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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 51–75 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
EEG-Based Speech Decoding: A Novel Approach Using Multi-Kernel Ensemble Diffusion Models—0
Towards Unified Neural Decoding of Perceived, Spoken and Imagined Speech from EEG Signals—0
Dynamic Neural Communication: Convergence of Computer Vision and Brain-Computer Interface—0
Imagined Speech and Visual Imagery as Intuitive Paradigms for Brain-Computer Interfaces—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
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