Sep 19, 2024 · FoME is pre-trained on a diverse 1.7TB dataset of scalp and intracranial EEG recordings, comprising 745M parameters trained for 1,096k steps.
Sep 19, 2024 · FoME is pre-trained on a diverse 1.7TB dataset of scalp and intracranial EEG recordings, comprising 745M parameters trained for 1,096k steps.
Sep 22, 2024 · Our model introduces two key innovations: a time-frequency fusion embedding technique and an adaptive time-lateral attention scaling (ATLAS) ...
Sep 19, 2024 · A new deep learning model that can analyze EEG brain data. EEG measures electrical activity in the brain using electrodes on the scalp.
Oct 17, 2024 · Bibliographic details on FoME: A Foundation Model for EEG using Adaptive Temporal-Lateral Attention Scaling.
Sep 20, 2024 · FoME: A Foundation Model for EEG using Adaptive Temporal-Lateral Attention Scaling. https://arxiv.org/abs/2409.12454.
FoME: A Foundation Model for EEG using Adaptive Temporal-Lateral Attention Scaling ... Electroencephalography (EEG) is a vital tool to measure and record ...
FoME: A Foundation Model for EEG using Adaptive Temporal-Lateral Attention Scaling ... Electroencephalography (EEG) is a vital tool to measure and record ...
In this paper, we propose FoME (Foundation Model for EEG), a novel approach using adaptive temporal-lateral attention scaling to address above-mentioned ...
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A unified foundation model for EEG called Large Brain Model (LaBraM), which enables cross-dataset learning by segmenting the EEG signals into EEG channel ...