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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 451466 of 466 papers

TitleStatusHype
Embedding neurophysiological signalsCode0
Mining within-trial oscillatory brain dynamics to address the variability of optimized spatial filtersCode0
MixNet: Joining Force of Classical and Modern Approaches Toward the Comprehensive Pipeline in Motor Imagery EEG ClassificationCode0
Stimulus-Informed Generalized Canonical Correlation Analysis for Group Analysis of Neural Responses to Natural StimuliCode0
EEG-DG: A Multi-Source Domain Generalization Framework for Motor Imagery EEG ClassificationCode0
EEG-DCNet: A Fast and Accurate MI-EEG Dilated CNN Classification MethodCode0
A Temporal-Spectral Fusion Transformer with Subject-Specific Adapter for Enhancing RSVP-BCI DecodingCode0
Classification of Motor Imagery EEG Signals by Using a Divergence Based Convolutional Neural NetworkCode0
Unidirectional brain-computer interface: Artificial neural network encoding natural images to fMRI response in the visual cortexCode0
Short-length SSVEP data extension by a novel generative adversarial networks based frameworkCode0
Deep Optimal Transport for Domain Adaptation on SPD ManifoldsCode0
Fast and Accurate Multiclass Inference for MI-BCIs Using Large Multiscale Temporal and Spectral FeaturesCode0
Probabilistic Decomposed Linear Dynamical Systems for Robust Discovery of Latent Neural DynamicsCode0
Exploring Embedding Methods in Binary Hyperdimensional Computing: A Case Study for Motor-Imagery based Brain-Computer InterfacesCode0
Aggregating Intrinsic Information to Enhance BCI Performance through Federated LearningCode0
Toward asynchronous EEG-based BCI: Detecting imagined words segments in continuous EEG signalsCode0
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