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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 76–100 of 466 papers

TitleStatusHype
Pretraining Large Brain Language Model for Active BCI: Silent Speech—0
Sub-Scalp Brain-Computer Interface Device Design and Fabrication—0
Siamese Network with Dual Attention for EEG-Driven Social Learning: Bridging the Human-Robot Gap in Long-Tail Autonomous Driving—0
Riemannian Geometry for the classification of brain states with intracortical brain-computer interfaces—0
Optimized Feature Selection and Neural Network-Based Classification of Motor Imagery Using EEG Signals—0
EEG2GAIT: A Hierarchical Graph Convolutional Network for EEG-based Gait Decoding—0
Edge-Fog Computing-Enabled EEG Data Compression via Asymmetrical Variational Discrete Cosine Transform Network—0
On Questions of Predictability and Control of an Intelligent System Using Probabilistic State-Transitions—0
Integrating Biological and Machine Intelligence: Attention Mechanisms in Brain-Computer Interfaces—0
Motor Imagery EEG Signals: Multi-Task Classification and Subject Identification with a Lightweight CNN—0
Minima Possible Weights: A Homogenous Deep Ensemble Method for Cross-Subject Motor Imagery ClassificationCode0
SSVEP-BiMA: Bifocal Masking Attention Leveraging Native and Symmetric-Antisymmetric Components for Robust SSVEP Decoding—0
CSSSTN: A Class-sensitive Subject-to-subject Semantic Style Transfer Network for EEG Classification in RSVP TasksCode0
ISAM-MTL: Cross-subject multi-task learning model with identifiable spikes and associative memory networks—0
Interpretable Dual-Filter Fuzzy Neural Networks for Affective Brain-Computer Interfaces—0
Easing Seasickness through Attention Redirection with a Mindfulness-Based Brain--Computer Interface—0
Towards Probabilistic Inference of Human Motor Intentions by Assistive Mobile Robots Controlled via a Brain-Computer Interface—0
Integrating Language-Image Prior into EEG Decoding for Cross-Task Zero-Calibration RSVP-BCI—0
Human-AI Teaming Using Large Language Models: Boosting Brain-Computer Interfacing (BCI) and Brain Research—0
Improving SSVEP BCI Spellers With Data Augmentation and Language ModelsCode0
Low count of optically pumped magnetometers furnishes a reliable real-time access to sensorimotor rhythm—0
Canine EEG Helps Human: Cross-Species and Cross-Modality Epileptic Seizure Detection via Multi-Space Alignment—0
A3E: Aligned and Augmented Adversarial Ensemble for Accurate, Robust and Privacy-Preserving EEG Decoding—0
EEG-GMACN: Interpretable EEG Graph Mutual Attention Convolutional Network—0
Imagined Speech State Classification for Robust Brain-Computer Interface—0
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