medical-8
Brain Computer Interface
3260 papers • 126 benchmarks • 313 datasets
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.
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Priming Cross-Session Motor Imagery Classification with A Universal Deep Domain Adaptation Framework
Zhengqing Miao, Mei-rong Zhao, Xin Zhang, C. Menon, Yelong Zheng, Dong Ming•Fri Feb 18 2022
The proposed Siamese deep domain adaptation (SDDA) framework for cross-session MI classification based on mathematical models in domain adaptation theory can be easily applied to most existing artificial neural networks without altering the network structure, which facilitates the method with great flexibility and transferability.