This primer explains how EEG/MEG activity can be organized into neural state representations, projected onto axes organized by decreasing covariance, truncated into low-dimensional representations, and visualized as trajectories that capture how distributed activity evolves over time.
Vanessa Hadid, Hamza Abdelhedi, A. Pascarella et al.· 0 citations
Summary Individuals adopt different encoding strategies to facilitate learning, yet few studies have examined the neurophysiological basis of these strategies across individuals. The present work addresses this gap by extending our previous findings on the direct relationship between cortical spectral power, measured v...
Victor Oswald, Mathieu Landry, Hamza Abdelhedi et al.· iScience· 0 citations
How neural activity across the ventral visual hierarchy supports face recognition is an open question. A long-standing debate asks whether face processing, particularly in fusiform cortex, relies on face-specific computations or representations shared with broader visual recognition. Here we combine source-resolved mag...
Hamza Abdelhedi, Shahab Bakhtiari, Karim Jerbi· bioRxiv· 0 citations
This perspective lays down the basic concepts needed to understand MEG foundation models and provides a didactic overview of the field's key design choices, including tokenization, sensor- versus source-space representations, sensor-geometry encoding, backbone architectures, self-supervised objectives, and pretraining...
Philipp Thölke, Hamza Abdelhedi, Yorguin-José Mantilla-Ramos et al.· 1 citation
The ambition of the 2025 PNPL competition (Landau et al., 2025) was to launch a multi-year curriculum for non-invasive speech decoding. Designed to progress from foundational tasks toward the linguistic complexity required for a practical brain-computer interface (BCI), it set the stage with speech detection and phonem...
Francesco Mantegna, Gereon Elvers, D. Jayalath et al.· 0 citations
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