12 papers
Physiological Noise Augmentation Improves Non-Invasive Brain-to-Speech
Benjamin Ballyk, Teyun Kwon, Miran Ãzdogan +1
Non-invasive brain-to-speech decoding aims to restore communication to patients suffering from neurodegenerative disease, without the risks of neurosurgery. Existing MEG- and EEG-b…
MEG-XL: Data-Efficient Brain-to-Text via Long-Context Pre-Training
Dulhan Jayalath, Oiwi Parker Jones
Clinical brain-to-text interfaces are designed for paralysed patients who cannot provide extensive training recordings. Pre-training improves data-efficient generalisation by learn…
A Systematic Evaluation of Sample-Level Tokenization Strategies for MEG Foundation Models
SungJun Cho, Chetan Gohil, Rukuang Huang +2
Recent success in natural language processing has motivated growing interest in large-scale foundation models for neuroimaging data. Such models often require discretization of con…
GPT2MEG: Quantizing MEG for Autoregressive Generation
Richard Csaky, Mats W. J. van Es, Oiwi Parker Jones +1
Foundation models trained with self-supervised objectives are increasingly applied to brain recordings, but autoregressive generation of realistic multichannel neural time series r…
MEGnifying Emotion: Sentiment Analysis from Annotated Brain Data
Brian Liu, Oiwi Parker Jones
Decoding emotion from brain activity could unlock a deeper understanding of the human experience. While a number of existing datasets align brain data with speech and with speech t…
Gated Uncertainty-Aware Runtime Dual Invariants for Neural Signal-Controlled Robotics
Tasha Kim, Oiwi Parker Jones
Safety-critical assistive systems that directly decode user intent from neural signals require rigorous guarantees of reliability and trust. We present GUARDIAN (Gated Uncertainty-…