Papers Eeg Decoding
“Eeg Decoding” 태그가 달린 논문 130편 · 필터 해제
ViEEG: Hierarchical Neural Coding with Cross-Modal Progressive Enhancement for EEG-Based Visual Decoding
Understanding and decoding brain activity into visual representations is a fundamental challenge at the intersection of neuroscience and artificial intelligence. While EEG-based visual decoding has shown promise due to i…
Brain DecodingContrastive LearningEEGEeg Decoding+1EEG-CLIP : Learning EEG representations from natural language descriptions
Deep networks for electroencephalogram (EEG) decoding are currently often trained to only solve a specific task like pathology or gender decoding. A more general approach leveraging the medical reports of clinical EEG re…
Contrastive LearningEEGEeg DecodingElectroencephalogram (EEG)MVCNet: Multi-View Contrastive Network for Motor Imagery Classification
Electroencephalography (EEG)-based brain-computer interfaces (BCIs) enable neural interaction by decoding brain activity for external communication. Motor imagery (MI) decoding has received significant attention due to i…
Brain Computer InterfaceContrastive LearningData AugmentationEEG+3Subject Disentanglement Neural Network for Speech Envelope Reconstruction from EEG
Reconstructing speech envelopes from EEG signals is essential for exploring neural mechanisms underlying speech perception. Yet, EEG variability across subjects and physiological artifacts complicate accurate reconstruct…
DisentanglementEEGEeg DecodingEEG-ReMinD: Enhancing Neurodegenerative EEG Decoding through Self-Supervised State Reconstruction-Primed Riemannian Dynamics
The development of EEG decoding algorithms confronts challenges such as data sparsity, subject variability, and the need for precise annotations, all of which are vital for advancing brain-computer interfaces and enhanci…
EEGEeg DecodingIntegrating Language-Image Prior into EEG Decoding for Cross-Task Zero-Calibration RSVP-BCI
Rapid Serial Visual Presentation (RSVP)-based Brain-Computer Interface (BCI) is an effective technology used for information detection by detecting Event-Related Potentials (ERPs). The current RSVP decoding methods can p…
Brain Computer InterfaceEEGEeg DecodingImage RetrievalA3E: Aligned and Augmented Adversarial Ensemble for Accurate, Robust and Privacy-Preserving EEG Decoding
An electroencephalogram (EEG) based brain-computer interface (BCI) enables direct communication between the brain and external devices. However, EEG-based BCIs face at least three major challenges in real-world applicati…
Adversarial RobustnessBrain Computer InterfaceEEGEeg Decoding+3CBraMod: A Criss-Cross Brain Foundation Model for EEG Decoding
Electroencephalography (EEG) is a non-invasive technique to measure and record brain electrical activity, widely used in various BCI and healthcare applications. Early EEG decoding methods rely on supervised learning, li…
EEGEeg DecodingTowards Scalable Handwriting Communication via EEG Decoding and Latent Embedding Integration
In recent years, brain-computer interfaces have made advances in decoding various motor-related tasks, including gesture recognition and movement classification, utilizing electroencephalogram (EEG) data. These developme…
EEGEeg DecodingElectroencephalogram (EEG)Gesture RecognitionPersonalized Continual EEG Decoding: Retaining and Transferring Knowledge
The significant inter-subject variability in electroen-cephalogram (EEG) signals often results in substantial changes to neural network weights as data distributions shift. This variability frequently causes catastrophic…
Brain Computer InterfaceDomain AdaptationEEGEeg Decoding+3Dataset Refinement for Improving the Generalization Ability of the EEG Decoding Model
Electroencephalography (EEG) is a generally used neuroimaging approach in brain-computer interfaces due to its non-invasive characteristics and convenience, making it an effective tool for understanding human intentions.…
EEGEeg DecodingMotor ImageryIndependent Feature Enhanced Crossmodal Fusion for Match-Mismatch Classification of Speech Stimulus and EEG Response
It is crucial for auditory attention decoding to classify matched and mismatched speech stimuli with corresponding EEG responses by exploring their relationship. However, existing methods often adopt two independent netw…
EEGEeg DecodingDual-TSST: A Dual-Branch Temporal-Spectral-Spatial Transformer Model for EEG Decoding
The decoding of electroencephalography (EEG) signals allows access to user intentions conveniently, which plays an important role in the fields of human-machine interaction. To effectively extract sufficient characterist…
EEGEeg DecodingCTNet: A Convolutional Transformer Network for EEG-Based Motor Imagery Classification
Brain-computer interface (BCI) technology bridges the direct communication between the brain and machines, unlocking new possibilities for human interaction and rehabilitation. EEG-based motor imagery (MI) plays a pivota…
Brain Computer InterfaceEEGEeg DecodingMotor ImageryPassenger hazard perception based on EEG signals for highly automated driving vehicles
Enhancing the safety of autonomous vehicles is crucial, especially given recent accidents involving automated systems. As passengers in these vehicles, humans' sensory perception and decision-making can be integrated wit…
Autonomous VehiclesDecision MakingEEGEeg DecodingOn-device Learning of EEGNet-based Network For Wearable Motor Imagery Brain-Computer Interface
Electroencephalogram (EEG)-based Brain-Computer Interfaces (BCIs) have garnered significant interest across various domains, including rehabilitation and robotics. Despite advancements in neural network-based EEG decodin…
Brain Computer InterfaceEEGEeg DecodingElectroencephalogram (EEG)+1Towards Linguistic Neural Representation Learning and Sentence Retrieval from Electroencephalogram Recordings
Decoding linguistic information from non-invasive brain signals using EEG has gained increasing research attention due to its vast applicational potential. Recently, a number of works have adopted a generative-based fram…
EEGEeg DecodingElectroencephalogram (EEG)Representation Learning+3Explain EEG-based End-to-end Deep Learning Models in the Frequency Domain
The recent rise of EEG-based end-to-end deep learning models presents a significant challenge in elucidating how these models process raw EEG signals and generate predictions in the frequency domain. This challenge limit…
Deep LearningEEGEeg DecodingBeware of Overestimated Decoding Performance Arising from Temporal Autocorrelations in Electroencephalogram Signals
Researchers have reported high decoding accuracy (>95%) using non-invasive Electroencephalogram (EEG) signals for brain-computer interface (BCI) decoding tasks like image decoding, emotion recognition, auditory spatial a…
Brain Computer InterfaceEEGEeg DecodingElectroencephalogram (EEG)+2Combining Euclidean Alignment and Data Augmentation for BCI decoding
Automated classification of electroencephalogram (EEG) signals is complex due to their high dimensionality, non-stationarity, low signal-to-noise ratio, and variability between subjects. Deep neural networks (DNNs) have …
ClassificationData AugmentationEEGEeg Decoding+1