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eess.SP2026
Neuron-Level Architecture Growth: A Controlled Evaluation for EEG Time-Series Decoding
Adam Mounir, Stella Douka, Arnault H. Caillet +2
Convolutional EEG decoders are trained at a fixed width, usually set by their authors on other data. Growing methods add neurons during training where the loss could decrease the m…
eess.SP2026
Automated Artifact Removal in EEG Age Prediction: A systematic comparison
Davoud Hajhassani, Paul-Adrien Graignic, Bruno Aristimunha +8
Automated EEG artifact removal may improve downstream analysis but can also alter predictive information. We benchmarked nine automated artifact removal methods against a common no…
eess.SP2026
From EEG Cleaning to Decoding: The Role of Artifact Rejection in MI-based BCIs
Davoud Hajhassani, Bruno Aristimunha, Paul-Adrien Graignic +5
Motor imagery (MI) BCIs are sensitive to EEG artifacts, yet the practical impact of automated artifact rejection on downstream MI decoding performance remains unclear. While most w…