60 citations · 68 across the 20 of their papers we have counts for
7 papers · 1 filter
Scaling subjects in cross-modal alignment: video decoding with EEG foundation model
Dung Truong, Kuntal Kokate, Arnaud Delorme
Naturalistic visual decoding from EEG has long been constrained by cohort size: existing scaling literature caps out at a few dozen subjects, leading prior work to conclude that sc…
What masking geometry works best for EEG foundation models?
Pierre Guetschel, Bruno Aristimunha, Yassine El Ouahidi +3
EEG foundation models hold promise for scalable brain-signal decoding across clinical and cognitive neuroscience applications, yet their pre-training pipelines remain poorly unders…
EEGPrep: a validated Python implementation of the EEGLAB preprocessing pipeline
Arnaud Delorme, Suraj Ranganath, Christian Kothe +3
Objective. Automated EEG preprocessing is common in research and clinical work, but few pipelines have been tested systematically. In a recent benchmark, the default EEGLAB pipelin…
EEGDash: An open-source platform for machine learning on public neurophysiological data
Bruno Aristimunha, Aviv Dotan, Pierre Guetschel +8
Public recordings of electroencephalography (EEG), magnetoencephalography (MEG), intracranial EEG (iEEG), functional near-infrared spectroscopy (fNIRS) and electromyography (EMG) a…
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…
Channel Adaptation for EEG Foundation Models: A Systematic Benchmark Across Architectures, Tasks, and Training Regimes
Kuntal Kokate, Bruno Aristimunha, Dung Truong +1
Scaling EEG foundation models requires pooling data across heterogeneous electrode montages, a prerequisite both for larger pretraining corpora and for downstream deployment. We pr…
ENIGMA: EEG-to-Image in 15 Minutes Using Less Than 1% of the Parameters
Reese Kneeland, Wangshu Jiang, Ugo Bruzadin Nunes +3
To be practical for real-life applications, models for brain-computer interfaces must be easily and quickly deployable on new subjects, effective on affordable scanning hardware, a…