ArticleHuman brain mapping2017
Deep learning with convolutional neural networks for EEG decoding and visualization.
Article in Human brain mapping, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 609 papers, 4 of them syntheses that pooled it.
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Who cites it
609 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Decoding Upper-Limb Motor Imagery from EEG Signals: A Systematic Review of Methods and Applications.Annals of biomedical engineering · 2026Pooled it
- Resting-state EEG changes associated with cognitive decline in Parkinson's disease: a systematic review.Journal of neural transmission (Vienna, Austria : 1996) · 2026Pooled it
- Computational predictive processing models of consciousness: a systematic review of non-invasive brain signal analysis in disorders of consciousness.Frontiers in computational neuroscience · 2026Pooled it
- Artificial Intelligence Approaches for EEG Signal Acquisition and Processing in Lower-Limb Motor Imagery: A Systematic Review.Sensors (Basel, Switzerland) · 2025Pooled it
- Corticomorphic Hybrid CNN-SNN Architecture for EEG-Based Low-Footprint Low-Latency Auditory Attention Detection.Annals of biomedical engineering · 2026Article
- Subject-Specific Deep Learning Model for Motor Imagery Direction Decoding.Sensors (Basel, Switzerland) · 2026Article
- From EEG Source Localization to Brain Network Analysis: A Computational Neuroscience Perspective.Biology · 2026Review
- NeuroStream: spectral-spatio-temporal deep learning for visual stimulus classification from EEG.Scientific reports · 2026Article
- Transformer-Based Modeling of Directed Transfer Entropy Connectivity for EEG-Based ADHD Classification in Children.Sensors (Basel, Switzerland) · 2026Article
- Task-Oriented Semantic Feature Transmission for Robust EEG Motor Imagery Decoding Under Additive White Gaussian Noise.Sensors (Basel, Switzerland) · 2026Article
- Deep learning based depth of anaesthesia monitoring using EEG: a 4-layer CNN model with PSD and BSR correlation features.Physical and engineering sciences in medicine · 2026Article
- Leveraging pretrained vision transformers for classifying alcohol use disorder using raw resting-state EEG.Neuroimage. Reports · 2026Article
- CNN models in time-frequency domain for identification of motor imagery tasks from EEG signals.Physical and engineering sciences in medicine · 2026Article
- PDC-Net: A Prefrontal Dual-Channel Network with Gated Mamba Interaction for Cross-Subject Visual Attentional State Decoding.Brain sciences · 2026Article
- FSC-BiMamba: A Dual-Branch Bidirectional Mamba Network for Frontal Sparse-Channel EEG Depression Detection.Brain sciences · 2026Article
- Evaluating Validation Strategies in Motor Imagery EEG: A Full-Cohort GAF-PLV Analysis and Matched Sensitivity Study.Brain sciences · 2026Article
- Deep Learning for Ear-EEG-Based Brain-Computer Interface: A Systematic Comparison and Design Insights.Biosensors · 2026Article
- A Comprehensive Review of Sequence and Generative Models in Motor Imagery (MI) Classification for Brain-Computer Interfaces (BCIs).Sensors (Basel, Switzerland) · 2026Review
- An ERP dataset for multi-information identity authentication.Data in brief · 2026Article
- A Learnable Entropy-Power Scaling Transform with a Radial Basis Function Network for Electroencephalography-Based Familiar and Unfamiliar Face Classification.Entropy (Basel, Switzerland) · 2026Article
549 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Deep learning with convolutional neural networks (deep ConvNets) has revolutionized computer vision through end-to-end learning, that is, learning from the raw data. There is increasing interest in using deep ConvNets for end-to-end EEG analysis, but a better understanding of how to design and train ConvNets for end-to-end EEG decoding and how to visualize the informative EEG features the ConvNets learn is still needed. Here, we studied deep ConvNets with a range of different architectures, designed for decoding imagined or executed tasks from raw EEG. Our results show that recent advances from the machine learning field, including batch normalization and exponential linear units, together with a cropped training strategy, boosted the deep ConvNets decoding performance, reaching at least as good performance as the widely used filter bank common spatial patterns (FBCSP) algorithm (mean decoding accuracies 82.1% FBCSP, 84.0% deep ConvNets). While FBCSP is designed to use spectral power modulations, the features used by ConvNets are not fixed a priori. Our novel methods for visualizing the learned features demonstrated that ConvNets indeed learned to use spectral power modulations in the alpha, beta, and high gamma frequencies, and proved useful for spatially mapping the learned features by revealing the topography of the causal contributions of features in different frequency bands to the decoding decision. Our study thus shows how to design and train ConvNets to decode task-related information from the raw EEG without handcrafted features and highlights the potential of deep ConvNets combined with advanced visualization techniques for EEG-based brain mapping. Hum Brain Mapp 38:5391-5420, 2017. © 2017 Wiley Periodicals, Inc.
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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.