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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
EEG Synthetic Data Generation Using Probabilistic Diffusion ModelsCode1
Cross Task Neural Architecture Search for EEG Signal ClassificationsCode1
Closed loop BCI System for Cybathlon 2020Code1
DTP-Net: Learning to Reconstruct EEG signals in Time-Frequency Domain by Multi-scale Feature ReuseCode1
EEG-Inception: An Accurate and Robust End-to-End Neural Network for EEG-based Motor Imagery ClassificationCode1
AGTCNet: A Graph-Temporal Approach for Principled Motor Imagery EEG ClassificationCode1
A Deep Neural Network for SSVEP-based Brain-Computer InterfacesCode1
A magnetoencephalography dataset for motor and cognitive imagery-based brain-computer interfaceCode1
FingerFlex: Inferring Finger Trajectories from ECoG signalsCode1
CNN-based Approaches For Cross-Subject Classification in Motor Imagery: From The State-of-The-Art to DynamicNetCode1
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