{"total":628,"rows":[{"id":"2405.18765","title":"Large Brain Model for Learning Generic Representations with Tremendous EEG Data in BCI","created":"2024-05-29","cited_by_count":327,"area":null},{"id":"2101.12037","title":"BENDR: using transformers and a contrastive self-supervised learning task to learn from massive amounts of EEG data","created":"2021-01-28","cited_by_count":199,"area":null},{"id":"2412.07236","title":"CBraMod: A Criss-Cross Brain Foundation Model for EEG Decoding","created":"2024-12-10","cited_by_count":199,"area":null},{"id":"2208.12266","title":"Decoding speech perception from non-invasive brain recordings","created":"2022-08-25","cited_by_count":130,"area":null},{"id":"2106.11170","title":"Transformer-based Spatial-Temporal Feature Learning for EEG Decoding","created":"2021-06-11","cited_by_count":97,"area":null},{"id":"2308.13234","title":"Decoding Natural Images from EEG for Object Recognition","created":"2023-08-25","cited_by_count":81,"area":null},{"id":"2403.07721","title":"Visual Decoding and Reconstruction via EEG Embeddings with Guided Diffusion","created":"2024-03-12","cited_by_count":81,"area":"diffusion"},{"id":"2510.21585","title":"REVE: A Foundation Model for EEG -- Adapting to Any Setup with Large-Scale Pretraining on 25,000 Subjects","created":"2025-10-24","cited_by_count":80,"area":null},{"id":"2311.03764","title":"Neuro-GPT: Towards A Foundation Model for EEG","created":"2023-11-07","cited_by_count":74,"area":null},{"id":"2211.02625","title":"MAEEG: Masked Auto-encoder for EEG Representation Learning","created":"2022-10-27","cited_by_count":62,"area":null},{"id":"2104.01233","title":"FBCNet: A Multi-view Convolutional Neural Network for Brain-Computer Interface","created":"2021-03-17","cited_by_count":57,"area":null},{"id":"2407.00463","title":"Open-Source Conversational AI with SpeechBrain 1.0","created":"2024-06-29","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":"2405.19363","title":"Medformer: A Multi-Granularity Patching Transformer for Medical Time-Series Classification","created":"2024-05-24","cited_by_count":53,"area":null},{"id":"2506.23075","title":"CSBrain: A Cross-scale Spatiotemporal Brain Foundation Model for EEG Decoding","created":"2025-06-29","cited_by_count":53,"area":null},{"id":"2104.02935","title":"TSception: Capturing Temporal Dynamics and Spatial Asymmetry from EEG for Emotion Recognition","created":"2021-04-07","cited_by_count":52,"area":null},{"id":"2401.10278","title":"EEGFormer: Towards Transferable and Interpretable Large-Scale EEG Foundation Model","created":"2024-01-11","cited_by_count":52,"area":null},{"id":"2210.01986","title":"MAtt: A Manifold Attention Network for EEG Decoding","created":"2022-10-05","cited_by_count":44,"area":null},{"id":"2203.11279","title":"EEG based Emotion Recognition: A Tutorial and Review","created":"2022-03-16","cited_by_count":43,"area":null},{"id":"2105.02786","title":"LGGNet: Learning from Local-Global-Graph Representations for Brain-Computer Interface","created":"2021-05-05","cited_by_count":36,"area":null},{"id":"2507.11783","title":"EEG Foundation Models: A Critical Review of Current Progress and Future Directions","created":"2025-07-15","cited_by_count":32,"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":"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":"2208.07448","title":"Self-Supervised Learning for Anomalous Channel Detection in EEG Graphs: Application to Seizure Analysis","created":"2022-08-15","cited_by_count":28,"area":null},{"id":"2305.18035","title":"Physics-Informed Computer Vision: A Review and Perspectives","created":"2023-05-29","cited_by_count":28,"area":null},{"id":"2506.09110","title":"CodeBrain: Bridging Decoupled Tokenizer and Multi-Scale Architecture for EEG Foundation Model","created":"2025-06-10","cited_by_count":28,"area":null},{"id":"2405.00719","title":"EEG-Deformer: A Dense Convolutional Transformer for Brain-computer Interfaces","created":"2024-04-25","cited_by_count":26,"area":null},{"id":"2510.22257","title":"LUNA: Efficient and Topology-Agnostic Foundation Model for EEG Signal Analysis","created":"2025-10-25","cited_by_count":26,"area":null},{"id":"2306.13101","title":"BrainNet: Epileptic Wave Detection from SEEG with Hierarchical Graph Diffusion Learning","created":"2023-06-15","cited_by_count":25,"area":null},{"id":"2202.12950","title":"2021 BEETL Competition: Advancing Transfer Learning for Subject Independence & Heterogenous EEG Data Sets","created":"2022-02-14","cited_by_count":23,"area":null},{"id":"2107.09507","title":"EEG-based Cross-Subject Driver Drowsiness Recognition with an Interpretable Convolutional Neural Network","created":"2021-05-30","cited_by_count":22,"area":null},{"id":"2502.17480","title":"Brain-to-Text Decoding: A Non-invasive Approach via Typing","created":"2025-02-18","cited_by_count":22,"area":null},{"id":"2111.05100","title":"EEGEyeNet: a Simultaneous Electroencephalography and Eye-tracking Dataset and Benchmark for Eye Movement Prediction","created":"2021-11-06","cited_by_count":21,"area":null},{"id":"2202.02472","title":"Tensor-CSPNet: A Novel Geometric Deep Learning Framework for Motor Imagery Classification","created":"2022-02-05","cited_by_count":21,"area":null},{"id":"2206.13676","title":"TTS-CGAN: A Transformer Time-Series Conditional GAN for Biosignal Data Augmentation","created":"2022-06-28","cited_by_count":21,"area":null},{"id":"2310.02152","title":"Graph Neural Network-based EEG Classification: A Survey","created":"2023-10-03","cited_by_count":21,"area":null},{"id":"2407.20254","title":"EEGMamba: Bidirectional State Space Model with Mixture of Experts for EEG Multi-task Classification","created":"2024-07-20","cited_by_count":21,"area":"ssm"},{"id":"2305.10351","title":"BIOT: Cross-data Biosignal Learning in the Wild","created":"2023-05-10","cited_by_count":20,"area":null},{"id":"2307.14389","title":"Diff-E: Diffusion-based Learning for Decoding Imagined Speech EEG","created":"2023-07-26","cited_by_count":17,"area":"diffusion"},{"id":"2407.03089","title":"Generative AI Enables EEG Super-Resolution via Spatio-Temporal Adaptive Diffusion Learning","created":"2024-07-03","cited_by_count":17,"area":"diffusion"},{"id":"2507.09882","title":"AdaBrain-Bench: Benchmarking Brain Foundation Models for Brain-Computer Interface Applications","created":"2025-07-14","cited_by_count":17,"area":null},{"id":"2102.03814","title":"MIN2Net: End-to-End Multi-Task Learning for Subject-Independent Motor Imagery EEG Classification","created":"2021-02-07","cited_by_count":16,"area":null},{"id":"2502.06438","title":"FEMBA: Efficient and Scalable EEG Analysis with a Bidirectional Mamba Foundation Model","created":"2025-02-10","cited_by_count":16,"area":"ssm"},{"id":"2102.03777","title":"EEGFuseNet: Hybrid Unsupervised Deep Feature Characterization and Fusion for High-Dimensional EEG with An Application to Emotion Recognition","created":"2021-02-07","cited_by_count":15,"area":null},{"id":"2207.08002","title":"EEG2Vec: Learning Affective EEG Representations via Variational Autoencoders","created":"2022-07-16","cited_by_count":15,"area":null},{"id":"2211.02641","title":"Graph Neural Networks on SPD Manifolds for Motor Imagery Classification: A Perspective from the Time-Frequency Analysis","created":"2022-10-25","cited_by_count":15,"area":null},{"id":"2402.17433","title":"Enhancing EEG-to-Text Decoding through Transferable Representations from Pre-trained Contrastive EEG-Text Masked Autoencoder","created":"2024-02-27","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":"2507.01196","title":"Are Large Brainwave Foundation Models Capable Yet? Insights from Fine-tuning","created":"2025-07-01","cited_by_count":15,"area":null},{"id":"2105.12916","title":"Robust learning from corrupted EEG with dynamic spatial filtering","created":"2021-05-27","cited_by_count":14,"area":null},{"id":"2204.12440","title":"Neuro-BERT: Rethinking Masked Autoencoding for Self-supervised Neurological Pretraining","created":"2022-04-20","cited_by_count":14,"area":null},{"id":"2210.04172","title":"A Transformer-based deep neural network model for SSVEP classification","created":"2022-10-09","cited_by_count":14,"area":null},{"id":"2306.08256","title":"Data Augmentation for Seizure Prediction with Generative Diffusion Model","created":"2023-06-14","cited_by_count":14,"area":"diffusion"},{"id":"2412.17337","title":"Neural-MCRL: Neural Multimodal Contrastive Representation Learning for EEG-based Visual Decoding","created":"2024-12-23","cited_by_count":14,"area":null},{"id":"2510.09095","title":"Neural Codecs as Biosignal Tokenizers","created":"2025-10-10","cited_by_count":14,"area":null},{"id":"2511.06836","title":"NeuroBridge: Bio-Inspired Self-Supervised EEG-to-Image Decoding via Cognitive Priors and Bidirectional Semantic Alignment","created":"2025-11-10","cited_by_count":14,"area":null},{"id":"2103.15750","title":"Enabling Design Methodologies and Future Trends for Edge AI: Specialization and Co-design","created":"2021-03-25","cited_by_count":13,"area":null},{"id":"2102.09050","title":"End-to-end learnable EEG channel selection for deep neural networks with Gumbel-softmax","created":"2021-02-11","cited_by_count":12,"area":null},{"id":"2106.00613","title":"A Compact and Interpretable Convolutional Neural Network for Cross-Subject Driver Drowsiness Detection from Single-Channel EEG","created":"2021-05-30","cited_by_count":12,"area":null},{"id":"2311.18520","title":"Calibration-free online test-time adaptation for electroencephalography motor imagery decoding","created":"2023-11-30","cited_by_count":12,"area":null},{"id":"2401.10746","title":"A Systematic Evaluation of Euclidean Alignment with Deep Learning for EEG Decoding","created":"2024-01-19","cited_by_count":12,"area":null},{"id":"2506.10351","title":"PhysioWave: A Multi-Scale Wavelet-Transformer for Physiological Signal Representation","created":"2025-06-12","cited_by_count":12,"area":null},{"id":"2506.19141","title":"EEG Foundation Challenge: From Cross-Task to Cross-Subject EEG Decoding","created":"2025-06-23","cited_by_count":12,"area":null},{"id":"2204.06947","title":"EEG-ITNet: An Explainable Inception Temporal Convolutional Network for Motor Imagery Classification","created":"2022-04-14","cited_by_count":11,"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":"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":"2412.17842","title":"Canine EEG Helps Human: Cross-Species and Cross-Modality Epileptic Seizure Detection via Multi-Space Alignment","created":"2024-12-18","cited_by_count":11,"area":null},{"id":"2506.21140","title":"DBConformer: Dual-Branch Convolutional Transformer for EEG Decoding","created":"2025-06-26","cited_by_count":11,"area":null},{"id":"2510.16548","title":"NeurIPT: Foundation Model for Neural Interfaces","created":"2025-10-18","cited_by_count":11,"area":null},{"id":"2111.10026","title":"IC-U-Net: A U-Net-based Denoising Autoencoder Using Mixtures of Independent Components for Automatic EEG Artifact Removal","created":"2021-11-19","cited_by_count":10,"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":"2509.24222","title":"Uni-NTFM: A Unified Foundation Model for EEG Signal Representation Learning","created":"2025-09-29","cited_by_count":10,"area":null},{"id":"2202.05170","title":"Efficacy of Transformer Networks for Classification of Raw EEG Data","created":"2022-02-08","cited_by_count":9,"area":null},{"id":"2301.05869","title":"Functional Neural Networks: Shift invariant models for functional data with applications to EEG classification","created":"2023-01-14","cited_by_count":9,"area":null},{"id":"2308.16207","title":"MASA-TCN: Multi-anchor Space-aware Temporal Convolutional Neural Networks for Continuous and Discrete EEG Emotion Recognition","created":"2023-08-30","cited_by_count":9,"area":null},{"id":"2409.19130","title":"Multi-modal Cross-domain Self-supervised Pre-training for fMRI and EEG Fusion","created":"2024-09-27","cited_by_count":9,"area":null},{"id":"2502.01678","title":"LEAD: An EEG Foundation Model for Alzheimer's Disease Detection","created":"2025-02-02","cited_by_count":9,"area":null},{"id":"2601.06134","title":"DeeperBrain: A Neuro-Grounded EEG Foundation Model Towards Universal BCI","created":"2026-01-05","cited_by_count":9,"area":null},{"id":"2008.04873","title":"Maximizing BCI Human Feedback using Active Learning","created":"2020-08-11","cited_by_count":8,"area":"alignment"},{"id":"2102.02729","title":"Adversarial Attacks and Defenses in Physiological Computing: A Systematic Review","created":"2021-02-04","cited_by_count":8,"area":null},{"id":"2205.06986","title":"Evaluating Membership Inference Through Adversarial Robustness","created":"2022-05-14","cited_by_count":8,"area":"safety"},{"id":"2308.02408","title":"Evaluating the structure of cognitive tasks with transfer learning","created":"2023-07-28","cited_by_count":8,"area":null},{"id":"2410.07299","title":"OTIS: Learning High-Quality Time Series Features With Tiny Encoders","created":"2024-10-09","cited_by_count":8,"area":null},{"id":"2410.09767","title":"LibEER: A Comprehensive Benchmark and Algorithm Library for EEG-based Emotion Recognition","created":"2024-10-13","cited_by_count":8,"area":null},{"id":"2511.13733","title":"THD-BAR: Topology Hierarchical Derived Brain Autoregressive Modeling for EEG Generic Representations","created":"2025-11-05","cited_by_count":8,"area":null},{"id":"2105.07917","title":"CNN-based Approaches For Cross-Subject Classification in Motor Imagery: From The State-of-The-Art to DynamicNet","created":"2021-05-17","cited_by_count":7,"area":null},{"id":"2202.06948","title":"Towards Best Practice of Interpreting Deep Learning Models for EEG-based Brain Computer Interfaces","created":"2022-02-12","cited_by_count":7,"area":null},{"id":"2202.09559","title":"Priming Cross-Session Motor Imagery Classification with A Universal Deep Domain Adaptation Framework","created":"2022-02-19","cited_by_count":7,"area":null},{"id":"2203.00516","title":"Mental State Classification Using Multi-graph Features","created":"2022-02-25","cited_by_count":7,"area":null},{"id":"2205.14102","title":"Group-level Brain Decoding with Deep Learning","created":"2022-05-27","cited_by_count":7,"area":null},{"id":"2206.08672","title":"A Deep Learning Approach for the Segmentation of Electroencephalography Data in Eye Tracking Applications","created":"2022-06-17","cited_by_count":7,"area":null},{"id":"2308.00454","title":"ViT2EEG: Leveraging Hybrid Pretrained Vision Transformers for EEG Data","created":"2023-08-01","cited_by_count":7,"area":null},{"id":"2312.09454","title":"Uncertainty Quantification in Machine Learning for Biosignal Applications -- A Review","created":"2023-11-15","cited_by_count":7,"area":null},{"id":"2405.02485","title":"A Survey of Few-Shot Learning for Biomedical Time Series","created":"2024-05-03","cited_by_count":7,"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":"2502.03081","title":"Human-Aligned Image Models Improve Visual Decoding from the Brain","created":"2025-02-05","cited_by_count":7,"area":null},{"id":"2502.09203","title":"Revisiting Euclidean Alignment for Transfer Learning in EEG-Based Brain-Computer Interfaces","created":"2025-02-13","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":"2509.22556","title":"ECHO: Toward Contextual Seq2Seq Paradigms in Large EEG Models","created":"2025-09-26","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}],"next_cursor":"100"}