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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 401–410 of 466 papers

TitleStatusHype
Reconfiguration of Brain Network between Resting-state and Oddball Paradigm—0
Towards Asynchronous Motor Imagery-Based Brain-Computer Interfaces: a joint training scheme using deep learning—0
Deep Transfer Learning for EEG-based Brain Computer Interface—0
Classification of EEG Signal based on non-Gaussian Neutral Vector—0
Multimodal Classification with Deep Convolutional-Recurrent Neural Networks for Electroencephalography—0
A neuro-inspired system for online learning and recognition of parallel spike trains, based on spike latency and heterosynaptic STDP—0
Improving brain computer interface performance by data augmentation with conditional Deep Convolutional Generative Adversarial Networks—0
Fast and Accurate Multiclass Inference for MI-BCIs Using Large Multiscale Temporal and Spectral FeaturesCode0
Brain-Computer Interface with Corrupted EEG Data: A Tensor Completion Approach—0
A Multi-Context Character Prediction Model for a Brain-Computer Interface—0
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