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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 5160 of 466 papers

TitleStatusHype
EEG motor imagery decoding: A framework for comparative analysis with channel attention mechanismsCode1
EEG Synthetic Data Generation Using Probabilistic Diffusion ModelsCode1
AGTCNet: A Graph-Temporal Approach for Principled Motor Imagery EEG ClassificationCode1
SPD domain-specific batch normalization to crack interpretable unsupervised domain adaptation in EEGCode1
A 1D CNN for high accuracy classification and transfer learning in motor imagery EEG-based brain-computer interfaceCode1
FBCNet: A Multi-view Convolutional Neural Network for Brain-Computer InterfaceCode1
FingerFlex: Inferring Finger Trajectories from ECoG signalsCode1
The Discriminative Kalman Filter for Bayesian Filtering with Nonlinear and Nongaussian Observation ModelsCode1
Priming Cross-Session Motor Imagery Classification with A Universal Deep Domain Adaptation FrameworkCode1
T-TIME: Test-Time Information Maximization Ensemble for Plug-and-Play BCIsCode1
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