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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 81–90 of 466 papers

TitleStatusHype
A Novel Semi-supervised Meta Learning Method for Subject-transfer Brain-computer Interface—0
An intertwined neural network model for EEG classification in brain-computer interfaces—0
A3E: Aligned and Augmented Adversarial Ensemble for Accurate, Robust and Privacy-Preserving EEG Decoding—0
A New Method for Features Normalization in Motor Imagery Few-Shot Learning using Resting-State—0
A neuro-inspired system for online learning and recognition of parallel spike trains, based on spike latency and heterosynaptic STDP—0
A GA-based feature selection of the EEG signals by classification evaluation: Application in BCI systems—0
4D Attention-based Neural Network for EEG Emotion Recognition—0
Covariance Density Neural Networks—0
Canonical Polyadic Decomposition with Auxiliary Information for Brain Computer Interface—0
A Neural-Inspired Architecture for EEG-Based Auditory Attention Detection—0
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