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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 291–300 of 466 papers

TitleStatusHype
Complex common spatial patterns on time-frequency decomposed EEG for brain-computer interfaceCode0
Partial Maximum Correntropy Regression for Robust Trajectory Decoding from Noisy Epidural Electrocorticographic Signals—0
Transformer-based Spatial-Temporal Feature Learning for EEG DecodingCode1
Wheelchair automation by a hybrid BCI system using SSVEP and eye blinks—0
Subject-Independent Brain-Computer Interface for Decoding High-Level Visual Imagery Tasks—0
Generating Ten BCI Commands Using Four Simple Motor Imageries—0
An overview of deep learning techniques for epileptic seizures detection and prediction based on neuroimaging modalities: Methods, challenges, and future works—0
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
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