Neuron-Level Architecture Growth: A Controlled Evaluation for EEG Time-Series Decoding

eess.SP, cs.HC, cs.LG, cs.NE

Submitted: 2026-09-27

Updated: 2026-09-27

Comments: 5 pages, 3 figures, 2 tables. Submitted to ICASSP 2027

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Sources

• Are Large Brainwave Foundation Models Capable Yet? Insights from Fine-tuning
• The largest EEG-based BCI reproducibility study for open science: the MOABB benchmark
• Evaluating the structure of cognitive tasks with transfer learning
• Cross Task Neural Architecture Search for EEG Signal Classifications
• Deep comparisons of Neural Networks from the EEGNet family
• When To Grow? A Fitting Risk-Aware Policy for Layer Growing in Deep Neural Networks

Related papers

• What If They Took the Shot? A Hierarchical Bayesian Framework for Counterfactual Expected Goals
• Runtime Assurance Under Measurement Attack: Necessary and Sufficient Observability Conditions for Learned Control in Radio Access Networks
• Physics-Constrained Deep Learning Model for Contactless Blood Pressure Monitoring from Triaxial Bodyseismography
• SpellerSSL: Self-Supervised Learning with P300 Aggregation for Speller BCIs
• Uncertainty Quantification in Machine Learning for Biosignal Applications -- A Review
• Continuous Orthogonal Mode Decomposition: Haptic Signal Prediction in Tactile Internet