Papers Brain Computer Interface
“Brain Computer Interface” 태그가 달린 논문 466편 · 필터 해제
Improving 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+2Low count of optically pumped magnetometers furnishes a reliable real-time access to sensorimotor rhythm
This study presents an analysis of sensorimotor rhythms using an advanced, optically-pumped magnetoencephalography (OPM-MEG) system - a novel and rapidly developing technology. We conducted real-movement and motor imager…
Brain Computer InterfaceMotor ImageryRhythmCanine EEG Helps Human: Cross-Species and Cross-Modality Epileptic Seizure Detection via Multi-Space Alignment
Epilepsy significantly impacts global health, affecting about 65 million people worldwide, along with various animal species. The diagnostic processes of epilepsy are often hindered by the transient and unpredictable nat…
Brain Computer InterfaceDiagnosticDomain AdaptationEEG+3A3E: 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+3Imagined Speech State Classification for Robust Brain-Computer Interface
This study examines the effectiveness of traditional machine learning classifiers versus deep learning models for detecting the imagined speech using electroencephalogram data. Specifically, we evaluated conventional mac…
Brain Computer InterfaceDeep LearningRepresentation LearningEEG-GMACN: Interpretable EEG Graph Mutual Attention Convolutional Network
Electroencephalogram (EEG) is a valuable technique to record brain electrical activity through electrodes placed on the scalp. Analyzing EEG signals contributes to the understanding of neurological conditions and develop…
Brain Computer InterfaceEEGElectroencephalogram (EEG)Transfer Learning with Active Sampling for Rapid Training and Calibration in BCI-P300 Across Health States and Multi-centre Data
Machine learning and deep learning advancements have boosted Brain-Computer Interface (BCI) performance, but their wide-scale applicability is limited due to factors like individual health, hardware variations, and cultu…
Brain Computer InterfaceTransfer LearningRobust Feature Engineering Techniques for Designing Efficient Motor Imagery-Based BCI-Systems
A multitude of individuals across the globe grapple with motor disabilities. Neural prosthetics utilizing Brain-Computer Interface (BCI) technology exhibit promise for improving motor rehabilitation outcomes. The intrica…
Brain Computer InterfaceEEGFeature EngineeringMotor ImageryAdversarial Filtering Based Evasion and Backdoor Attacks to EEG-Based Brain-Computer Interfaces
A brain-computer interface (BCI) enables direct communication between the brain and an external device. Electroencephalogram (EEG) is a common input signal for BCIs, due to its convenience and low cost. Most research on …
Brain Computer InterfaceEEGElectroencephalogram (EEG)T-TIME: Test-Time Information Maximization Ensemble for Plug-and-Play BCIs
Objective: An electroencephalogram (EEG)-based brain-computer interface (BCI) enables direct communication between the human brain and a computer. Due to individual differences and non-stationarity of EEG signals, such B…
Brain Computer InterfaceEEGElectroencephalogram (EEG)Ensemble Learning+3Channel Reflection: Knowledge-Driven Data Augmentation for EEG-Based Brain-Computer Interfaces
A brain-computer interface (BCI) enables direct communication between the human brain and external devices. Electroencephalography (EEG) based BCIs are currently the most popular for able-bodied users. To increase user-f…
Brain Computer InterfaceData AugmentationEEGMotor ImageryFederated Motor Imagery Classification for Privacy-Preserving Brain-Computer Interfaces
Training an accurate classifier for EEG-based brain-computer interface (BCI) requires EEG data from a large number of users, whereas protecting their data privacy is a critical consideration. Federated learning (FL) is a…
Brain Computer InterfaceEEGFederated LearningMotor Imagery+1Bi-Band ECoGNet for ECoG Decoding on Classification Task
In the application of brain-computer interface (BCI), being able to accurately decode brain signals is a critical task. For the multi-class classification task of brain signal ECoG, how to improve the classification accu…
Brain Computer InterfaceMulti-class ClassificationProtecting Multiple Types of Privacy Simultaneously in EEG-based Brain-Computer Interfaces
A brain-computer interface (BCI) enables direct communication between the brain and an external device. Electroencephalogram (EEG) is the preferred input signal in non-invasive BCIs, due to its convenience and low cost. …
Brain Computer InterfaceEEGElectroencephalogram (EEG)ArEEG_Words: Dataset for Envisioned Speech Recognition using EEG for Arabic Words
Brain-Computer-Interface (BCI) aims to support communication-impaired patients by translating neural signals into speech. A notable research topic in BCI involves Electroencephalography (EEG) signals that measure the ele…
Brain Computer InterfaceEEGspeech-recognitionSpeech RecognitionDecoding Imagined Movement in People with Multiple Sclerosis for Brain-Computer Interface Translation
Multiple Sclerosis (MS) is a heterogeneous autoimmune-mediated disorder affecting the central nervous system, commonly manifesting as fatigue and progressive limb impairment. This can significantly impact quality of life…
Brain Computer InterfaceEEGMotor ImageryDual Prototyping with Domain and Class Prototypes for Affective Brain-Computer Interface in Unseen Target Conditions
EEG signals have emerged as a powerful tool in affective brain-computer interfaces, playing a crucial role in emotion recognition. However, current deep transfer learning-based methods for EEG recognition face challenges…
Brain Computer InterfaceDisentanglementEEGEmotion Recognition+1Towards Personalized Brain-Computer Interface Application Based on Endogenous EEG Paradigms
In this paper, we propose a conceptual framework for personalized brain-computer interface (BCI) applications, which can offer an enhanced user experience by customizing services to individual preferences and needs, base…
Brain Computer InterfaceClassificationEEGMotor Imagery+1Imagined Speech and Visual Imagery as Intuitive Paradigms for Brain-Computer Interfaces
Brain-computer interfaces (BCIs) have shown promise in enabling communication for individuals with motor impairments. Recent advancements like brain-to-speech technology aim to reconstruct speech from neural activity. Ho…
Brain Computer InterfaceEEGFunctional ConnectivityEEG-Based Speech Decoding: A Novel Approach Using Multi-Kernel Ensemble Diffusion Models
In this study, we propose an ensemble learning framework for electroencephalogram-based overt speech classification, leveraging denoising diffusion probabilistic models with varying convolutional kernel sizes. The ensemb…
Brain Computer InterfaceDenoisingEEGEnsemble Learning