Papers Eeg Decoding
“Eeg Decoding” 태그가 달린 논문 130편 · 필터 해제
ProCA: Progressive Contrastive Alignment for Robust EEG Visual Decoding
Electroencephalogram (EEG) visual decoding aims to recover visual semantics from non-invasive neural time-series signals, for which robust alignment between noisy neural responses and stable semantic representations is k…
Contrastive LearningEeg DecodingEEG Decoding Using CNN and LSTM Network
Motor imagery (MI) brain--computer interfaces (BCIs) have emerged as a promising approach for establishing flexible communication pathways between the human brain and external devices , particularly for individuals affec…
Eeg DecodingEEG-PRIME: Prototype-Aligned Representation Learning with Multi-Level Conditioning for EEG Decoding
Electroencephalography (EEG) decoding models often generalize poorly across datasets and subjects due to domain shifts in acquisition protocols and individual neurophysiology. We propose EEG-PRIME, a two-stage EEG founda…
Representation LearningEmotion RecognitionEeg DecodingCoCoT-EEG: Contrastive-Pretrained Multiscale Convolutional Transformer for EEG Decoding
Self-supervised pretrained foundation models (FM) have shown early promise for non-invasive electroencephalogram (EEG) decoding applications. Many recent large-scale models converged on the approach of tokenizing raw EEG…
Contrastive LearningEeg DecodingTowards Robust EEG Decoding Based on Riemannian Self-Attention
Brain-Computer Interface (BCI) based on electroencephalography (EEG) enables direct interaction between the brain and external environments and has significant applications in assistive technologies, medical rehabilitati…
Eeg DecodingDual-Stream EEG Decoding for 3D Visual Perception
This paper explores a novel brain decoding model for 3D shape perception through a dual pathway architecture mirroring biological vision. Our bio-inspired approach implements separate decoding modules for object identity…
3D ReconstructionBrain DecodingEeg DecodingLearning aligned EEG representations with subject-specific encoders
Cross-subject EEG decoding promises more training data, but it also exposes neural networks to strong inter-subject distribution shifts. We study whether task supervision and architecture alone can learn subject-aligned …
Eeg DecodingZero-Shot Neural Priors for Generalizable Cross-Subject and Cross-Task EEG Decoding
The development of generalizable electroencephalography (EEG) decoding models is essential for robust brain-computer interfaces (BCI) and objective neural biomarkers in mental health. Conventional approaches have been hi…
Eeg DecodingA Sliced-Wasserstein Framework on Correlation Matrices for EEG Decoding
Electroencephalography (EEG) offers noninvasive, millisecond resolution recordings of neuronal activity and is widely used in neuroscience and healthcare. Many EEG decoding pipelines rely on covariance descriptors for th…
Domain GeneralizationEeg DecodingEvoBrain: Continual Learning of EEG Foundation Models Across Heterogeneous BCI Tasks
Electroencephalography (EEG) is the cornerstone of non-invasive brain-computer interfaces (BCIs), yet conventional decoding relies on fragmented, task-specific architectures that severely limit cross-task scalability. Wh…
Continual LearningBrain DecodingEeg DecodingRouting on the Stiefel Manifold: When Does Adaptive Subspace Selection Help for Cross-Domain EEG Decoding?
Cross-domain EEG decoding remains challenging despite advances in Riemannian deep learning: covariance matrices from different subjects occupy systematically distinct regions of the SPD manifold, yet existing domain adap…
Domain AdaptationEeg DecodingBenchmarking Positional Encoding Strategies for Transformer-Based EEG Foundation Models
Electroencephalography (EEG) is a widely used non-invasive technique for measuring brain activity in brain-computer interface (BCI) applications. Supervised EEG decoding models often struggle to generalize across tasks, …
Self-Supervised LearningEmotion RecognitionEeg DecodingSYNAPSE: Neuro-Symbolic Visual Thought-to-Text Decoding via Topological Semantic Denoising
Recent advances in large language models have accelerated open-vocabulary EEG-to-imagined-text decoding, where non-invasive neural activity recorded during visual perception is translated into coherent natural language d…
Text GenerationEeg DecodingSame Brain, Different Prediction: How Preprocessing Choices Undermine EEG Decoding Reliability
Electroencephalography (EEG) is a cornerstone of brain-computer interfaces and clinical neuroscience, yet deep learning models are typically trained and evaluated under a single, unreported preprocessing pipeline. We for…
Eeg DecodingCross-Subject Generalization for EEG Decoding: A Survey of Deep Learning Methods
Deep learning for cross-subject EEG decoding is hindered by high inter-subject variability, which introduces a severe domain shift between training and unseen test subjects. This survey presents a comprehensive review of…
Contrastive LearningEeg DecodingDSAINet: An Efficient Dual-Scale Attentive Interaction Network for General EEG Decoding
In real-world applications of noninvasive electroencephalography (EEG), specialized decoders often show limited generalizability across diverse tasks under subject-independent settings. One central challenge is that task…
Eeg DecodingPA-TCNet: Pathology-Aware Temporal Calibration with Physiology-Guided Target Refinement for Cross-Subject Motor Imagery EEG Decoding in Stroke Patients
Stroke patient cross-subject electroencephalography (EEG) decoding of motor imagery (MI) brain-computer interface (BCI) is essential for motor rehabilitation, yet lesion-related abnormal temporal dynamics and pronounced …
Eeg DecodingBrain3D: EEG-to-3D Decoding of Visual Representations via Multimodal Reasoning
Decoding visual information from electroencephalography (EEG) has recently achieved promising results, primarily focusing on reconstructing two-dimensional (2D) images from brain activity. However, the reconstruction of …
Multimodal Reasoning3D Reconstruction3D GenerationEeg DecodingLI-DSN: A Layer-wise Interactive Dual-Stream Network for EEG Decoding
Electroencephalography (EEG) provides a non-invasive window into brain activity, offering high temporal resolution crucial for understanding and interacting with neural processes through brain-computer interfaces (BCIs).…
Emotion RecognitionEeg DecodingCIPHER: Conformer-based Inference of Phonemes from High-density EEG
Decoding speech information from scalp EEG remains difficult due to low SNR and spatial blurring. We present CIPHER (Conformer-based Inference of Phonemes from High-density EEG Representations), a dual-pathway model usin…
Eeg Decoding