53 citations · 53 across the 15 of their papers we have counts for
6 papers · 1 filter
Test-Time Adaptation for EEG Foundation Models: A Systematic Study under Real-World Distribution Shifts
Gabriel Jason Lee, Jathurshan Pradeepkumar, Jimeng Sun
Electroencephalography (EEG) foundation models have shown strong potential for learning generalizable representations from large-scale neural data, yet their clinical deployment is…
Making Conformal Predictors Robust in Healthcare Settings: a Case Study on EEG Classification
Arjun Chatterjee, Sayeed Sajjad Razin, John Wu +3
Quantifying uncertainty in clinical predictions is critical for high-stakes diagnosis tasks. Conformal prediction offers a principled approach by providing prediction sets with the…
Neural Signals Generate Clinical Notes in the Wild
Jathurshan Pradeepkumar, Zheng Chen, Jimeng Sun
Generating clinical reports that summarize abnormal patterns, diagnostic findings, and clinical interpretations from long-term EEG recordings remains labor-intensive. We present CE…
PyHealth 2.0: A Comprehensive Open-Source Toolkit for Accessible and Reproducible Clinical Deep Learning
John Wu, Yongda Fan, Zhenbang Wu +14
Difficulty replicating baselines, high computational costs, and required domain expertise create persistent barriers to clinical AI research. To address these challenges, we introd…
Tokenizing Single-Channel EEG with Time-Frequency Motif Learning
Jathurshan Pradeepkumar, Xihao Piao, Zheng Chen +1
Foundation models are reshaping EEG analysis, yet an important problem of EEG tokenization remains a challenge. This paper presents TFM-Tokenizer, a novel tokenization framework th…
Toward Interpretable Sleep Stage Classification Using Cross-Modal Transformers
Jathurshan Pradeepkumar, Mithunjha Anandakumar, Vinith Kugathasan +4
Accurate sleep stage classification is significant for sleep health assessment. In recent years, several machine-learning based sleep staging algorithms have been developed , and i…