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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 101–110 of 466 papers

TitleStatusHype
Classification of Distraction Levels Using Hybrid Deep Neural Networks From EEG Signals—0
Classification of EEG Motor Imagery Using Deep Learning for Brain-Computer Interface Systems—0
Classification of EEG Signal based on non-Gaussian Neutral Vector—0
Classification of Electroencephalograms during Mathematical Calculations Using Deep Learning—0
Classification of Emerging Neural Activity from Planning to Grasp Execution using a Novel EEG-Based BCI Platform—0
Classification of fNIRS Data Under Uncertainty: A Bayesian Neural Network Approach—0
An Ensemble Learning Based Classification of Individual Finger Movement from EEG—0
A Dynamic Domain Adaptation Deep Learning Network for EEG-based Motor Imagery Classification—0
Classification of Upper Arm Movements from EEG signals using Machine Learning with ICA Analysis—0
Brain-Computer Interface with Corrupted EEG Data: A Tensor Completion Approach—0
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