Papers Eeg Decoding
“Eeg Decoding” 태그가 달린 논문 130편 · 필터 해제
SpecMoE: Spectral Mixture-of-Experts Foundation Model for Cross-Species EEG Decoding
Decoding the orchestration of neural activity in electroencephalography (EEG) signals is a central challenge in bridging neuroscience with artificial intelligence. Foundation models have made strides in generalized EEG d…
Emotion RecognitionEeg DecodingHierarchic-EEG2Text: Assessing EEG-To-Text Decoding across Hierarchical Abstraction Levels
An electroencephalogram (EEG) records the spatially averaged electrical activity of neurons in the brain, measured from the human scalp. Prior studies have explored EEG-based classification of objects or concepts, often …
Eeg DecodingTemporal Context and Architecture: A Benchmark for Naturalistic EEG Decoding
We study how model architecture and temporal context interact in naturalistic EEG decoding. Using the HBN movie-watching dataset, we benchmark five architectures, CNN, LSTM, a stabilized Transformer (EEGXF), S4, and S5, …
Eeg DecodingFusion of Spatio-Temporal and Multi-Scale Frequency Features for Dry Electrodes MI-EEG Decoding
Dry-electrode Motor Imagery Electroencephalography (MI-EEG) enables fast, comfortable, real-world Brain Computer Interface by eliminating gels and shortening setup for at-home and wearable use.However, dry recordings pos…
Eeg DecodingHCFT: Hierarchical Convolutional Fusion Transformer for EEG Decoding
Electroencephalography (EEG) decoding requires models that can effectively extract and integrate complex temporal, spectral, and spatial features from multichannel signals. To address this challenge, we propose a lightwe…
Representation LearningSeizure predictionEeg DecodingContrastive and Multi-Task Learning on Noisy Brain Signals with Nonlinear Dynamical Signatures
We introduce a two-stage multitask learning framework for analyzing Electroencephalography (EEG) signals that integrates denoising, dynamical modeling, and representation learning. In the first stage, a denoising autoenc…
Self-Supervised LearningRepresentation LearningMulti-Task LearningEeg DecodingBackpropagation-Free Test-Time Adaptation for Lightweight EEG-Based Brain-Computer Interfaces
Electroencephalogram (EEG)-based brain-computer interfaces (BCIs) face significant deployment challenges due to inter-subject variability, signal non-stationarity, and computational constraints. While test-time adaptatio…
Test-time AdaptationBayesian InferenceEeg DecodingHEEGNet: Hyperbolic Embeddings for EEG
Electroencephalography (EEG)-based brain-computer interfaces facilitate direct communication with a computer, enabling promising applications in human-computer interactions. However, their utility is currently limited be…
Emotion RecognitionDomain AdaptationEeg DecodingFusion of Multiscale Features Via Centralized Sparse-attention Network for EEG Decoding
Electroencephalography (EEG) signal decoding is a key technology that translates brain activity into executable commands, laying the foundation for direct brain-machine interfacing and intelligent interaction. To address…
Eeg DecodingDecoding Motor Behavior Using Deep Learning and Reservoir Computing
We present a novel approach to EEG decoding for non-invasive brain machine interfaces (BMIs), with a focus on motor-behavior classification. While conventional convolutional architectures such as EEGNet and DeepConvNet a…
Eeg DecodingMind-to-Face: Neural-Driven Photorealistic Avatar Synthesis via EEG Decoding
Current expressive avatar systems rely heavily on visual cues, failing when faces are occluded or when emotions remain internal. We present Mind-to-Face, the first framework that decodes non-invasive electroencephalogram…
Eeg DecodingGCMCG: A Clustering-Aware Graph Attention and Expert Fusion Network for Multi-Paradigm, Multi-task, and Cross-Subject EEG Decoding
Brain-Computer Interfaces (BCIs) based on Motor Execution (ME) and Motor Imagery (MI) electroencephalogram (EEG) signals offer a direct pathway for human-machine interaction. However, developing robust decoding models re…
Eeg DecodingGeometry-Aware Deep Congruence Networks for Manifold Learning in Cross-Subject Motor Imagery
Cross-subject motor imagery decoding remains a fundamental challenge in EEG-based brain-computer interfaces due to substantial inter-subject variability. Recent approaches have leveraged Riemannian geometry by representi…
Eeg DecodingPrototype-Guided Non-Exemplar Continual Learning for Cross-subject EEG Decoding
Due to the significant variability in electroencephalo-gram (EEG) signals across individuals, knowledge acquired from previous subjects is often overwritten as new subjects are introduced in continual EEG decoding tasks.…
Continual LearningEeg DecodingMultiDiffNet: A Multi-Objective Diffusion Framework for Generalizable Brain Decoding
Neural decoding from electroencephalography (EEG) remains fundamentally limited by poor generalization to unseen subjects, driven by high inter-subject variability and the lack of large-scale datasets to model it effecti…
Data AugmentationBrain DecodingEeg DecodingFeasibility of Embodied Dynamics Based Bayesian Learning for Continuous Pursuit Motion Control of Assistive Mobile Robots in the Built Environment
Non-invasive electroencephalography (EEG)-based brain-computer interfaces (BCIs) offer an intuitive means for individuals with severe motor impairments to independently operate assistive robotic wheelchairs and navigate …
Bayesian InferenceAutonomous DrivingTransfer LearningEeg DecodingInformed Bootstrap Augmentation Improves EEG Decoding
Electroencephalography (EEG) offers detailed access to neural dynamics but remains constrained by noise and trial-by-trial variability, limiting decoding performance in data-restricted or complex paradigms. Data augmenta…
Data AugmentationEeg DecodingToward Practical BCI: A Real-time Wireless Imagined Speech EEG Decoding System
Brain-computer interface (BCI) research, while promising, has largely been confined to static and fixed environments, limiting real-world applicability. To move towards practical BCI, we introduce a real-time wireless im…
Eeg DecodingBalancing Interpretability and Performance in Motor Imagery EEG Classification: A Comparative Study of ANFIS-FBCSP-PSO and EEGNet
Achieving both accurate and interpretable classification of motor-imagery EEG remains a key challenge in brain-computer interface (BCI) research. In this paper, we compare a transparent fuzzy-reasoning approach (ANFIS-FB…
Eeg DecodingReconstructing Unseen Sentences from Speech-related Biosignals for Open-vocabulary Neural Communication
Brain-to-speech (BTS) systems represent a groundbreaking approach to human communication by enabling the direct transformation of neural activity into linguistic expressions. While recent non-invasive BTS studies have la…
Speech SynthesisEeg Decoding