Decoding Language from Non-Invasive Brain Activity using Machine Learning

Research Proposal: Decoding Language from Non-Invasive Brain Activity using Machine Learning

My research focuses on advancing machine learning models to decode language from brain activity, specifically using non-invasive techniques such as functional Magnetic Resonance Imaging (fMRI) and Magnetoencephalography (MEG). Despite recent advancements, current models struggle with poor generalization to unseen linguistic contexts, especially across novel participants and diverse language inputs. This limitation highlights the need for more robust models capable of handling complex and variable brain data. Decoding language from brain signals presents significant challenges, because of the high-dimensional and noisy nature of the data, as well as substantial individual variability in brain responses. Therefore, my research specifically aims to address the following fundamental questions: How and why do brain signals differ between individuals? How can decoding models account for these individual differences while ensuring performance on unseen participants? I am particularly interested in developing methods that learn meaningful representations of participants, enabling the clustering and understanding of similar participants, improving our understanding of differences and the model's ability to generalize across diverse populations. Through this, I hope to contribute to the development of more accurate and adaptive brain decoding models that bridge the gap between brain activity and language.