6 papers
Integrating Language Models into Listened and Imagined Speech Decoding from MEG
Maryam Maghsoudi, Sai Samrat Kankanala, Shihab A. Shamma +1
Decoding imagined speech is an important goal for brain-computer interfaces but remains challenging due to weak neural responses, low signal-to-noise ratio, and limited imagined-sp…
Mechanistic Interpretability Reveals Shared Causal Subspaces in Brain-to-Speech Decoders
Maryam Maghsoudi, Ayushi Mishra, Sanghamitra Dutta
Decoding covert speech, such as mimed or imagined, from brain activity is harder than decoding vocalized speech. Cross-modal transfer, where information from one speech form helps…
Zero-Shot Imagined Speech Decoding via Imagined-to-Listened MEG Mapping
Maryam Maghsoudi, Shihab Shamma
Decoding imagined speech from non-invasive brain recordings is challenging because imagined datasets are scarce and difficult to align temporally across subjects and sessions In th…
Relating the Neural Representations of Vocalized, Mimed, and Imagined Speech
Maryam Maghsoudi, Rupesh Chillale, Shihab A. Shamma
We investigated the relationship among neural representations of vocalized, mimed, and imagined speech recorded using publicly available stereotactic EEG recordings. Most prior stu…
Mechanistic Interpretability of Brain-to-Speech Models Across Speech Modes
Maryam Maghsoudi, Ayushi Mishra
Brain-to-speech decoding models demonstrate robust performance in vocalized, mimed, and imagined speech; yet, the fundamental mechanisms via which these models capture and transmit…
A Convolutional Framework for Mapping Imagined Auditory MEG into Listened Brain Responses
Maryam Maghsoudi, Mohsen Rezaeizadeh, Shihab Shamma
Decoding imagined speech engages complex neural processes that are difficult to interpret due to uncertainty in timing and the limited availability of imagined-response datasets. I…