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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 371380 of 466 papers

TitleStatusHype
Active Semi-supervised Transfer Learning (ASTL) for Offline BCI Calibration0
Adaptive neural network classifier for decoding MEG signals0
Advancing Brain-Computer Interface System Performance in Hand Trajectory Estimation with NeuroKinect0
A Dynamic Domain Adaptation Deep Learning Network for EEG-based Motor Imagery Classification0
A GA-based feature selection of the EEG signals by classification evaluation: Application in BCI systems0
Agreement Rate Initialized Maximum Likelihood Estimator for Ensemble Classifier Aggregation and Its Application in Brain-Computer Interface0
A Hybrid Brain-Computer Interface Using Motor Imagery and SSVEP Based on Convolutional Neural Network0
A Literature Review on the Smart Wheelchair Systems0
A Low-complexity Brain-computer Interface for High-complexity Robot Swarm Control0
AMDET: Attention based Multiple Dimensions EEG Transformer for Emotion Recognition0
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