Papers Brain Computer Interface
“Brain Computer Interface” 태그가 달린 논문 466편 · 필터 해제
Towards Unified Neural Decoding of Perceived, Spoken and Imagined Speech from EEG Signals
Brain signals accompany various information relevant to human actions and mental imagery, making them crucial to interpreting and understanding human intentions. Brain-computer interface technology leverages this brain a…
Brain Computer InterfaceEEGDynamic Neural Communication: Convergence of Computer Vision and Brain-Computer Interface
Interpreting human neural signals to decode static speech intentions such as text or images and dynamic speech intentions such as audio or video is showing great potential as an innovative communication tool. Human commu…
Brain Computer InterfaceEEG-DCNet: A Fast and Accurate MI-EEG Dilated CNN Classification Method
The electroencephalography (EEG)-based motor imagery (MI) classification is a critical and challenging task in brain-computer interface (BCI) technology, which plays a significant role in assisting patients with function…
Brain Computer InterfaceEEGMotor ImageryUser-wise Perturbations for User Identity Protection in EEG-Based BCIs
Objective: An electroencephalogram (EEG)-based brain-computer interface (BCI) is a direct communication pathway between the human brain and a computer. Most research so far studied more accurate BCIs, but much less atten…
Brain Computer InterfaceEEGElectroencephalogram (EEG)Ethics+1Personalized 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+3Neurophysiological Analysis in Motor and Sensory Cortices for Improving Motor Imagination
Brain-computer interface (BCI) enables direct communication between the brain and external devices by decoding neural signals, offering potential solutions for individuals with motor impairments. This study explores the …
Brain Computer InterfaceEEGMotor ImageryFeature Selection via Dynamic Graph-based Attention Block in MI-based EEG Signals
Brain-computer interface (BCI) technology enables direct interaction between humans and computers by analyzing brain signals. Electroencephalogram (EEG) is one of the non-invasive tools used in BCI systems, providing hig…
Brain Computer InterfaceEEGElectroencephalogram (EEG)feature selection+1SPDIM: Source-Free Unsupervised Conditional and Label Shift Adaptation in EEG
The non-stationary nature of electroencephalography (EEG) introduces distribution shifts across domains (e.g., days and subjects), posing a significant challenge to EEG-based neurotechnology generalization. Without label…
Brain Computer InterfaceDomain AdaptationEEGEEG based sleep staging+2Evaluation Of P300 Speller Performance Using Large Language Models Along With Cross-Subject Training
Amyotrophic lateral sclerosis (ALS), a progressive neuromuscular degenerative disease, severely restricts patient communication capacity within a few years of onset, resulting in a significant deterioration of quality of…
Brain Computer InterfaceEEGEEG-based 90-Degree Turn Intention Detection for Brain-Computer Interface
Electroencephalography (EEG)--based turn intention prediction for lower limb movement is important to build an efficient brain-computer interface (BCI) system. This study investigates the feasibility of intention detecti…
Brain Computer InterfaceDimensionality ReductionEEGfeature selectionEEG-based AI-BCI Wheelchair Advancement: A Brain-Computer Interfacing Wheelchair System Using Deep Learning Approach
This study offers a revolutionary strategy to developing wheelchairs based on the Brain-Computer Interface (BCI) that incorporates Artificial Intelligence (AI) using a The device uses electroencephalogram (EEG) data to m…
Brain Computer InterfaceEEGElectroencephalogram (EEG)Source Data Selection for Brain-Computer Interfaces based on Simple Features
This paper demonstrates that simple features available during the calibration of a brain-computer interface can be utilized for source data selection to improve the performance of the brain-computer interface for a new t…
Brain Computer InterfaceMotor ImageryTransfer LearningAM-MTEEG: Multi-task EEG classification based on impulsive associative memory
Electroencephalogram-based brain-computer interface (BCI) has potential applications in various fields, but their development is hindered by limited data and significant cross-individual variability. Inspired by the prin…
Brain Computer InterfaceClassificationDecoderEEG+1Method for Evaluating the Number of Signal Sources and Application to Non-invasive Brain-computer Interface
This paper provides a brief introduction of the mathematical theory behind the time series unfolding method. The algorithms presented serve as a valuable mathematical and analytical tool for analyzing data collected from…
Brain Computer InterfaceTime SeriesTranslating Mental Imaginations into Characters with Codebooks and Dynamics-Enhanced Decoding
Advancements in non-invasive electroencephalogram (EEG)-based Brain-Computer Interface (BCI) technology have enabled communication through brain activity, offering significant potential for individuals with motor impairm…
Brain Computer InterfaceEEGElectroencephalogram (EEG)Guess What I Think: Streamlined EEG-to-Image Generation with Latent Diffusion Models
Generating images from brain waves is gaining increasing attention due to its potential to advance brain-computer interface (BCI) systems by understanding how brain signals encode visual cues. Most of the literature has …
Brain Computer InterfaceEEGImage GenerationSEE: Semantically Aligned EEG-to-Text Translation
Decoding neurophysiological signals into language is of great research interest within brain-computer interface (BCI) applications. Electroencephalography (EEG), known for its non-invasiveness, ease of use, and cost-effe…
Brain Computer InterfaceEEGLanguage ModelingLanguage Modelling+1Enhancing EEG Signal Generation through a Hybrid Approach Integrating Reinforcement Learning and Diffusion Models
The present study introduces an innovative approach to the synthesis of Electroencephalogram (EEG) signals by integrating diffusion models with reinforcement learning. This integration addresses key challenges associated…
Brain Computer InterfaceData AugmentationEEGElectroencephalogram (EEG)https://arxiv.org/pdf/2409.07491
This article introduces a cost-effective gateway into the fascinating world of neuroscience: the PIEEG-16, a versatile shield for RaspberryPi designed to measure 16 channels of various biosignals, including EEG (electr…
Brain Computer InterfaceEEGPiEEG-16 to Measure 16 EEG Channels with Raspberry Pi for Brain-Computer Interfaces and EEG devices
This article introduces a cost-effective gateway into the fascinating world of neuroscience: the PIEEG-16, a versatile shield for RaspberryPi designed to measure 16 channels of various biosignals, including EEG (electroe…
Brain Computer InterfaceEEG