnetometer (OPM) data. Our aim is to investigate different types of inner speech and push decoding performance by collecting a high number of trials and sessions from a few participants. However, the decoding results are found to be mostly negative, underscoring the difficulty of decoding inner speech.