Philipp Thölke

Yann Harel

Kory Mathewson

Jay A. Olson

Yoshua Bengio

Psychology Department

U. Montr'eal

Montreal

Qc

Canada

Music department

C. University

Sociology

Anthropology department

Mila

Departmentof Psychology

University of Toronto Mississauga … (see 5 more)

Mississauga

On

Department of Computer Science

Operations Research

Unique Center

The recent surge of Large Language Models (LLMs) has led to claims that they are approaching a level of creativity akin to human capabilitie… (see more)s. This idea has sparked a blend of excitement and apprehension. However, a critical piece that has been missing in this discourse is a systematic evaluation of LLMs’ semantic diversity, particularly in comparison to human divergent thinking. To bridge this gap, we leverage recent advances in computational creativity to analyze semantic divergence in both state-of-the-art LLMs and a substantial dataset of 100,000 humans. These divergence-based measures index associative thinking—the ability to access and combine remote concepts in semantic space—an established facet of creative cognition. We benchmark performance on the Divergent Association Task (DAT) and across multiple creative-writing tasks (haiku, story synopses, and flash fiction), using identical, objective scoring. We found evidence that LLMs can surpass average human performance on the DAT, and approach human creative writing abilities, yet they remain below the mean creativity scores observed among the more creative segment of human participants. Notably, even the top performing LLMs are still largely surpassed by the aggregated top half of human participants, underscoring a ceiling that current LLMs still fail to surpass. We also systematically varied linguistic strategy prompts and temperature, observing reliable gains in semantic divergence for several models. Our human-machine benchmarking framework addresses the polemic surrounding the imminent replacement of human creative labor by AI, disentangling the quality of the respective creative linguistic outputs using established objective measures. While prompting deeper exploration of the distinctive elements of human inventive thought compared to those of AI systems, we lay out a series of techniques to improve their outputs with respect to semantic diversity, such as prompt design and hyper-parameter tuning.

2026-01-20

Scientific Reports (published)

arxiv.org

Meditation induces shifts in neural oscillations, brain complexity, and critical dynamics: novel insights from MEG

Annalisa Pascarella

David Meunier

Jordan O'Byrne

Tarek Lajnef

Antonino Raffone

Roberto Guidotti

Vittorio Pizzella

Laura Marzetti

While the beneficial impacts of meditation are increasingly acknowledged, its underlying neural mechanisms remain poorly understood. We exam… (see more)ined the electrophysiological brain signals of expert Buddhist monks during two established meditation methods known as Samatha and Vipassana, which employ focused attention and open-monitoring technique. By combining source-space magnetoencephalography with advanced signal processing and machine learning tools, we provide an unprecedented assessment of the role of brain oscillations, complexity, and criticality in meditation. In addition to power spectral density, we computed long-range temporal correlations (LRTC), deviation from criticality coefficient (DCC), Lempel–Ziv complexity, 1/f slope, Higuchi fractal dimension, and spectral entropy. Our findings indicate increased levels of neural signal complexity during both meditation practices compared to the resting state, alongside widespread reductions in gamma-band LRTC and 1/f slope. Importantly, the DCC analysis revealed a separation between Samatha and Vipassana, suggesting that their distinct phenomenological properties are mediated by specific computational characteristics of their dynamic states. Furthermore, in contrast to most previous reports, we observed a decrease in oscillatory gamma power during meditation, a divergence likely due to the correction of the power spectrum by the 1/f slope, which could reduce potential confounds from broadband 1/f activity. We discuss how these results advance our comprehension of the neural processes associated with focused attention and open-monitoring meditation practices.

2025-11-22

Neuroscience of Consciousness (published)

Oneiris: An AI-augmented Brain-Computer Interface for Exploring Personal and Collective Dreamscapes

Antoine Bellemare‐Pepin

Karim Jerbi CoCo Lab

Suzanne Kite

2025-11-04

Proceedings of the Conference on Animation and Interactive Art (published)

REVE: A Foundation Model for EEG -- Adapting to Any Setup with Large-Scale Pretraining on 25,000 Subjects

Yassine El Ouahidi

Jonathan Lys

Nicolas Farrugia

Bastien Pasdeloup

Vincent Gripon

Karim Jerbi CoCo Lab

Giulia Lioi

2025-10-23

ArXiv (preprint)

arxiv.org

Caffeine induces age-dependent increases in brain complexity and criticality during sleep

Maxine Arcand-Lavigne

Tarek Lajnef

Sonia Frenette

Julie Carrier

Caffeine is the most widely consumed psychoactive stimulant worldwide. Yet important gaps persist in understanding its effects on the brain,… (see more) especially during sleep. We analyzed sleep electroencephalography (EEG) in 40 subjects, contrasting 200 mg of caffeine against a placebo condition, utilizing inferential statistics and machine learning. We found that caffeine ingestion led to an increase in brain complexity, a widespread flattening of the power spectrum’s 1/f-like slope, and a reduction in long-range temporal correlations. Being most prominent during non-rapid eye movement (NREM) sleep, these results suggest that caffeine shifts the brain towards a critical regime and more diverse neural dynamics. Interestingly, this was more pronounced in younger adults (20–27 years) compared to middle-aged participants (41–58 years) during rapid eye movement (REM) sleep, while no significant age effects were observed during NREM. Interpreting these data in the light of modeling and empirical work on EEG-derived measures of excitation-inhibition balance suggests that caffeine promotes a shift in brain dynamics towards increased neural excitation and closer proximity to a critical regime, particularly during NREM sleep.

2025-04-29

Communications Biology (published)

Neuro-GPT: Towards A Foundation Model for EEG

Wenhui Cui

Woojae Jeong

Takfarinas Medani

Karim Jerbi CoCo Lab

Anand A. Joshi

Richard M. Leahy

To handle the scarcity and heterogeneity of electroencephalography (EEG) data for Brain-Computer Interface (BCI) tasks, and to harness the p… (see more)ower of large publicly available data sets, we propose Neuro-GPT, a foundation model consisting of an EEG encoder and a GPT model. The foundation model is pre-trained on a large-scale data set using a self-supervised task that learns how to reconstruct masked EEG segments. We then fine-tune the model on a Motor Imagery Classification task to validate its performance in a low-data regime (9 subjects). Our experiments demonstrate that applying a foundation model can significantly improve classification performance compared to a model trained from scratch, which provides evidence for the generalizability of the foundation model and its ability to address challenges of data scarcity and heterogeneity in EEG. The code is publicly available at github.com/wenhui0206/NeuroGPT.

2024-05-26

2024 IEEE International Symposium on Biomedical Imaging (ISBI) (published)

arxiv.org

Bio-Mechanical Poet: An Immersive Audiovisual Playground for Brain Signals and Generative AI.

Antoine Bellemare‐Pepin

Yann Harel

François Lespinasse

Karim Jerbi CoCo Lab

2023-12-31

ICCC (published)

dblp.uni-trier.de

Class imbalance should not throw you off balance: Choosing the right classifiers and performance metrics for brain decoding with imbalanced data

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

Machine learning (ML) is increasingly used in cognitive, computational and clinical neuroscience. The reliable and efficient application of … (see more)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 (published)