Papers Subject Transfer
“Subject Transfer” 태그가 달린 논문 21편 · 필터 해제
Transferring Spatial Filters via Tangent Space Alignment in Motor Imagery BCIs
We propose a method to improve subject transfer in motor imagery BCIs by aligning covariance matrices on a Riemannian manifold, followed by computing a new common spatial patterns (CSP) based spatial filter. We explore v…
Motor ImagerySubject TransferContrastive Learning to Fine-Tune Feature Extraction Models for the Visual Cortex
Predicting the neural response to natural images in the visual cortex requires extracting relevant features from the images and relating those feature to the observed responses. In this work, we optimize the feature extr…
Contrastive LearningDimensionality Reductionimage-classificationImage Classification+1EZIGen: Enhancing zero-shot personalized image generation with precise subject encoding and decoupled guidance
Zero-shot personalized image generation models aim to produce images that align with both a given text prompt and subject image, requiring the model to effectively incorporate both sources of guidance. However, existing …
DenoisingImage GenerationPersonalized Image GenerationSubject TransferStabilizing Subject Transfer in EEG Classification with Divergence Estimation
Classification models for electroencephalogram (EEG) data show a large decrease in performance when evaluated on unseen test sub jects. We reduce this performance decrease using new regularization techniques during model…
EEGElectroencephalogram (EEG)Subject TransferTarget-centered Subject Transfer Framework for EEG Data Augmentation
Data augmentation approaches are widely explored for the enhancement of decoding electroencephalogram signals. In subject-independent brain-computer interface system, domain adaption and generalization are utilized to sh…
Brain Computer InterfaceData AugmentationDomain AdaptationEEG+2SCAM! Transferring humans between images with Semantic Cross Attention Modulation
A large body of recent work targets semantically conditioned image generation. Most such methods focus on the narrower task of pose transfer and ignore the more challenging task of subject transfer that consists in not o…
DiversityImage GenerationPose TransferReconstruction+1A Novel Semi-supervised Meta Learning Method for Subject-transfer Brain-computer Interface
Brain-computer interface (BCI) provides a direct communication pathway between human brain and external devices. Before a new subject could use BCI, a calibration procedure is usually required. Because the inter- and int…
Brain Computer InterfaceEmotion RecognitionMeta-LearningSleep Staging+2AutoTransfer: Subject Transfer Learning with Censored Representations on Biosignals Data
We provide a regularization framework for subject transfer learning in which we seek to train an encoder and classifier to minimize classification loss, subject to a penalty measuring independence between the latent repr…
EEGElectroencephalogram (EEG)Subject TransferTransfer LearningConfidence-Aware Subject-to-Subject Transfer Learning for Brain-Computer Interface
The inter/intra-subject variability of electroencephalography (EEG) makes the practical use of the brain-computer interface (BCI) difficult. In general, the BCI system requires a calibration procedure to tune the model e…
Brain Computer InterfaceEEGElectroencephalogram (EEG)Subject Transfer+1EEG-based Classification of Drivers Attention using Convolutional Neural Network
Accurate detection of a drivers attention state can help develop assistive technologies that respond to unexpected hazards in real time and therefore improve road safety. This study compares the performance of several at…
Brain Computer InterfaceClassificationEEGElectroencephalogram (EEG)+2MS-MDA: Multisource Marginal Distribution Adaptation for Cross-subject and Cross-session EEG Emotion Recognition
As an essential element for the diagnosis and rehabilitation of psychiatric disorders, the electroencephalogram (EEG) based emotion recognition has achieved significant progress due to its high precision and reliability.…
Domain AdaptationEEGEEG Emotion RecognitionElectroencephalogram (EEG)+2Towards Explainable, Privacy-Preserved Human-Motion Affect Recognition
Human motion characteristics are used to monitor the progression of neurological diseases and mood disorders. Since perceptions of emotions are also interleaved with body posture and movements, emotion recognition from h…
Emotion RecognitionSubject TransferTransfer LearningUniversal Physiological Representation Learning with Soft-Disentangled Rateless Autoencoders
Human computer interaction (HCI) involves a multidisciplinary fusion of technologies, through which the control of external devices could be achieved by monitoring physiological status of users. However, physiological bi…
DisentanglementRepresentation LearningSubject TransferDisentangled Adversarial Autoencoder for Subject-Invariant Physiological Feature Extraction
Recent developments in biosignal processing have enabled users to exploit their physiological status for manipulating devices in a reliable and safe manner. One major challenge of physiological sensing lies in the variab…
Subject TransferTransfer LearningAutoBayes: Automated Bayesian Graph Exploration for Nuisance-Robust Inference
Learning data representations that capture task-related features, but are invariant to nuisance variations remains a key challenge in machine learning. We introduce an automated Bayesian inference framework, called AutoB…
Bayesian InferenceBIG-bench Machine LearningDecoderEnsemble Learning+3Cross-Subject Transfer Learning in Human Activity Recognition Systems using Generative Adversarial Networks
Application of intelligent systems especially in smart homes and health-related topics has been drawing more attention in the last decades. Training Human Activity Recognition (HAR) models -- as a major module -- require…
Activity RecognitionGenerative Adversarial NetworkHuman Activity RecognitionSubject Transfer+1Cross-Subject Transfer Learning Improves the Practicality of Real-World Applications of Brain-Computer Interfaces
Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) have shown its robustness in facilitating high-efficiency communication. State-of-the-art training-based SSVEP decoding methods such as …
SSVEPSubject TransferTransfer LearningActive Semi-supervised Transfer Learning (ASTL) for Offline BCI Calibration
Single-trial classification of event-related potentials in electroencephalogram (EEG) signals is a very important paradigm of brain-computer interface (BCI). Because of individual differences, usually some subject-specif…
Active LearningBrain Computer InterfaceEEGElectroencephalogram (EEG)+2Interpretable Deep Neural Networks for Single-Trial EEG Classification
Background: In cognitive neuroscience the potential of Deep Neural Networks (DNNs) for solving complex classification tasks is yet to be fully exploited. The most limiting factor is that DNNs as notorious 'black boxes' d…
ClassificationEEGElectroencephalogram (EEG)General Classification+2Reducing training requirements through evolutionary based dimension reduction and subject transfer
Training Brain Computer Interface (BCI) systems to understand the intention of a subject through Electroencephalogram (EEG) data currently requires multiple training sessions with a subject in order to develop the necess…
Brain Computer InterfaceDimensionality ReductionEEGElectroencephalogram (EEG)+1