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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 1–25 of 466 papers

TitleStatusHype
AGTCNet: A Graph-Temporal Approach for Principled Motor Imagery EEG ClassificationCode1
TCANet: A Temporal Convolutional Attention Network for Motor Imagery EEG DecodingCode1
The 2025 PNPL Competition: Speech Detection and Phoneme Classification in the LibriBrain Dataset—0
Dataset combining EEG, eye-tracking, and high-speed video for ocular activity analysis across BCI paradigmsCode0
Decoding Saccadic Eye Movements from Brain Signals Using an Endovascular Neural Interface—0
Minimally Invasive Brain Computer Interfaces: Evaluating the Impact of Tissue Layers on Signal Quality of Sub-Scalp EEG—0
Sub-Scalp EEG for Sensorimotor Brain-Computer Interface—0
EEG Foundation Models for BCI Learn Diverse Features of Electrophysiology—0
From Theory to Application: Fine-Tuning Large EEG Model with Real-World Stress Data—0
Non-invasive two-step strategy BCI: brain-muscle-hand interface—0
AbsoluteNet: A Deep Learning Neural Network to Classify Cerebral Hemodynamic Responses of Auditory Processing—0
BrainStratify: Coarse-to-Fine Disentanglement of Intracranial Neural Dynamics—0
The Study of Human Preference Based on Integrated Analysis of N1 and LPP Components—0
QSVM-QNN: Quantum Support Vector Machine Based Quantum Neural Network Learning Algorithm for Brain-Computer Interfacing Systems—0
Unlocking Non-Invasive Brain-to-Text—0
Covariance Density Neural Networks—0
Real-Time Brain-Computer Interface Control of Walking Exoskeleton with Bilateral Sensory Feedback—0
Pretraining Large Brain Language Model for Active BCI: Silent Speech—0
Sub-Scalp Brain-Computer Interface Device Design and Fabrication—0
Multi-scale convolutional transformer network for motor imagery brain-computer interfaceCode2
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
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