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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 141–150 of 466 papers

TitleStatusHype
Classification of Emerging Neural Activity from Planning to Grasp Execution using a Novel EEG-Based BCI Platform—0
An overview of deep learning techniques for epileptic seizures detection and prediction based on neuroimaging modalities: Methods, challenges, and future works—0
Classification of Electroencephalograms during Mathematical Calculations Using Deep Learning—0
Classification of EEG Signal based on non-Gaussian Neutral Vector—0
Application of Common Spatial Patterns in Gravitational Waves Detection—0
Agreement Rate Initialized Maximum Likelihood Estimator for Ensemble Classifier Aggregation and Its Application in Brain-Computer Interface—0
A Computationally Efficient Multiclass Time-Frequency Common Spatial Pattern Analysis on EEG Motor Imagery—0
Classification of EEG Motor Imagery Using Deep Learning for Brain-Computer Interface Systems—0
Classification of Distraction Levels Using Hybrid Deep Neural Networks From EEG Signals—0
A Novel Semi-supervised Meta Learning Method for Subject-transfer Brain-computer Interface—0
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