3 papers
cs.LG2026
Multi-Subject Pretraining Enables Short-Calibration Personalization for Closed-Corpus Surface EMG Speech Decoding
Chenqian Le, Beatrice Fumagalli, Yasamin Esmaeili +5
Surface electromyography (sEMG)-based silent speech interfaces are limited by cross-user variability and calibration burden. We study a limited-data setting in which each of 27 spe…
cs.CV2025
VoxelFormer: Parameter-Efficient Multi-Subject Visual Decoding from fMRI
Chenqian Le, Yilin Zhao, Nikasadat Emami +4
Recent advances in fMRI-based visual decoding have enabled compelling reconstructions of perceived images. However, most approaches rely on subject-specific training, limiting scal…
cs.LG2025
Machine Learning-Based Prediction of Speech Arrest During Direct Cortical Stimulation Mapping
Nikasadat Emami, Amirhossein Khalilian-Gourtani, Jianghao Qian +4
Identifying cortical regions critical for speech is essential for safe brain surgery in or near language areas. While Electrical Stimulation Mapping (ESM) remains the clinical gold…