ArticleFrontiers in human neuroscience2021
BENDR: Using Transformers and a Contrastive Self-Supervised Learning Task to Learn From Massive Amounts of EEG Data.
Article in Frontiers in human neuroscience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers, 1 of them a synthesis that pooled it.
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Who cites it
44 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Harnessing Few-Shot Learning for EEG signal classification: a survey of state-of-the-art techniques and future directions.Frontiers in human neuroscience · 2024Pooled it
- An Explainable Self-Supervised Temporal Transformer for Participant-Level Classification of Depressive Symptoms From Resting-State EEG.International journal of methods in psychiatric research · 2026Article
- NeuroStream: spectral-spatio-temporal deep learning for visual stimulus classification from EEG.Scientific reports · 2026Article
- A Comprehensive Review of Sequence and Generative Models in Motor Imagery (MI) Classification for Brain-Computer Interfaces (BCIs).Sensors (Basel, Switzerland) · 2026Review
- Hybrid self-supervised EEG emotion representation: masked reconstruction joint with mutual information bounds.Brain informatics · 2026Article
- Window leakage in clinical EEG machine learning: matched subject-independent evaluation across ADHD and Parkinson cohorts.Frontiers in neuroinformatics · 2026Article
- Towards decoding individual words from non-invasive brain recordings.Nature communications · 2025Article
- Nanomedicine-Driven Modulation of the Gut-Brain Axis: Innovative Approaches to Managing Chronic Inflammation in Alzheimer's and Parkinson's Disease.International journal of molecular sciences · 2025Review
- Representation Learning and Foundation Models for Electroencephalography Analyses: Current trends, fundamental insights, and future directions?IEEE signal processing magazine · 2025Article
- Harnessing electroencephalography connectomes for cognitive and clinical neuroscience.Nature biomedical engineering · 2025Review
- Large-scale foundation models and generative AI for BigData neuroscience.Neuroscience research · 2025Review
- Self-Supervised Learning with Adaptive Frequency-Time Attention Transformer for Seizure Prediction and Classification.Brain sciences · 2025Article
- Flexible Patched Brain Transformer model for EEG decoding.Scientific reports · 2025Article
- Transformers in EEG Analysis: A Review of Architectures and Applications in Motor Imagery, Seizure, and Emotion Classification.Sensors (Basel, Switzerland) · 2025Review
- A Generalist Intracortical Motor Decoder.bioRxiv : the preprint server for biology · 2025Article
- TCPL: task-conditioned prompt learning for few-shot cross-subject motor imagery EEG decoding.Frontiers in neuroscience · 2025Article
- Deep learning approaches for EEG-based healthcare applications: a comprehensive review.Frontiers in human neuroscience · 2025Review
- Neural networks and foundation models: two strategies for EEG-to-fMRI prediction.Frontiers in systems biology · 2025Article
- Reconstructing signal during brain stimulation with Stim-BERT: a self-supervised learning model trained on millions of iEEG files.Frontiers in artificial intelligence · 2025Article
- Deep learning for inner speech recognition: a pilot comparative study of EEGNet and a spectro-temporal Transformer on bimodal EEG-fMRI data.Frontiers in human neuroscience · 2025Article
Corrections and comments
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3 authors.
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Abstract
Deep neural networks (DNNs) used for brain-computer interface (BCI) classification are commonly expected to learn general features when trained across a variety of contexts, such that these features could be fine-tuned to specific contexts. While some success is found in such an approach, we suggest that this interpretation is limited and an alternative would better leverage the newly (publicly) available massive electroencephalography (EEG) datasets. We consider how to adapt techniques and architectures used for language modeling (LM) that appear capable of ingesting awesome amounts of data toward the development of encephalography modeling with DNNs in the same vein. We specifically adapt an approach effectively used for automatic speech recognition, which similarly (to LMs) uses a self-supervised training objective to learn compressed representations of raw data signals. After adaptation to EEG, we find that a single pre-trained model is capable of modeling completely novel raw EEG sequences recorded with differing hardware, and different subjects performing different tasks. Furthermore, both the internal representations of this model and the entire architecture can be fine-tuned to a
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.