SOTAVerified

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 301310 of 466 papers

TitleStatusHype
Classification of Upper Arm Movements from EEG signals using Machine Learning with ICA Analysis0
DAL: Feature Learning from Overt Speech to Decode Imagined Speech-based EEG Signals with Convolutional Autoencoder0
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 Signals0
Wheelchair automation by a hybrid BCI system using SSVEP and eye blinks0
Subject-Independent Brain-Computer Interface for Decoding High-Level Visual Imagery Tasks0
Generating Ten BCI Commands Using Four Simple Motor Imageries0
An overview of deep learning techniques for epileptic seizures detection and prediction based on neuroimaging modalities: Methods, challenges, and future works0
Toward asynchronous EEG-based BCI: Detecting imagined words segments in continuous EEG signalsCode0
Source Aware Deep Learning Framework for Hand Kinematic Reconstruction using EEG Signal0
Show:102550
← PrevPage 31 of 47Next →

No leaderboard results yet.