AI RESEARCH
DLink: Distilling Layer-wise and Dominant Knowledge from EEG Foundation Models
arXiv CS.LG
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ArXi:2604.15016v1 Announce Type: new EEG foundation models (FMs) achieve strong cross-subject and cross-task generalization but impose substantial computational and memory costs that hinder deployment on embedded BCI systems. Knowledge distillation is a natural solution; however, conventional methods fail for EEG FMs because task-relevant semantics are often distributed across intermediate layers, and aggressive dimensionality reduction can distort oscillatory structure via representational collapse and aliasing.