Papers SSVEP
“SSVEP” 태그가 달린 논문 51편 · 필터 해제
Non-invasive two-step strategy BCI: brain-muscle-hand interface
Brain-computer interface enables direct interaction between brain and device. However, common brain-computer interfaces often employ one-step strategy that rely on non-natural paradigms, such as SSVEP-BCI and MI-BCI, are…
Brain Computer InterfaceSSVEPSSVEP-BiMA: Bifocal Masking Attention Leveraging Native and Symmetric-Antisymmetric Components for Robust SSVEP Decoding
Brain-computer interface (BCI) based on steady-state visual evoked potentials (SSVEP) is a popular paradigm for its simplicity and high information transfer rate (ITR). Accurate and fast SSVEP decoding is crucial for rel…
Brain Computer InterfaceSSVEPHybrid Brain-Machine Interface: Integrating EEG and EMG for Reduced Physical Demand
We present a hybrid brain-machine interface (BMI) that integrates steady-state visually evoked potential (SSVEP)-based EEG and facial EMG to improve multimodal control and mitigate fatigue in assistive applications. Trad…
EEGSSVEPIncepFormerNet: A multi-scale multi-head attention network for SSVEP classification
In recent years, deep learning (DL) models have shown outstanding performance in EEG classification tasks, particularly in Steady-State Visually Evoked Potential(SSVEP)-based Brain-Computer-Interfaces(BCI)systems. DL met…
EEGSSVEPImproving SSVEP BCI Spellers With Data Augmentation and Language Models
Steady-State Visual Evoked Potential (SSVEP) spellers are a promising communication tool for individuals with disabilities. This Brain-Computer Interface utilizes scalp potential data from (electroencephalography) EEG el…
Brain Computer InterfaceData AugmentationEEGLanguage Modeling+2Adapting the Biological SSVEP Response to Artificial Neural Networks
Neuron importance assessment is crucial for understanding the inner workings of artificial neural networks (ANNs) and improving their interpretability and efficiency. This paper introduces a novel approach to neuron sign…
Decision MakingExplainable artificial intelligenceimage-classificationImage Classification+2iFuzzyTL: Interpretable Fuzzy Transfer Learning for SSVEP BCI System
The rapid evolution of Brain-Computer Interfaces (BCIs) has significantly influenced the domain of human-computer interaction, with Steady-State Visual Evoked Potentials (SSVEP) emerging as a notably robust paradigm. Thi…
Domain AdaptationEEGFew-Shot LearningSSVEP+1The VEP Booster: A Closed-Loop AI System for Visual EEG Biomarker Auto-generation
Effective visual brain-machine interfaces (BMI) is based on reliable and stable EEG biomarkers. However, traditional adaptive filter-based approaches may suffer from individual variations in EEG signals, while deep neura…
EEGSSVEPThe largest EEG-based BCI reproducibility study for open science: the MOABB benchmark
Objective. This study conduct an extensive Brain-computer interfaces (BCI) reproducibility analysis on open electroencephalography datasets, aiming to assess existing solutions and establish open and reproducible benchma…
EEGMotor ImagerySSVEPWithin-Session ERP+4S-JEPA: towards seamless cross-dataset transfer through dynamic spatial attention
Motivated by the challenge of seamless cross-dataset transfer in EEG signal processing, this article presents an exploratory study on the use of Joint Embedding Predictive Architectures (JEPAs). In recent years, self-sup…
Brain Computer InterfaceBrain DecodingEEGERP+4Lateral Control of Brain-Controlled Vehicle Based on SVM Probability Output Model
The non-stationary characteristics of EEG signal and the individual differences of brain-computer interfaces (BCIs) lead to poor performance in the control process of the brain-controlled vehicles (BCVs). In this paper, …
EEGSSVEPSSVEP-DAN: A Data Alignment Network for SSVEP-based Brain Computer Interfaces
Steady-state visual-evoked potential (SSVEP)-based brain-computer interfaces (BCIs) offer a non-invasive means of communication through high-speed speller systems. However, their efficiency heavily relies on individual t…
SSVEPA Brain-Computer Interface Augmented Reality Framework with Auto-Adaptive SSVEP Recognition
Brain-Computer Interface (BCI) initially gained attention for developing applications that aid physically impaired individuals. Recently, the idea of integrating BCI with Augmented Reality (AR) emerged, which uses BCI no…
Brain Computer InterfaceSSVEPSSVEP-Based BCI Wheelchair Control System
A brain-computer interface (BCI) is a system that allows a person to communicate or control the surroundings without depending on the brain's normal output pathways of peripheral nerves and muscles. A lot of successful a…
Brain Computer InterfaceEEGSSVEPBioGAP: a 10-Core FP-capable Ultra-Low Power IoT Processor, with Medical-Grade AFE and BLE Connectivity for Wearable Biosignal Processing
Wearable biosignal processing applications are driving significant progress toward miniaturized, energy-efficient Internet-of-Things solutions for both clinical and consumer applications. However, scaling toward high-den…
Brain Computer InterfaceSSVEPSource-Free Domain Adaptation for SSVEP-based Brain-Computer Interfaces
This paper presents a source free domain adaptation method for steady-state visually evoked potentials (SSVEP) based brain-computer interface (BCI) spellers. SSVEP-based BCI spellers assist individuals experiencing speec…
Brain Computer InterfaceDomain AdaptationPseudo LabelSource-Free Domain Adaptation+1Short-length SSVEP data extension by a novel generative adversarial networks based framework
Steady-state visual evoked potentials (SSVEPs) based brain-computer interface (BCI) has received considerable attention due to its high information transfer rate (ITR) and available quantity of targets. However, the perf…
Brain Computer InterfaceEEGElectroencephalogram (EEG)SSVEPInformation Transfer Rate in BCIs: Towards Tightly Integrated Symbiosis
The information transmission rate (ITR), or effective bit rate, is a popular and widely used information measurement metric, particularly popularized for SSVEP-based Brain-Computer (BCI) interfaces. By combining speed an…
Binary ClassificationSSVEPA Hybrid Brain-Computer Interface Using Motor Imagery and SSVEP Based on Convolutional Neural Network
The key to electroencephalography (EEG)-based brain-computer interface (BCI) lies in neural decoding, and its accuracy can be improved by using hybrid BCI paradigms, that is, fusing multiple paradigms. However, hybrid BC…
Brain Computer InterfaceEEGElectroencephalogram (EEG)Motor Imagery+1A Transformer-based deep neural network model for SSVEP classification
Steady-state visual evoked potential (SSVEP) is one of the most commonly used control signal in the brain-computer interface (BCI) systems. However, the conventional spatial filtering methods for SSVEP classification hig…
Brain Computer InterfaceClassificationEEGEEG Signal Classification+2