3 papers
cs.LG2026
How Much Capacity Does EEG Denoising Need? Ultra-Compact Networks reveal Benchmark Saturation and Metric-Utility Gap
Jasmeet Singh Bindra, Siddharth Panwar
Deep learning EEG denoising architectures have scaled from tens of thousands to tens of millions of parameters, yet no prior study has isolated model capacity as the experimental v…
cs.LG2026
A spectral audit framework reveals task-dependent aperiodic reliance across EEG and ECG deep learning
Jasmeet Singh Bindra, Siddharth Panwar
Deep learning on physiological time series is interpreted through domain-specific features -- oscillatory rhythms in EEG, morphological complexes in ECG -- yet these signals sit at…
cs.LG2026
PRISM: Exploring Heterogeneous Pretrained EEG Foundation Model Transfer to Clinical Differential Diagnosis
Jeet Bandhu Lahiri, Parshva Runwal, Arvasu Kulkarni +4
EEG foundation models are typically pretrained on narrow-source clinical archives and evaluated on benchmarks from the same ecosystem, leaving unclear whether representations encod…