Papers Brain Computer Interface
“Brain Computer Interface” 태그가 달린 논문 466편 · 필터 해제
PHemoNet: A Multimodal Network for Physiological Signals
Emotion recognition is essential across numerous fields, including medical applications and brain-computer interface (BCI). Emotional responses include behavioral reactions, such as tone of voice and body movement, and c…
Brain Computer InterfaceEEGElectroencephalogram (EEG)Emotion Recognition+2ART: Artifact Removal Transformer for Reconstructing Noise-Free Multichannel Electroencephalographic Signals
Artifact removal in electroencephalography (EEG) is a longstanding challenge that significantly impacts neuroscientific analysis and brain-computer interface (BCI) performance. Tackling this problem demands advanced algo…
Brain Computer InterfaceComponent ClassificationDenoisingEEG+1PiEEG-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 InterfaceEEGMixNet: Joining Force of Classical and Modern Approaches Toward the Comprehensive Pipeline in Motor Imagery EEG Classification
Recent advances in deep learning (DL) have significantly impacted motor imagery (MI)-based brain-computer interface (BCI) systems, enhancing the decoding of electroencephalography (EEG) signals. However, most studies str…
Brain Computer InterfaceClassificationEEGMotor ImageryCTNet: 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 ImageryProbabilistic Decomposed Linear Dynamical Systems for Robust Discovery of Latent Neural Dynamics
Time-varying linear state-space models are powerful tools for obtaining mathematically interpretable representations of neural signals. For example, switching and decomposed models describe complex systems using latent v…
Brain Computer InterfaceState Space ModelsResearch Advances and New Paradigms for Biology-inspired Spiking Neural Networks
Spiking neural networks (SNNs) are gaining popularity in the computational simulation and artificial intelligence fields owing to their biological plausibility and computational efficiency. This paper explores the histor…
Automatic Speech RecognitionBrain Computer InterfaceComputational Efficiencycontinuous-control+3On-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)+1Emotion-Agent: Unsupervised Deep Reinforcement Learning with Distribution-Prototype Reward for Continuous Emotional EEG Analysis
Continuous electroencephalography (EEG) signals are widely used in affective brain-computer interface (aBCI) applications. However, not all continuously collected EEG signals are relevant or meaningful to the task at han…
Brain Computer InterfaceDeep Reinforcement LearningEEGEfficient ExplorationEEG Right & Left Voluntary Hand Movement-based Virtual Brain-Computer Interfacing Keyboard Using Hybrid Deep Learning Approach
Brain-machine interfaces (BMIs), particularly those based on electroencephalography (EEG), offer promising solutions for assisting individuals with motor disabilities. However, challenges in reliably interpreting EEG sig…
Brain Computer InterfaceDeep LearningEEGERP+1RISE-iEEG: Robust to Inter-Subject Electrodes Implantation Variability iEEG Classifier
Intracranial electroencephalography (iEEG) is increasingly used for clinical and brain-computer interface applications due to its high spatial and temporal resolution. However, inter-subject variability in electrode impl…
Brain Computer InterfaceDecoderEEGMobile: Enhancing Speed and Accuracy in EEG-Based Gaze Prediction with Advanced Mobile Architectures
Electroencephalography (EEG) analysis is an important domain in the realm of Brain-Computer Interface (BCI) research. To ensure BCI devices are capable of providing practical applications in the real world, brain signal …
Brain Computer InterfaceEEGGaze PredictionKnowledge Distillation+2Domain Adaptation-Enhanced Searchlight: Enabling classification of brain states from visual perception to mental imagery
In cognitive neuroscience and brain-computer interface research, accurately predicting imagined stimuli is crucial. This study investigates the effectiveness of Domain Adaptation (DA) in enhancing imagery prediction usin…
Binary ClassificationBrain Computer InterfaceBrain DecodingDomain Adaptation+2Dareplane: A modular open-source software platform for BCI research with application in closed-loop deep brain stimulation
Objective - This work introduces Dareplane, a modular and broad technology-agnostic open source software platform for brain-computer interface research with an application focus on adaptive deep brain stimulation (aDBS).…
Brain Computer InterfaceDecoding Linguistic Representations of Human Brain
Language, as an information medium created by advanced organisms, has always been a concern of neuroscience regarding how it is represented in the brain. Decoding linguistic representations in the evoked brain has shown …
Brain Computer InterfaceBrain DecodingHow Does a Single EEG Channel Tell Us About Brain States in Brain-Computer Interfaces ?
Over recent decades, neuroimaging tools, particularly electroencephalography (EEG), have revolutionized our understanding of the brain and its functions. EEG is extensively used in traditional brain-computer interface (B…
Brain Computer InterfaceEEGMotor ImageryL-Sort: An Efficient Hardware for Real-time Multi-channel Spike Sorting with Localization
Spike sorting is essential for extracting neuronal information from neural signals and understanding brain function. With the advent of high-density microelectrode arrays (HDMEAs), the challenges and opportunities in mul…
Brain Computer InterfaceClusteringSpike SortingESI-GAL: EEG Source Imaging-based Trajectory Estimation for Grasp and Lift Task
Electroencephalogram (EEG) signals-based motor kinematics prediction (MKP) has been an active area of research to develop brain-computer interface (BCI) systems such as exosuits, prostheses, and rehabilitation devices. H…
Brain Computer InterfaceDecoderEEGElectroencephalogram (EEG)+2GET: A Generative EEG Transformer for Continuous Context-Based Neural Signals
Generating continuous electroencephalography (EEG) signals through advanced artificial neural networks presents a novel opportunity to enhance brain-computer interface (BCI) technology. This capability has the potential …
Brain Computer InterfaceData AugmentationEEGMotor ImageryMAD: Multi-Alignment MEG-to-Text Decoding
Deciphering language from brain activity is a crucial task in brain-computer interface (BCI) research. Non-invasive cerebral signaling techniques including electroencephalography (EEG) and magnetoencephalography (MEG) ar…
Brain Computer InterfaceEEGText Generation