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cs.LG2025
SAMBA: Toward a Long-Context EEG Foundation Model via Spatial Embedding and Differential Mamba
Jiazhen Hong, Geoffrey Mackellar, Soheila Ghane
Long-sequence electroencephalogram (EEG) modeling is essential for developing generalizable EEG representation models. This need arises from the high sampling rate of EEG data and…
cs.LG2025
EEG-X: Device-Agnostic and Noise-Robust Foundation Model for EEG
Navid Mohammadi Foumani, Soheila Ghane, Nam Nguyen +3
Foundation models for EEG analysis are still in their infancy, limited by two key challenges: (1) variability across datasets caused by differences in recording devices and configu…
cs.LG2025
An Efficient Self-Supervised Framework for Long-Sequence EEG Modeling
Jiazhen Hong, Geoffrey Mackellar, Soheila Ghane
Electroencephalogram (EEG) signals generally exhibit low signal-to-noise ratio (SNR) and high inter-subject variability, making generalization across subjects and domains challengi…