Papers EEG
“EEG” 태그가 달린 논문 2,431편 · 필터 해제
NeuroXAI: Adaptive, robust, explainable surrogate framework for determination of channel importance in EEG application
Electroencephalogram (EEG)-based applications often require numerous channels to achieve high performance, which limits their widespread use. Various channel selection methods have been proposed to identify minimum EEG c…
channel selectionEEGElectroencephalogram (EEG)Explainable Artificial Intelligence (XAI)+1Aligning Humans and Robots via Reinforcement Learning from Implicit Human Feedback
Conventional reinforcement learning (RL) ap proaches often struggle to learn effective policies under sparse reward conditions, necessitating the manual design of complex, task-specific reward functions. To address this …
EEGMuJoCoreinforcement-learningReinforcement Learning+1Uncertainty-Aware Cross-Modal Knowledge Distillation with Prototype Learning for Multimodal Brain-Computer Interfaces
Electroencephalography (EEG) is a fundamental modality for cognitive state monitoring in brain-computer interfaces (BCIs). However, it is highly susceptible to intrinsic signal errors and human-induced labeling errors, w…
EEGKnowledge DistillationAFPM: Alignment-based Frame Patch Modeling for Cross-Dataset EEG Decoding
Electroencephalogram (EEG) decoding models for brain-computer interfaces (BCIs) struggle with cross-dataset learning and generalization due to channel layout inconsistencies, non-stationary signal distributions, and limi…
EEGEeg DecodingElectroencephalogram (EEG)Motor ImageryCATVis: Context-Aware Thought Visualization
EEG-based brain-computer interfaces (BCIs) have shown promise in various applications, such as motor imagery and cognitive state monitoring. However, decoding visual representations from EEG signals remains a significant…
cross-modal alignmentEEGImage GenerationMotor Imagery+1An Automated Classifier of Harmful Brain Activities for Clinical Usage Based on a Vision-Inspired Pre-trained Framework
Timely identification of harmful brain activities via electroencephalography (EEG) is critical for brain disease diagnosis and treatment, which remains limited application due to inter-rater variability, resource constra…
Binary ClassificationEEGeegFloss: A Python package for refining sleep EEG recordings using machine learning models
Electroencephalography (EEG) allows monitoring of brain activity, providing insights into the functional dynamics of various brain regions and their roles in cognitive processes. EEG is a cornerstone in sleep research, s…
EEGSleep StagingDBConformer: Dual-Branch Convolutional Transformer for EEG Decoding
Electroencephalography (EEG)-based brain-computer interfaces (BCIs) transform spontaneous/evoked neural activity into control commands for external communication. While convolutional neural networks (CNNs) remain the mai…
EEGEeg DecodingMotor ImagerySeizure DetectionAGTCNet: A Graph-Temporal Approach for Principled Motor Imagery EEG Classification
Brain-computer interface (BCI) technology utilizing electroencephalography (EEG) marks a transformative innovation, empowering motor-impaired individuals to engage with their environment on equal footing. Despite its pro…
Brain Computer InterfaceEEGInductive BiasMotor ImageryThe time course of visuo-semantic representations in the human brain is captured by combining vision and language models
The human visual system provides us with a rich and meaningful percept of the world, transforming retinal signals into visuo-semantic representations. For a model of these representations, here we leveraged a combination…
EEGEEG Foundation Challenge: From Cross-Task to Cross-Subject EEG Decoding
Current electroencephalogram (EEG) decoding models are typically trained on small numbers of subjects performing a single task. Here, we introduce a large-scale, code-submission-based competition comprising two challenge…
EEGEeg DecodingElectroencephalogram (EEG)Cross-Modal Epileptic Signal Harmonization: Frequency Domain Mapping Quantization for Pre-training a Unified Neurophysiological Transformer
Scalp electroencephalography (EEG) and intracranial EEG (iEEG) are vital for epilepsy diagnosis and treatment. Their unified analysis offers the potential to harness the complementary strengths of each modality but is ch…
EEGQuantizationRepresentation LearningEfficient and faithful reconstruction of dynamical attractors using homogeneous differentiators
Reconstructing the attractors of complex nonlinear dynamical systems from available measurements is key to analyse and predict their time evolution. Existing attractor reconstruction methods typically rely on topological…
EEGOn using AI for EEG-based BCI applications: problems, current challenges and future trends
Imagine unlocking the power of the mind to communicate, create, and even interact with the world around us. Recent breakthroughs in Artificial Intelligence (AI), especially in how machines "see" and "understand" language…
EEGExploring EEG Indicators to Evaluate Listening Difficulties in Noisy Environments
Auditory processing difficulties involve challenges in understanding speech in noisy environments despite normal hearing. However, the neural mechanisms remain unclear, and standardized diagnostic criteria are lacking. T…
DiagnosticEEGLeveraging Transfer Learning and User-Specific Updates for Rapid Training of BCI Decoders
Lengthy subject- or session-specific data acquisition and calibration remain a key barrier to deploying electroencephalography (EEG)-based brain-computer interfaces (BCIs) outside the laboratory. Previous work has shown …
DecoderEEGTransfer LearningA Silent Speech Decoding System from EEG and EMG with Heterogenous Electrode Configurations
Silent speech decoding, which performs unvocalized human speech recognition from electroencephalography/electromyography (EEG/EMG), increases accessibility for speech-impaired humans. However, data collection is difficul…
EEGspeech-recognitionSpeech RecognitionTCANet: A Temporal Convolutional Attention Network for Motor Imagery EEG Decoding
Decoding motor imagery electroencephalogram (MI-EEG) signals is fundamental to the development of brain–computer interface (BCI) systems. However, robust decoding remains a challenge due to the inherent complexity and va…
Brain Computer InterfaceEEGEeg DecodingMotor ImageryCLEAN-MI: A Scalable and Efficient Pipeline for Constructing High-Quality Neurodata in Motor Imagery Paradigm
The construction of large-scale, high-quality datasets is a fundamental prerequisite for developing robust and generalizable foundation models in motor imagery (MI)-based brain-computer interfaces (BCIs). However, EEG si…
EEGMotor ImageryDifferences in Neurovascular Coupling in Patients with Major Depressive Disorder: Evidence from Simultaneous Resting-State EEG-fNIRS
Neurovascular coupling (NVC) refers to the process by which local neural activity, through energy consumption, induces changes in regional cerebral blood flow to meet the metabolic demands of neurons. Event-related studi…
EEG