Portrait de Hamza Abdelhedi

Hamza Abdelhedi

Doctorat - UdeM
Superviseur⋅e principal⋅e
Karim Jerbi
Sujets de recherche
Apprentissage automatique médical
Apprentissage de représentations
Apprentissage profond
IA inspirée du cerveau
Imagerie fonctionnelle du cerveau
Modèles de fondation
NeuroIA
Neurosciences
Neurosciences computationnelles
Science cognitive
Site web
Google Scholar
GitHub

Publications

Coord2Region: A Python Package for Mapping 3D Brain Coordinates to Atlas Labels, Literature, and AI Summaries
Hamza Abdelhedi
Yorguin-Jose Mantilla-Ramos
Sina Esmaeili
Annalisa Pascarella
Vanessa Hadid
Karim Jerbi CoCo Lab
We present Coord2Region, an open-source Python package that streamlines coordinate-based neuroimaging workflows by automatically mapping 3D … (voir plus)brain coordinates (e.g., MNI or Talairach) to anatomical regions across multiple atlases. The package links mapped coordinates to meta-analytic resources via the Neuroimaging Meta-Analysis Research Environment (NiMARE) , providing direct integration with Neurosynth and NeuroQuery. This directly connects coordinates and regions to the broader neuroimaging literature. In addition to atlas-based labeling and literature retrieval, Coord2Region offers an optional large language model (LLM) functionality that generates text summaries of linked studies and illustrative images of queried regions. These AI-assisted features are intended to support interpretation and exploration, while remaining clearly complementary to peer-reviewed literature and established neuroimaging tools. Coord2Region provides a unified pipeline with a robust command-line interface, flexible dataset management, and provider-agnostic LLM utilities, and it supports both single-coordinate and high-throughput batch queries with nearest-region fallback for volume and surface atlases. Furthermore, Coord2Region includes a web interface for interactive configuration (via JSON Schema forms) and cloud execution (via Hugging Face), enabling users to build YAML configurations and run analyses in-browser without local installation. Together, these capabilities lower friction, reduce manual errors, and improve reproducibility in coordinate-centric neuroimaging workflows, promoting more robust and transparent research practices.
2025-12-19
ArXiv (prépublication)
arxiv.org
arxiv.org
Intrinsic Neural Oscillations Predict Verbal Learning Performance and Encoding Strategy Use
Victor Oswald
Mathieu Landry
Hamza Abdelhedi
Sarah Lippé
Philippe Robaey
Karim Jerbi
2025-09-27
BioRxiv (prépublication)
doi.org
The 2025 PNPL Competition: Speech Detection and Phoneme Classification in the LibriBrain Dataset
Gilad Landau
Miran Ozdogan
Gereon Elvers
Francesco Mantegna
Pratik Somaiya
Dulhan Hansaja Jayalath
Luisa Kurth
Teyun Kwon
Brendan Shillingford
Greg Farquhar
Minqi Jiang
Karim Jerbi CoCo Lab
Hamza Abdelhedi
Yorguin José Mantilla Ramos
Caglar Gulçehre
M. Woolrich
Natalie Voets
Oiwi Parker Jones
The advance of speech decoding from non-invasive brain data holds the potential for profound societal impact. Among its most promising appli… (voir plus)cations is the restoration of communication to paralysed individuals affected by speech deficits such as dysarthria, without the need for high-risk surgical interventions. The ultimate aim of the 2025 PNPL competition is to produce the conditions for an"ImageNet moment"or breakthrough in non-invasive neural decoding, by harnessing the collective power of the machine learning community. To facilitate this vision we present the largest within-subject MEG dataset recorded to date (LibriBrain) together with a user-friendly Python library (pnpl) for easy data access and integration with deep learning frameworks. For the competition we define two foundational tasks (i.e. Speech Detection and Phoneme Classification from brain data), complete with standardised data splits and evaluation metrics, illustrative benchmark models, online tutorial code, a community discussion board, and public leaderboard for submissions. To promote accessibility and participation the competition features a Standard track that emphasises algorithmic innovation, as well as an Extended track that is expected to reward larger-scale computing, accelerating progress toward a non-invasive brain-computer interface for speech.
2025-06-10
ArXiv (prépublication)
doi.org
arxiv.org
Artificial Neural Networks for Magnetoencephalography: A review of an emerging field
Arthur Dehgan
Hamza Abdelhedi
Vanessa Hadid
Irina Rish
Karim Jerbi CoCo Lab
Magnetoencephalography (MEG) is a cutting-edge neuroimaging technique that measures the intricate brain dynamics underlying cognitive proces… (voir plus)ses with an unparalleled combination of high temporal and spatial precision. MEG data analytics has always relied on advanced signal processing and mathematical and statistical tools for various tasks ranging from data cleaning to probing the signals' rich dynamics and estimating the neural sources underlying the surface-level recordings. Like in most domains, the surge in Artificial Intelligence (AI) has led to the increased use of Machine Learning (ML) methods for MEG data classification. More recently, an emerging trend in this field is using Artificial Neural Networks (ANNs) to address many MEG-related tasks. This review provides a comprehensive overview of how ANNs are being used with MEG data from three vantage points: First, we review work that employs ANNs for MEG signal classification, i.e., for brain decoding. Second, we report on work that has used ANNs as putative models of information processing in the human brain. Finally, we examine studies that use ANNs as techniques to tackle methodological questions in MEG, including artifact correction and source estimation. Furthermore, we assess the current strengths and limitations of using ANNs with MEG and discuss future challenges and opportunities in this field. Finally, by establishing a detailed portrait of the field and providing practical recommendations for the future, this review seeks to provide a helpful reference for both seasoned MEG researchers and newcomers to the field who are interested in using ANNs to enhance the exploration of the complex dynamics of the human brain with MEG.
2025-05-26
Journal of Neural Engineering (publié)
doi.org
arxiv.org
Class imbalance should not throw you off balance: Choosing the right classifiers and performance metrics for brain decoding with imbalanced data
Philipp Thölke
Yorguin-Jose Mantilla-Ramos
Hamza Abdelhedi
Charlotte Maschke
Arthur Dehgan
Yann Harel
Anirudha Kemtur
Loubna Mekki Berrada
Myriam Sahraoui
Tammy Young
Antoine Bellemare Pépin
Clara El Khantour
Mathieu Landry
Annalisa Pascarella
Vanessa Hadid
Etienne Combrisson
Jordan O'Byrne
Karim Jerbi
Machine learning (ML) is increasingly used in cognitive, computational and clinical neuroscience. The reliable and efficient application of … (voir plus)ML requires a sound understanding of its subtleties and limitations. Training ML models on datasets with imbalanced classes is a particularly common problem, and it can have severe consequences if not adequately addressed. With the neuroscience ML user in mind, this paper provides a didactic assessment of the class imbalance problem and illustrates its impact through systematic manipulation of data imbalance ratios in (i) simulated data and (ii) brain data recorded with electroencephalography (EEG), magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI). Our results illustrate how the widely-used Accuracy (Acc) metric, which measures the overall proportion of successful predictions, yields misleadingly high performances, as class imbalance increases. Because Acc weights the per-class ratios of correct predictions proportionally to class size, it largely disregards the performance on the minority class. A binary classification model that learns to systematically vote for the majority class will yield an artificially high decoding accuracy that directly reflects the imbalance between the two classes, rather than any genuine generalizable ability to discriminate between them. We show that other evaluation metrics such as the Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC), and the less common Balanced Accuracy (BAcc) metric - defined as the arithmetic mean between sensitivity and specificity, provide more reliable performance evaluations for imbalanced data. Our findings also highlight the robustness of Random Forest (RF), and the benefits of using stratified cross-validation and hyperprameter optimization to tackle data imbalance. Critically, for neuroscience ML applications that seek to minimize overall classification error, we recommend the routine use of BAcc, which in the specific case of balanced data is equivalent to using standard Acc, and readily extends to multi-class settings. Importantly, we present a list of recommendations for dealing with imbalanced data, as well as open-source code to allow the neuroscience community to replicate and extend our observations and explore alternative approaches to coping with imbalanced data.
2023-07-31
NeuroImage (publié)
doi.org