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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 51–60 of 466 papers

TitleStatusHype
CNN-based Approaches For Cross-Subject Classification in Motor Imagery: From The State-of-The-Art to DynamicNetCode1
LGGNet: Learning from Local-Global-Graph Representations for Brain-Computer InterfaceCode1
A magnetoencephalography dataset for motor and cognitive imagery-based brain-computer interfaceCode1
FBCNet: A Multi-view Convolutional Neural Network for Brain-Computer InterfaceCode1
BENDR: using transformers and a contrastive self-supervised learning task to learn from massive amounts of EEG dataCode1
EEG-Inception: An Accurate and Robust End-to-End Neural Network for EEG-based Motor Imagery ClassificationCode1
A Deep Neural Network for SSVEP-based Brain-Computer InterfacesCode1
The Discriminative Kalman Filter for Bayesian Filtering with Nonlinear and Nongaussian Observation ModelsCode1
Tiny noise, big mistakes: Adversarial perturbations induce errors in Brain-Computer Interface spellersCode1
Different Set Domain Adaptation for Brain-Computer Interfaces: A Label Alignment ApproachCode1
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