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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 321330 of 466 papers

TitleStatusHype
Comparison of Classification Algorithms Towards Subject-Specific and Subject-Independent BCICode0
Light-Weight 1-D Convolutional Neural Network Architecture for Mental Task Identification and Classification Based on Single-Channel EEG0
Improving EEG Decoding via Clustering-based Multi-task Feature Learning0
An algorithm for onset detection of linguistic segments in continuous electroencephalogram signalsCode0
T-WaveNet: Tree-Structured Wavelet Neural Network for Sensor-Based Time Series Analysis0
Cross-Correlation Based Discriminant Criterion for Channel Selection in Motor Imagery BCI Systems0
Deep Learning in EEG: Advance of the Last Ten-Year Critical Period0
Interval-valued aggregation functions based on moderate deviations applied to Motor-Imagery-Based Brain Computer Interface0
A Deep Neural Network for SSVEP-based Brain-Computer InterfacesCode1
Deep learning-based classification of fine hand movements from low frequency EEG0
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