1 citations · 2 across the 6 of their papers we have counts for
6 papers
SAMBA: Toward a Long-Context EEG Foundation Model via Spatial Embedding and Differential Mamba
Jiazhen Hong, Geoffrey Mackellar, Soheila Ghane
Long-sequence electroencephalogram (EEG) modeling is essential for developing generalizable EEG representation models. This need arises from the high sampling rate of EEG data and…
EEG-X: Device-Agnostic and Noise-Robust Foundation Model for EEG
Navid Mohammadi Foumani, Soheila Ghane, Nam Nguyen +3
Foundation models for EEG analysis are still in their infancy, limited by two key challenges: (1) variability across datasets caused by differences in recording devices and configu…
SpellerSSL: Self-Supervised Learning with P300 Aggregation for Speller BCIs
Jiazhen Hong, Geoff Mackellar, Soheila Ghane
Electroencephalogram (EEG)-based P300 speller brain-computer interfaces (BCIs) face three main challenges: low signal-to-noise ratio (SNR), poor generalization, and time-consuming…
An Efficient Self-Supervised Framework for Long-Sequence EEG Modeling
Jiazhen Hong, Geoffrey Mackellar, Soheila Ghane
Electroencephalogram (EEG) signals generally exhibit low signal-to-noise ratio (SNR) and high inter-subject variability, making generalization across subjects and domains challengi…
EEG2Rep: Enhancing Self-supervised EEG Representation Through Informative Masked Inputs
Navid Mohammadi Foumani, Geoffrey Mackellar, Soheila Ghane +3
Self-supervised approaches for electroencephalography (EEG) representation learning face three specific challenges inherent to EEG data: (1) The low signal-to-noise ratio which cha…
Detecting Driver's Distraction using Long-term Recurrent Convolutional Network
Chang Wei Tan, Mahsa Salehi, Geoffrey Mackellar
In this study we demonstrate a novel Brain Computer Interface (BCI) approach to detect driver distraction events to improve road safety. We use a commercial wireless headset that g…