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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 271–280 of 466 papers

TitleStatusHype
Neural Network-Based Feature Extraction for Multi-Class Motor Imagery Classification—0
A Survey on Brain-Computer Interaction—0
Learning shared neural manifolds from multi-subject FMRI data—0
Confidence-Aware Subject-to-Subject Transfer Learning for Brain-Computer Interface—0
Interpretable Convolutional Neural Networks for Subject-Independent Motor Imagery Classification—0
Are Brain-Computer Interfaces Feasible with Integrated Photonic Chips?—0
Functional connectivity ensemble method to enhance BCI performance (FUCONE)Code1
End-to-end translation of human neural activity to speech with a dual-dual generative adversarial network—0
Topological Data Analysis (TDA) Techniques Enhance Hand Pose Classification from ECoG Neural Recordings—0
A case study on profiling of an EEG-based brain decoding interface on Cloud and Edge servers—0
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