Deep learning with convolutional neural networks for EEG decoding and visualization

Deep learning-based electroencephalography analysis: a systematic review

CONTEXT: Electroencephalography (EEG) is a complex signal and can require several years of training, as well as advanced signal processing and feature extraction methodologies to be correctly interpreted. Recently, deep learning (DL) has shown great promise in helping make sense of EEG signals due to its capacity to learn good feature representations from raw data. Whether DL truly presents advantages as compared to more traditional EEG processing approaches, however, remains an open question. OBJECTIVE: In this work, we review 154 papers that apply DL to EEG, published between January 2010 and July 2018, and spanning different application domains such as epilepsy, sleep, brain-computer interfacing, and cognitive and affective monitoring. We extract trends and highlight interesting approaches from this large body of literature in order to inform future research and formulate recommendations. METHODS: Major databases spanning the fields of science and engineering were queried to identify relevant studies published in scientific journals, conferences, and electronic preprint repositories. Various data items were extracted for each study pertaining to (1) the data, (2) the preprocessing methodology, (3) the DL design choices, (4) the results, and (5) the reproducibility of the experiments. These items were then analyzed one by one to uncover trends. RESULTS: Our analysis reveals that the amount of EEG data used across studies varies from less than ten minutes to thousands of hours, while the number of samples seen during training by a network varies from a few dozens to several millions, depending on how epochs are extracted. Interestingly, we saw that more than half the studies used publicly available data and that there has also been a clear shift from intra-subject to inter-subject approaches over the last few years. About [Formula: see text] of the studies used convolutional neural networks (CNNs), while [Formula: see text] used recurrent neural networks (RNNs), most often with a total of 3-10 layers. Moreover, almost one-half of the studies trained their models on raw or preprocessed EEG time series. Finally, the median gain in accuracy of DL approaches over traditional baselines was [Formula: see text] across all relevant studies. More importantly, however, we noticed studies often suffer from poor reproducibility: a majority of papers would be hard or impossible to reproduce given the unavailability of their data and code. SIGNIFICANCE: To help the community progress and share work more effectively, we provide a list of recommendations for future studies and emphasize the need for more reproducible research. We also make our summary table of DL and EEG papers available and invite authors of published work to contribute to it directly. A planned follow-up to this work will be an online public benchmarking portal listing reproducible results.

Deep learning-based electroencephalography analysis: a systematic review

Deep learning-based electroencephalography analysis: a systematic review, Roy, Yannick, Banville, Hubert, Albuquerque, Isabela, Gramfort, Alexandre, Falk, Tiago H, Faubert, Jocelyn

#neuroscience #ai #python #opensource #neuroai | Jean-Rémi King | 34 comments

⚡ We're happy to release NeuralSet: a fast, simple, and scalable Python framework for Neuro-AI. Already supports: 🧠 fMRI, EEG, MEG, iEEG, spikes… recordings 💬 text, 🔊 audio, ▶️ video, 🏞️ image… embeddings 📦 `pip install neuralset` 💻 Code: https://lnkd.in/eamwxBUY 📄 Paper: https://lnkd.in/epbreyDy Made possible thanks to: Hubert Banville, Katie Begany, Corentin Bel, Yohann Benchetrit, Teon Brooks, Marlène Careil, Simon Dahan, Stéphane d'Ascoli, Alexandre Défossez, Linnea Evanson, PhD, Pablo J. Diego Simón, Julien Gadonneix, Sophia Houhamdi, Shubh Khanna, Jarod Lévy, Pierre Orhan, Antoine Ratouchniak, Joséphine Raugel, Andrea Elena Santos Revilla, Alexis Thual, Lucy (Mingfang) Zhang, Jérémy RAPIN #Neuroscience #AI #Python #OpenSource #NeuroAI | 34 comments on LinkedIn

#neuroscience #ai #python #opensource #neuroai | Jean-Rémi King | 34 comments

⚡ We're happy to release NeuralSet: a fast, simple, and scalable Python framework for Neuro-AI. Already supports: 🧠 fMRI, EEG, MEG, iEEG, spikes… recordings 💬 text, 🔊 audio, ▶️ video, 🏞️ image… embeddings 📦 `pip install neuralset` 💻 Code: https://lnkd.in/eamwxBUY 📄 Paper: https://lnkd.in/epbreyDy Made possible thanks to: Hubert Banville, Katie Begany, Corentin Bel, Yohann Benchetrit, Teon Brooks, Marlène Careil, Simon Dahan, Stéphane d'Ascoli, Alexandre Défossez, Linnea Evanson, PhD, Pablo J. Diego Simón, Julien Gadonneix, Sophia Houhamdi, Shubh Khanna, Jarod Lévy, Pierre Orhan, Antoine Ratouchniak, Joséphine Raugel, Andrea Elena Santos Revilla, Alexis Thual, Lucy (Mingfang) Zhang, Jérémy RAPIN #Neuroscience #AI #Python #OpenSource #NeuroAI | 34 comments on LinkedIn

#neuroscience #ai #python #opensource #neuroai | Jean-Rémi King

⚡ We're happy to release NeuralSet: a fast, simple, and scalable Python framework for Neuro-AI. Already supports: 🧠 fMRI, EEG, MEG, iEEG, spikes… recordings 💬 text, 🔊 audio, ▶️ video, 🏞️ image… embeddings 📦 `pip install neuralset` 💻 Code: https://lnkd.in/eamwxBUY 📄 Paper: https://lnkd.in/epbreyDy Made possible thanks to: Hubert Banville, Katie Begany, Corentin Bel, Yohann Benchetrit, Teon Brooks, Marlène Careil, Simon Dahan, Stéphane d'Ascoli, Alexandre Défossez, Linnea Evanson, PhD, Pablo J. Diego Simón, Julien Gadonneix, Sophia Houhamdi, Shubh Khanna, Jarod Lévy, Pierre Orhan, Antoine Ratouchniak, Joséphine Raugel, Andrea Elena Santos Revilla, Alexis Thual, Lucy (Mingfang) Zhang, Jérémy RAPIN #Neuroscience #AI #Python #OpenSource #NeuroAI

nubrain - A foundation model for neural decoding

Building the world's largest dataset of human brain activity.

#brainfoundationmodels #neuroai #eeg #meg #fmri #neuroscience #machinelearning #opensource #fair | Jarod Lévy

Today, the Brain&AI team announces a new release with NeuralBench. 📊📊📊 A unified framework for benchmarking foundation models of brain activity. This wouldn’t have been possible without the tremendous work of Hubert Banville 🎉 🎉 🎉 - 🧠 36 EEG tasks - 🗄️ 94 public datasets. - 💻 Code: https://lnkd.in/dPDMwja3 - 📄 Paper: https://lnkd.in/ddDU2p6d This package allows systematic evaluation of any foundation models. NeuralBench addresses this by defining each task end-to-end with config files (data source, preprocessing, splits, optimizer, metrics, architecture) so all models can be evaluated on the same footing. We invite the community to contribute new tasks, datasets, and models, especially for fMRI, MEG, and iEEG. The long-term goal is a fully unified benchmark across neuroimaging tasks and modalities. What's in the first release, NeuralBench-EEG v1.0: - 36 EEG tasks across 94 public datasets, spanning motor imagery, clinical classification, cognitive decoding, and phenotype prediction. - Task-specific deep learning architectures (EEGNet, Deep4, EEGConformer, CTNet, ...) benchmarked side-by-side with recent EEG foundation models (BENDR, LaBraM, BIOT, CBraMod, LUNA, REVE). - Extensible to other neuroimaging modalities: the framework already runs MEG and fMRI tasks, leveraging our NeuralSet ecosystem for accessing brain imaging data and the broader neuroscientific software stack. - Released under the MIT license. Big team effort with Stéphane d'Ascoli, Simon Dahan, Jérémy RAPIN, Marlène Careil, Yohann Benchetrit, Saarang P., Antoine Ratouchniak, Lucy (Mingfang) Zhang, Elisa Cascardi, Katie Begany Teon Brooks, and Jean-Rémi King. And special thanks: Alexandre Gramfort Thomas Moreau Arnaud Delorme Bruno A. Pierre Guetschel #BrainFoundationModels #NeuroAI #EEG #MEG #fMRI #Neuroscience #MachineLearning #OpenSource #FAIR

Distilling Dataset into Neural Field

Utilizing a large-scale dataset is essential for training high-performance deep learning models, but it also comes with substantial computation and storage costs. To overcome these challenges, dataset distillation has emerged as a promising solution by compressing the large-scale dataset into a smaller synthetic dataset that retains the essential information needed for training. This paper proposes a novel parameterization framework for dataset distillation, coined Distilling Dataset into Neural Field (DDiF), which leverages the neural field to store the necessary information of the large-scale dataset. Due to the unique nature of the neural field, which takes coordinates as input and output quantity, DDiF effectively preserves the information and easily generates various shapes of data. We theoretically confirm that DDiF exhibits greater expressiveness than some previous literature when the utilized budget for a single synthetic instance is the same. Through extensive experiments, we demonstrate that DDiF achieves superior performance on several benchmark datasets, extending beyond the image domain to include video, audio, and 3D voxel. We release the code at \url{https://github.com/aailab-kaist/DDiF}.

NeuralBench: A Unifying Framework to Benchmark NeuroAI Models | Hubert Banville

🧠 NeuralBench is now open source. Today we're releasing NeuralBench, a unified framework for benchmarking foundation models of brain activity, developed by the Brain & AI team at FAIR, Meta. 💻 Code: https://lnkd.in/dNJsgBgM 📄 White paper: https://lnkd.in/dvWMg7rx Brain foundation models are starting to show positive transfer to a range of downstream tasks, from brain-computer interfacing to clinical classification. But systematically evaluating them is hard: heterogeneous preprocessing pipelines, input structures, and adaptation methodologies make results difficult to compare. Most prior work also focuses on a narrow set of downstream tasks. NeuralBench addresses this by defining each task end-to-end with config files (data source, preprocessing, splits, optimiser, metrics, architecture) so all models can be evaluated on the same footing. What's in our first release, NeuralBench-EEG v1.0: ⚡ 36 EEG tasks across 94 public datasets, spanning motor imagery, clinical classification, cognitive decoding, and phenotype prediction. 🤖  Task-specific deep learning architectures (EEGNet, Deep4, EEGConformer, CTNet, ...) benchmarked side-by-side with recent EEG foundation models (BENDR, LaBraM, BIOT, CBraMod, LUNA, REVE). 🧩 Extensible to other neuroimaging modalities: the framework already runs MEG and fMRI tasks, leveraging our NeuralSet ecosystem for accessing brain imaging data and the broader neuroscientific software stack. 📜 Released under the MIT license. Help us make it better. Through our white paper, we invite the community to contribute new tasks, datasets, and models, especially for fMRI, MEG, and iEEG. The long-term goal is a fully unified benchmark across neuroimaging tasks and modalities. 🙏🙏🙏 This was a big team effort with Stéphane d'Ascoli, Simon Dahan, Jérémy RAPIN, Marlène Careil, Yohann Benchetrit, Jarod Lévy, Saarang P., Antoine Ratouchniak, Lucy (Mingfang) Zhang, Elisa Cascardi, Katie Begany, Teon Brooks, and Jean-Rémi King. Special thanks to Alexandre Gramfort, Thomas Moreau, Arnaud Delorme, Bruno A. and Pierre Guetschel for feedback and support. #Neuroscience #AI #NeuroAI #Python #OpenSource

The brain in the machine: How AI could help explain how we think | IBM

Scientists are using large AI models to predict patterns of brain activity at scale, a development that researchers say is pushing neuroscience toward a new kind of digital imaging.

🚀🚀🚀 Excited to share that our Brain&AI team is releasing NeuralSet! 🚀🚀🚀 A fast, simple, and scalable package for Neuro-AI: 📦 `pip install neuralset` 💻 Code: https://lnkd.in/eamwxBUY 📄… | Jarod Lévy | 12 comments

🚀🚀🚀 Excited to share that our Brain&AI team is releasing NeuralSet! 🚀🚀🚀 A fast, simple, and scalable package for Neuro-AI: 📦 `pip install neuralset` 💻 Code: https://lnkd.in/eamwxBUY 📄 Paper: https://lnkd.in/epbreyDy As a PhD student working extensively with neuro recordings and AI models, I find this library to be a game-changer. This has been the common backbone for the past several years of our projects (TribeV2, DynaDiff, Brain2Qwerty & more!). This library turns raw neuro-recordings into AI-ready tensors. It enables:  - Event-driven processing with typed, validated DataFrames. - Multimodal extractors for fMRI, EEG, MEG, EMG, and iEEG + text, image, audio, and video. - Torch-native segmentation for efficient batching. - Caching and remote compute through exca. Huge thanks to everyone on the team who made this release possible. If you're working on brain decoding, encoding models, or foundation models for neuroscience, don’t miss this! 🧠 Jean-Rémi King Hubert Banville Katie Begany Corentin Bel Yohann Benchetrit Teon Brooks Marlène Careil Simon Dahan Stéphane d'Ascoli Alexandre Défossez Linnea Evanson, PhD Pablo J. Diego Simón, Julien Gadonneix, Sophia Houhamdi Shubh Khanna Pierre Orhan Antoine Ratouchniak Joséphine Raugel Andrea Elena Santos Revilla Alexis Thual Lucy (Mingfang) Zhang Jérémy RAPIN | 12 comments on LinkedIn

Reverse-Engineering the Wetware: Spiking Networks, TD Errors, and the End of Matrix Math

An engineer-friendly deep dive into how the human brain truly processes information, learns without backpropagation, and uses spiking neural networks, predictive coding, and dopamine-driven TD errors—challenging our notions of AI and matrix math.

Understanding Deep Learning Algorithms that Leverage Unlabeled Data, Part 2: Contrastive Learning

Theoretical analysis of contrastive learning algorithms for leveraging unlabeled data.

jafioti/luminal | DeepWiki

This document provides a high-level introduction to the luminal deep learning framework, covering its core architecture, design principles, and key components. This overview explains how luminal's gra

#digitalbrainproject #neuroscience #artificialintelligence #openscience #callforprojects #research #ai #brain | Fondation Adolphe de Rothschild

The Rothschild Foundation Hospital is excited to launch the Digital Brain Project, a $5M collaborative research initiative to build an open source foundational model of the brain through open-access neural data.  Apply by May 15th for up to $500k in funding. Help crack the neural code 👉digitalbrainproject.org  Our objective is to develop a standardized, publicly available dataset of human brain recordings to accelerate fundamental research in cognitive neuroscience, AI system development, and clinical health. Our independent, multidisciplinary Scientific Committee oversees data collection, ethical review, privacy, and open science practices of our project — setting standards for large-scale brain research. The Digital Brain Project is conceived as an open collaborative infrastructure, at the intersection of neuroscience, artificial intelligence, and data science. It aims to accelerate data development, a key bottleneck to developing a foundational AI model of the human brain.  Key Focus Areas: 1️⃣ Brain recording data related to visual processing, language functions and cognitive actions 2️⃣ Rigorous quality, privacy, and ethical control, along with procedures for standardizing data across sites 3️⃣ Support for teams selected by an international multidisciplinary Scientific Committee Call for applications: Research teams working in neuroscience, neuroimaging, or artificial intelligence are invited to submit their applications by May 15, 2026. Selected teams will receive up to $500k USD funding to support their work. We appreciate our Scientific Committee (Lune Bellec Anne-Marie Kermarrec Arthur Mensch Russell Poldrack Lucia Melloni Jose Alain Sahel) for their independent scientific leadership, and partners Université de Montréal and AI at Meta for collaborating on this exciting project. #DigitalBrainProject #Neuroscience #ArtificialIntelligence #OpenScience #Callforprojects #Research #AI #Brain --------------------------------------------------------------  Pierre Bourdillon Julie Boyle Elisa Cascardi Jean-Rémi King Guillaume Le Hénanff Amélie Yavchitz Julien Gottsmann  Fabrice VERRIELE  Charlotte Cardin-Taillia