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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 441–450 of 466 papers

TitleStatusHype
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
Classification of Upper Arm Movements from EEG signals using Machine Learning with ICA Analysis—0
Classification of Visual Perception and Imagery based EEG Signals Using Convolutional Neural Networks—0
Classifying Single-Trial EEG during Motor Imagery with a Small Training Set—0
Cloud-based Deep Learning of Big EEG Data for Epileptic Seizure Prediction—0
Common Spatial Generative Adversarial Networks based EEG Data Augmentation for Cross-Subject Brain-Computer Interface—0
Comparison of Sub-Scalp EEG and Endovascular Stent-Electrode Array for Visual Evoked Potential Brain-Computer Interface—0
Confidence-Aware Subject-to-Subject Transfer Learning for Brain-Computer Interface—0
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