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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 76100 of 466 papers

TitleStatusHype
PhyAAt: Physiology of Auditory Attention to Speech DatasetCode0
Improving SSVEP BCI Spellers With Data Augmentation and Language ModelsCode0
Adversarial Filtering Based Evasion and Backdoor Attacks to EEG-Based Brain-Computer InterfacesCode0
Geometric Neural Network based on Phase Space for BCI-EEG decodingCode0
Federated Motor Imagery Classification for Privacy-Preserving Brain-Computer InterfacesCode0
Fast and Accurate Multiclass Inference for MI-BCIs Using Large Multiscale Temporal and Spectral FeaturesCode0
Feature Weighting and Regularization of Common Spatial Patterns in EEG-Based Motor Imagery BCICode0
FOIT: Fast Online Instance Transfer for Improved EEG Emotion RecognitionCode0
Evaluating Fast Adaptability of Neural Networks for Brain-Computer InterfaceCode0
An algorithm for onset detection of linguistic segments in continuous electroencephalogram signalsCode0
Evaluation Of P300 Speller Performance Using Large Language Models Along With Cross-Subject TrainingCode0
Embedding neurophysiological signalsCode0
Exploring Embedding Methods in Binary Hyperdimensional Computing: A Case Study for Motor-Imagery based Brain-Computer InterfacesCode0
Minima Possible Weights: A Homogenous Deep Ensemble Method for Cross-Subject Motor Imagery ClassificationCode0
Benchmarking framework for machine learning classification from fNIRS dataCode0
EEG-DCNet: A Fast and Accurate MI-EEG Dilated CNN Classification MethodCode0
EEG-DG: A Multi-Source Domain Generalization Framework for Motor Imagery EEG ClassificationCode0
EEGMobile: Enhancing Speed and Accuracy in EEG-Based Gaze Prediction with Advanced Mobile ArchitecturesCode0
Deep Optimal Transport for Domain Adaptation on SPD ManifoldsCode0
Dataset combining EEG, eye-tracking, and high-speed video for ocular activity analysis across BCI paradigmsCode0
Converting Your Thoughts to Texts: Enabling Brain Typing via Deep Feature Learning of EEG SignalsCode0
Comparison of Classification Algorithms Towards Subject-Specific and Subject-Independent BCICode0
Complex common spatial patterns on time-frequency decomposed EEG for brain-computer interfaceCode0
CSSSTN: A Class-sensitive Subject-to-subject Semantic Style Transfer Network for EEG Classification in RSVP TasksCode0
A Temporal-Spectral Fusion Transformer with Subject-Specific Adapter for Enhancing RSVP-BCI DecodingCode0
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