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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

Showing 451460 of 466 papers

TitleStatusHype
Constrained Variational Autoencoder for improving EEG based Speech Recognition Systems0
Covariate Shift Estimation based Adaptive Ensemble Learning for Handling Non-Stationarity in Motor Imagery related EEG-based Brain-Computer Interface0
CropCat: Data Augmentation for Smoothing the Feature Distribution of EEG Signals0
Cross-Correlation Based Discriminant Criterion for Channel Selection in Motor Imagery BCI Systems0
Canine EEG Helps Human: Cross-Species and Cross-Modality Epileptic Seizure Detection via Multi-Space Alignment0
Cross-Subject Deep Transfer Models for Evoked Potentials in Brain-Computer Interface0
DAL: Feature Learning from Overt Speech to Decode Imagined Speech-based EEG Signals with Convolutional Autoencoder0
Decoding Brain Motor Imagery with various Machine Learning techniques0
Decoding Event-related Potential from Ear-EEG Signals based on Ensemble Convolutional Neural Networks in Ambulatory Environment0
Decoding finger flexion from band-specific ECoG signals in humans0
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