Hamza Abdelhedi

Intrinsic Neural Oscillations Predict Verbal Learning Performance and Encoding Strategy Use

Victor Oswald

Mathieu Landry

Sarah Lippé

Philippe Robaey

2025-09-28

bioRxiv (prépublication)

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

Yorguin 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.

2025-06-11

ArXiv (prépublication)

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

Yorguin 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.

2025-06-11

ArXiv (prépublication)

Artificial Neural Networks for Magnetoencephalography: A review of an emerging field

Vanessa Hadid

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.

2025-05-27

Journal of Neural Engineering (publié)

Artificial Neural Networks for Magnetoencephalography: A review of an emerging field

Vanessa Hadid

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.

2025-01-20

ArXiv (prépublication)

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

Charlotte Maschke

Arthur Dehgan

Yann Harel

Anirudha Kemtur

Loubna Mekki Berrada

Myriam Sahraoui

Tammy Young

Antoine Bellemare‐Pepin

Clara El Khantour

Mathieu Landry

Annalisa Pascarella

Vanessa Hadid

Etienne Combrisson

Jordan O’Byrne

2023-08-01

NeuroImage (publié)