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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

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Showing 376–400 of 466 papers

TitleStatusHype
Protecting Privacy of Users in Brain-Computer Interface Applications—0
Prototype-based Domain Generalization Framework for Subject-Independent Brain-Computer Interfaces—0
Pseudo-online framework for BCI evaluation: A MOABB perspective—0
Psychometry: An Omnifit Model for Image Reconstruction from Human Brain Activity—0
QSVM-QNN: Quantum Support Vector Machine Based Quantum Neural Network Learning Algorithm for Brain-Computer Interfacing Systems—0
Real or Virtual? Using Brain Activity Patterns to differentiate Attended Targets during Augmented Reality Scenarios—0
Real-Time Brain-Computer Interface Control of Walking Exoskeleton with Bilateral Sensory Feedback—0
Real-Time EEG Classification via Coresets for BCI Applications—0
Real Time Vigilance Detection using Frontal EEG—0
Reconfiguration of Brain Network between Resting-state and Oddball Paradigm—0
Reconfiguring motor circuits for a joint manual and BCI task—0
Reducing training requirements through evolutionary based dimension reduction and subject transfer—0
EPOC Emotiv EEG Basics—0
Reputation-Based Federated Learning Defense to Mitigate Threats in EEG Signal Classification—0
Research Advances and New Paradigms for Biology-inspired Spiking Neural Networks—0
Revisiting the Application of Feature Selection Methods to Speech Imagery BCI Datasets—0
Riemannian Geometry for the classification of brain states with intracortical brain-computer interfaces—0
Robust alignment of cross-session recordings of neural population activity by behaviour via unsupervised domain adaptation—0
Robust Feature Engineering Techniques for Designing Efficient Motor Imagery-Based BCI-Systems—0
Sample Dominance Aware Framework via Non-Parametric Estimation for Spontaneous Brain-Computer Interface—0
Second Order Bilinear Discriminant Analysis for single trial EEG analysis—0
SEE: Semantically Aligned EEG-to-Text Translation—0
Selection of Proper EEG Channels for Subject Intention Classification Using Deep Learning—0
Sequential Best-Arm Identification with Application to Brain-Computer Interface—0
SG-GAN: Fine Stereoscopic-Aware Generation for 3D Brain Point Cloud Up-sampling from a Single Image—0
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