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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

Showing 2130 of 466 papers

TitleStatusHype
Siamese Network with Dual Attention for EEG-Driven Social Learning: Bridging the Human-Robot Gap in Long-Tail Autonomous Driving0
Riemannian Geometry for the classification of brain states with intracortical brain-computer interfaces0
Optimized Feature Selection and Neural Network-Based Classification of Motor Imagery Using EEG Signals0
EEG2GAIT: A Hierarchical Graph Convolutional Network for EEG-based Gait Decoding0
Edge-Fog Computing-Enabled EEG Data Compression via Asymmetrical Variational Discrete Cosine Transform Network0
On Questions of Predictability and Control of an Intelligent System Using Probabilistic State-Transitions0
Spatial Distillation based Distribution Alignment (SDDA) for Cross-Headset EEG ClassificationCode1
Motor Imagery EEG Signals: Multi-Task Classification and Subject Identification with a Lightweight CNN0
Integrating Biological and Machine Intelligence: Attention Mechanisms in Brain-Computer Interfaces0
Minima Possible Weights: A Homogenous Deep Ensemble Method for Cross-Subject Motor Imagery ClassificationCode0
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