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Brain Computer Interface

A Brain-Computer Interface (BCI), also known as a Brain-Machine Interface (BMI), is a technology that enables direct communication between the brain and an external device, such as a computer or a machine, without the need for any muscular or peripheral nerve activity. Essentially, BCIs establish a direct pathway between the brain and an external device, allowing for bidirectional communication.

BCIs typically work by detecting and interpreting brain signals, which are then translated into commands that control external devices or provide feedback to the user. These brain signals can be detected through various methods, including electroencephalography (EEG), which measures electrical activity in the brain through electrodes placed on the scalp, or invasive techniques such as implanted electrodes.

Papers

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Showing 51–100 of 466 papers

TitleStatusHype
Channel Reflection: Knowledge-Driven Data Augmentation for EEG-Based Brain-Computer Interfaces—0
Federated Motor Imagery Classification for Privacy-Preserving Brain-Computer InterfacesCode0
Bi-Band ECoGNet for ECoG Decoding on Classification Task—0
Protecting Multiple Types of Privacy Simultaneously in EEG-based Brain-Computer Interfaces—0
Decoding Imagined Movement in People with Multiple Sclerosis for Brain-Computer Interface Translation—0
ArEEG_Words: Dataset for Envisioned Speech Recognition using EEG for Arabic Words—0
Dual Prototyping with Domain and Class Prototypes for Affective Brain-Computer Interface in Unseen Target Conditions—0
Towards Personalized Brain-Computer Interface Application Based on Endogenous EEG Paradigms—0
Imagined Speech and Visual Imagery as Intuitive Paradigms for Brain-Computer Interfaces—0
EEG-Based Speech Decoding: A Novel Approach Using Multi-Kernel Ensemble Diffusion Models—0
Dynamic Neural Communication: Convergence of Computer Vision and Brain-Computer Interface—0
Towards Unified Neural Decoding of Perceived, Spoken and Imagined Speech from EEG Signals—0
EEG-DCNet: A Fast and Accurate MI-EEG Dilated CNN Classification MethodCode0
Personalized Continual EEG Decoding: Retaining and Transferring Knowledge—0
User-wise Perturbations for User Identity Protection in EEG-Based BCIs—0
Feature Selection via Dynamic Graph-based Attention Block in MI-based EEG Signals—0
Neurophysiological Analysis in Motor and Sensory Cortices for Improving Motor Imagination—0
SPDIM: Source-Free Unsupervised Conditional and Label Shift Adaptation in EEG—0
Evaluation Of P300 Speller Performance Using Large Language Models Along With Cross-Subject TrainingCode0
EEG-based 90-Degree Turn Intention Detection for Brain-Computer Interface—0
EEG-based AI-BCI Wheelchair Advancement: A Brain-Computer Interfacing Wheelchair System Using Deep Learning Approach—0
Source Data Selection for Brain-Computer Interfaces based on Simple Features—0
AM-MTEEG: Multi-task EEG classification based on impulsive associative memory—0
Method for Evaluating the Number of Signal Sources and Application to Non-invasive Brain-computer Interface—0
Translating Mental Imaginations into Characters with Codebooks and Dynamics-Enhanced Decoding—0
Guess What I Think: Streamlined EEG-to-Image Generation with Latent Diffusion ModelsCode2
Enhancing EEG Signal Generation through a Hybrid Approach Integrating Reinforcement Learning and Diffusion Models—0
SEE: Semantically Aligned EEG-to-Text Translation—0
PHemoNet: A Multimodal Network for Physiological SignalsCode2
PiEEG-16 to Measure 16 EEG Channels with Raspberry Pi for Brain-Computer Interfaces and EEG devicesCode2
https://arxiv.org/pdf/2409.07491Code2
ART: Artifact Removal Transformer for Reconstructing Noise-Free Multichannel Electroencephalographic Signals—0
PiEEG-16 to Measure 16 EEG Channels with Raspberry Pi for Brain-Computer Interfaces and EEG devicesCode2
MixNet: Joining Force of Classical and Modern Approaches Toward the Comprehensive Pipeline in Motor Imagery EEG ClassificationCode0
CTNet: A Convolutional Transformer Network for EEG-Based Motor Imagery ClassificationCode3
Probabilistic Decomposed Linear Dynamical Systems for Robust Discovery of Latent Neural DynamicsCode0
Research Advances and New Paradigms for Biology-inspired Spiking Neural Networks—0
On-device Learning of EEGNet-based Network For Wearable Motor Imagery Brain-Computer Interface—0
Emotion-Agent: Unsupervised Deep Reinforcement Learning with Distribution-Prototype Reward for Continuous Emotional EEG Analysis—0
EEG Right & Left Voluntary Hand Movement-based Virtual Brain-Computer Interfacing Keyboard Using Hybrid Deep Learning Approach—0
RISE-iEEG: Robust to Inter-Subject Electrodes Implantation Variability iEEG ClassifierCode0
EEGMobile: Enhancing Speed and Accuracy in EEG-Based Gaze Prediction with Advanced Mobile ArchitecturesCode0
Dareplane: A modular open-source software platform for BCI research with application in closed-loop deep brain stimulationCode1
Domain Adaptation-Enhanced Searchlight: Enabling classification of brain states from visual perception to mental imageryCode0
Decoding Linguistic Representations of Human Brain—0
How Does a Single EEG Channel Tell Us About Brain States in Brain-Computer Interfaces ?—0
L-Sort: An Efficient Hardware for Real-time Multi-channel Spike Sorting with Localization—0
ESI-GAL: EEG Source Imaging-based Trajectory Estimation for Grasp and Lift Task—0
GET: A Generative EEG Transformer for Continuous Context-Based Neural Signals—0
Speech Imagery BCI Training Using Game with a PurposeCode0
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