{"total":77,"rows":[{"id":"2208.12266","title":"Decoding speech perception from non-invasive brain recordings","created":"2022-08-25","cited_by_count":130,"area":null},{"id":"2301.00265","title":"Source-Free Unsupervised Domain Adaptation: A Survey","created":"2022-12-31","cited_by_count":80,"area":null},{"id":"2310.19812","title":"Brain decoding: toward real-time reconstruction of visual perception","created":"2023-10-18","cited_by_count":66,"area":null},{"id":"2306.16934","title":"DreamDiffusion: Generating High-Quality Images from Brain EEG Signals","created":"2023-06-29","cited_by_count":56,"area":null},{"id":"2206.01323","title":"SPD domain-specific batch normalization to crack interpretable unsupervised domain adaptation in EEG","created":"2022-06-02","cited_by_count":53,"area":null},{"id":"2206.14483","title":"Data augmentation for learning predictive models on EEG: a systematic comparison","created":"2022-06-29","cited_by_count":33,"area":null},{"id":"2203.09978","title":"WOODS: Benchmarks for Out-of-Distribution Generalization in Time Series","created":"2022-03-18","cited_by_count":30,"area":null},{"id":"2312.14871","title":"BrainVis: Exploring the Bridge between Brain and Visual Signals via Image Reconstruction","created":"2023-12-22","cited_by_count":30,"area":null},{"id":"2404.15319","title":"The largest EEG-based BCI reproducibility study for open science: the MOABB benchmark","created":"2024-04-03","cited_by_count":29,"area":null},{"id":"2605.08495","title":"NeuralBench: A Unifying Framework to Benchmark NeuroAI Models","created":"2026-05-08","cited_by_count":16,"area":null},{"id":"2401.00430","title":"Brain-Conditional Multimodal Synthesis: A Survey and Taxonomy","created":"2023-12-31","cited_by_count":15,"area":null},{"id":"2412.07228","title":"T-TIME: Test-Time Information Maximization Ensemble for Plug-and-Play BCIs","created":"2024-12-10","cited_by_count":15,"area":null},{"id":"2411.07249","title":"SPDIM: Source-Free Unsupervised Conditional and Label Shift Adaptation in EEG","created":"2024-10-26","cited_by_count":12,"area":null},{"id":"2212.10426","title":"Deep Riemannian Networks for End-to-End EEG Decoding","created":"2022-12-20","cited_by_count":11,"area":null},{"id":"2305.11288","title":"Riemannian Multinomial Logistics Regression for SPD Neural Networks","created":"2023-05-18","cited_by_count":11,"area":null},{"id":"2310.11198","title":"EEG motor imagery decoding: A framework for comparative analysis with channel attention mechanisms","created":"2023-10-17","cited_by_count":11,"area":null},{"id":"2304.10755","title":"Interpretable and Robust AI in EEG Systems: A Survey","created":"2023-04-21","cited_by_count":10,"area":null},{"id":"2308.02408","title":"Evaluating the structure of cognitive tasks with transfer learning","created":"2023-07-28","cited_by_count":8,"area":null},{"id":"2403.11261","title":"A Lie Group Approach to Riemannian Batch Normalization","created":"2024-03-17","cited_by_count":8,"area":null},{"id":"2409.19433","title":"RMLR: Extending Multinomial Logistic Regression into General Geometries","created":"2024-09-28","cited_by_count":8,"area":null},{"id":"2412.01079","title":"Federated Motor Imagery Classification for Privacy-Preserving Brain-Computer Interfaces","created":"2024-12-02","cited_by_count":7,"area":null},{"id":"2503.05349","title":"Spatial Distillation based Distribution Alignment (SDDA) for Cross-Headset EEG Classification","created":"2025-03-07","cited_by_count":7,"area":null},{"id":"2605.14698","title":"NeuroAtlas: Benchmarking Foundation Models for Clinical EEG and Brain-Computer Interfaces","created":"2026-05-14","cited_by_count":7,"area":null},{"id":"2111.03122","title":"Functional connectivity ensemble method to enhance BCI performance (FUCONE)","created":"2021-11-04","cited_by_count":6,"area":null},{"id":"2111.12071","title":"Minimizing subject-dependent calibration for BCI with Riemannian transfer learning","created":"2021-11-23","cited_by_count":6,"area":null},{"id":"2409.03251","title":"Dual-TSST: A Dual-Branch Temporal-Spectral-Spatial Transformer Model for EEG Decoding","created":"2024-09-05","cited_by_count":6,"area":null},{"id":"2211.11460","title":"Motor Imagery Decoding Using Ensemble Curriculum Learning and Collaborative Training","created":"2022-11-21","cited_by_count":5,"area":null},{"id":"2301.11447","title":"Personalised Federated Learning On Heterogeneous Feature Spaces","created":"2023-01-26","cited_by_count":5,"area":null},{"id":"2302.04508","title":"Classification of BCI-EEG based on augmented covariance matrix","created":"2023-02-09","cited_by_count":5,"area":null},{"id":"2308.11656","title":"Pseudo-online framework for BCI evaluation: A MOABB perspective","created":"2023-08-21","cited_by_count":5,"area":null},{"id":"2309.07159","title":"A Strong and Simple Deep Learning Baseline for BCI MI Decoding","created":"2023-09-11","cited_by_count":5,"area":null},{"id":"2309.12056","title":"BELT:Bootstrapping Electroencephalography-to-Language Decoding and Zero-Shot Sentiment Classification by Natural Language Supervision","created":"2023-09-21","cited_by_count":5,"area":null},{"id":"2403.05645","title":"Geometric Neural Network based on Phase Space for BCI-EEG decoding","created":"2024-03-08","cited_by_count":5,"area":null},{"id":"2411.11879","title":"CSP-Net: Common Spatial Pattern Empowered Neural Networks for EEG-Based Motor Imagery Classification","created":"2024-11-04","cited_by_count":5,"area":null},{"id":"2507.20254","title":"MIRepNet: A Pipeline and Foundation Model for EEG-Based Motor Imagery Classification","created":"2025-07-27","cited_by_count":5,"area":null},{"id":"2202.02001","title":"Introducing Block-Toeplitz Covariance Matrices to Remaster Linear Discriminant Analysis for Event-related Potential Brain-computer Interfaces","created":"2022-02-04","cited_by_count":4,"area":null},{"id":"2311.16109","title":"Transfer Learning between Motor Imagery Datasets using Deep Learning -- Validation of Framework and Comparison of Datasets","created":"2023-09-04","cited_by_count":4,"area":null},{"id":"2211.02624","title":"Spatial Graph Signal Interpolation with an Application for Merging BCI Datasets with Various Dimensionalities","created":"2022-10-28","cited_by_count":3,"area":null},{"id":"2403.09228","title":"Uncertainty Quantification for cross-subject Motor Imagery classification","created":"2024-03-14","cited_by_count":3,"area":null},{"id":"2410.08224","title":"A Survey of Spatio-Temporal EEG data Analysis: from Models to Applications","created":"2024-09-26","cited_by_count":3,"area":null},{"id":"2110.10011","title":"Riemannian classification of EEG signals with missing values","created":"2021-10-19","cited_by_count":2,"area":null},{"id":"2203.07807","title":"End-to-end P300 BCI using Bayesian accumulation of Riemannian probabilities","created":"2022-03-15","cited_by_count":2,"area":null},{"id":"2404.05553","title":"Alljoined1 -- A dataset for EEG-to-Image decoding","created":"2024-04-08","cited_by_count":2,"area":null},{"id":"2412.03224","title":"Channel Reflection: Knowledge-Driven Data Augmentation for EEG-Based Brain-Computer Interfaces","created":"2024-12-04","cited_by_count":2,"area":null},{"id":"2605.07212","title":"Same Brain, Different Prediction: How Preprocessing Choices Undermine EEG Decoding Reliability","created":"2026-05-08","cited_by_count":2,"area":null},{"id":"2304.06495","title":"An embedding for EEG signals learned using a triplet loss","created":"2023-03-23","cited_by_count":1,"area":null},{"id":"2403.15413","title":"Playing With Neuroscience: Past, Present and Future of Neuroimaging and Games","created":"2024-03-06","cited_by_count":1,"area":null},{"id":"2411.02726","title":"Elliptical Wishart distributions: information geometry, maximum likelihood estimator, performance analysis and statistical learning","created":"2024-11-05","cited_by_count":1,"area":null},{"id":"2503.16567","title":"Exploring Deep Learning Models for EEG Neural Decoding","created":"2025-03-20","cited_by_count":1,"area":null},{"id":"2506.16009","title":"Bridging Brain with Foundation Models through Self-Supervised Learning","created":"2025-06-19","cited_by_count":1,"area":null},{"id":"2506.21843","title":"3D-Telepathy: Reconstructing 3D Objects from EEG Signals","created":"2025-06-27","cited_by_count":1,"area":null},{"id":"2606.07926","title":"Barycentric Projections of Optimal Transport Plans on Riemannian Manifolds","created":"2026-06-06","cited_by_count":1,"area":null},{"id":"2607.03094","title":"Stacked LoRA for Subject-Adaptive EEG Foundation Models in Motor Imagery Decoding","created":"2026-07-03","cited_by_count":1,"area":null},{"id":"2312.00799","title":"hvEEGNet: exploiting hierarchical VAEs on EEG data for neuroscience applications","created":"2023-11-20","cited_by_count":0,"area":null},{"id":"2509.19401","title":"SpellerSSL: Self-Supervised Learning with P300 Aggregation for Speller BCIs","created":"2025-09-23","cited_by_count":0,"area":null},{"id":"2510.10604","title":"FusionGen: Feature Fusion-Based Few-Shot EEG Data Generation","created":"2025-10-12","cited_by_count":0,"area":null},{"id":"2511.07891","title":"Toward Adaptive BCIs: Enhancing Decoding Stability via User State-Aware EEG Filtering","created":"2025-11-11","cited_by_count":0,"area":null},{"id":"2511.20570","title":"Gated Uncertainty-Aware Runtime Dual Invariants for Neural Signal-Controlled Robotics","created":"2025-11-25","cited_by_count":0,"area":null},{"id":"2603.12296","title":"Synthetic Data Generation for Brain-Computer Interfaces: Overview, Benchmarking, and Future Directions","created":"2026-03-11","cited_by_count":0,"area":null},{"id":"2605.00865","title":"Leakage-Audited Benchmarking Reveals Limited Evidence for Cross-Subject Auditory-Evoked EEG Vowel Perception Decoding","created":"2026-04-22","cited_by_count":0,"area":null},{"id":"2605.03874","title":"Spatiotemporal Convolutions on EEG signal -- A Representation Learning Perspective on Efficient and Explainable EEG Classification with Convolutional Neural Nets","created":"2026-05-05","cited_by_count":0,"area":null},{"id":"2605.22893","title":"L-FAME: Longitudinal Focused Attention Meditation EEG Dataset and Benchmark","created":"2026-05-21","cited_by_count":0,"area":null},{"id":"2606.24394","title":"Average Rankings Mask Per-Subject Optimality: A Friedman-Nemenyi Benchmark of EEG Motor-Imagery BCI Decoders","created":"2026-06-23","cited_by_count":0,"area":null},{"id":"2607.06630","title":"When Certificates Fail: A Unified Safety Framework for Embedded Neural Interface Models","created":"2026-07-07","cited_by_count":0,"area":null},{"id":"2607.08783","title":"LieBN: Batch Normalization over Lie Groups","created":"2026-06-13","cited_by_count":0,"area":null},{"id":"2607.16225","title":"Overcoming the BCI Calibration Bottleneck: A Clinically-Grounded Architecture using Riemannian Alignment and Stochastic Weight Averaging","created":"2026-06-19","cited_by_count":0,"area":null},{"id":"2607.22980","title":"Bayesian Complete-Pooling in Cross-Subject Classification for Motor Imagery Electroencephalogram","created":"2026-07-25","cited_by_count":0,"area":null},{"id":"2609.10961","title":"When More Is Not Better: Component Anti-Synergy in a P300 Speller","created":"2026-09-10","cited_by_count":0,"area":null},{"id":"2609.18104","title":"iMINDBench: iEEG Multi-Institution Neural Decoding Benchmark","created":"2026-09-16","cited_by_count":0,"area":null},{"id":"2609.23924","title":"Matched-Input Estimates Differ in Sign Across Architectures: Auditing EEG Foundation Models on Motor Imagery","created":"2026-09-20","cited_by_count":0,"area":null},{"id":"2609.29037","title":"Personalised federated learning for Riemannian and Euclidean EEG decoding","created":"2026-09-24","cited_by_count":0,"area":null},{"id":"2609.31632","title":"EEGAgentBench: Benchmarking LLM Agents on Short- and Long-Horizon EEG Analysis","created":"2026-09-10","cited_by_count":0,"area":null},{"id":"2609.33880","title":"Neuron-Level Architecture Growth: A Controlled Evaluation for EEG Time-Series Decoding","created":"2026-09-27","cited_by_count":0,"area":null},{"id":"2609.34466","title":"PhysioTRACE: Provenance-Aware Stress Tests for Physiological Foundation Models","created":"2026-09-28","cited_by_count":0,"area":null},{"id":"2609.36609","title":"Best Practices in EEG Analysis: Preprocessing, Modeling, and Machine Learning","created":"2026-09-29","cited_by_count":0,"area":null},{"id":"2609.38587","title":"NeurDuo-EEG: A Long-Sequence EEG Foundation Model with Persistent State and Explicit Memory","created":"2026-09-29","cited_by_count":0,"area":null},{"id":"2610.01124","title":"CortexBridge: Cortical Alignment of EEG Montages for Foundation Models","created":"2026-10-01","cited_by_count":0,"area":null}],"next_cursor":null}