Papers Eeg Decoding
“Eeg Decoding” 태그가 달린 논문 130편 · 필터 해제
FusionGen: Feature Fusion-Based Few-Shot EEG Data Generation
Brain-computer interfaces (BCIs) provide potential for applications ranging from medical rehabilitation to cognitive state assessment by establishing direct communication pathways between the brain and external devices v…
Representation LearningEeg DecodingInvestigating the Impact of Rational Dilated Wavelet Transform on Motor Imagery EEG Decoding with Deep Learning Models
The present study investigates the impact of the Rational Discrete Wavelet Transform (RDWT), used as a plug-in preprocessing step for motor imagery electroencephalographic (EEG) decoding prior to applying deep learning c…
Eeg DecodingMitigating Subject Dependency in EEG Decoding with Subject-Specific Low-Rank Adapters
Subject-specific distribution shifts represent a fundamental obstacle to developing foundation models for brain decoding. We propose the Subject-Specific Low-Rank Adapter (SuLoRA), a drop-in replacement for standard line…
Brain DecodingEeg DecodingNeuroTTT: Bridging Pretraining-Downstream Task Misalignment in EEG Foundation Models via Test-Time Training
Large-scale foundation models for EEG signals offer a promising path to generalizable brain-computer interface (BCI) applications, but they often suffer from misalignment between pretraining objectives and downstream tas…
Eeg DecodingSpatial-Functional awareness Transformer-based graph archetype contrastive learning for Decoding Visual Neural Representations from EEG
Decoding visual neural representations from Electroencephalography (EEG) signals remains a formidable challenge due to their high-dimensional, noisy, and non-Euclidean nature. In this work, we propose a Spatial-Functiona…
Contrastive LearningBrain DecodingEeg DecodingBrainPro: Towards Large-scale Brain State-aware EEG Representation Learning
Electroencephalography (EEG) reflects underlying brain states, whose activities are distributed across brain regions and manifest as spatial patterns on the scalp. Learning these spatially structured, state-related patte…
Representation LearningEeg DecodingEEG-Driven AR-Robot System for Zero-Touch Grasping Manipulation
Reliable brain-computer interface (BCI) control of robots provides an intuitive and accessible means of human-robot interaction, particularly valuable for individuals with motor impairments. However, existing BCI-Robot s…
Robotic GraspingPose EstimationEeg DecodingSPICED: A Synaptic Homeostasis-Inspired Framework for Unsupervised Continual EEG Decoding
Human brain achieves dynamic stability-plasticity balance through synaptic homeostasis. Inspired by this biological principle, we propose SPICED: a neuromorphic framework that integrates the synaptic homeostasis mechanis…
Continual LearningEeg DecodingNeurocognitive Modeling for Text Generation: Deep Learning Architecture for EEG Data
Text generating capabilities have undergone a substantial transformation with the introduction of large language models (LLMs). Electroencephalography (EEG)-based text production is still difficult, though, because it re…
Transfer LearningText GenerationEeg DecodingFoundation Models for Cross-Domain EEG Analysis Application: A Survey
Electroencephalography (EEG) analysis stands at the forefront of neuroscience and artificial intelligence research, where foundation models are reshaping the traditional EEG analysis paradigm by leveraging their powerful…
Domain GeneralizationEeg DecodingPTSM: Physiology-aware and Task-invariant Spatio-temporal Modeling for Cross-Subject EEG Decoding
Cross-subject electroencephalography (EEG) decoding remains a fundamental challenge in brain-computer interface (BCI) research due to substantial inter-subject variability and the scarcity of subject-invariant representa…
Zero-shot GeneralizationEeg DecodingTowards Generalizable Learning Models for EEG-Based Identification of Pain Perception
EEG-based analysis of pain perception, enhanced by machine learning, reveals how the brain encodes pain by identifying neural patterns evoked by noxious stimulation. However, a major challenge that remains is the general…
Eeg DecodingTime-Resolved EEG Decoding of Semantic Processing Reveals Altered Neural Dynamics in Depression and Suicidality
Depression and suicidality affect cognitive and emotional processes, yet objective, task-evoked neural readouts of mental health remain limited. We investigated the spatiotemporal dynamics of affective semantic processin…
Eeg DecodingWhen Brain Foundation Model Meets Cauchy-Schwarz Divergence: A New Framework for Cross-Subject Motor Imagery Decoding
Decoding motor imagery (MI) electroencephalogram (EEG) signals, a key non-invasive brain-computer interface (BCI) paradigm for controlling external systems, has been significantly advanced by deep learning. However, cros…
Domain AdaptationEeg DecodingAFPM: 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 ImageryTransformer-based EEG Decoding: A Survey
Electroencephalography (EEG) is one of the most common signals used to capture the electrical activity of the brain, and the decoding of EEG, to acquire the user intents, has been at the forefront of brain-computer/machi…
Eeg DecodingSystem Filter-Based Common Components Modeling for Cross-Subject EEG Decoding
Brain-computer interface (BCI) technology enables direct communication between the brain and external devices through electroencephalography (EEG) signals. However, existing decoding models often mix common and personali…
Eeg DecodingDBConformer: 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 DetectionEEG 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)TCANet: 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 Imagery