SOTAVerified

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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 376–400 of 466 papers

TitleStatusHype
Agreement Rate Initialized Maximum Likelihood Estimator for Ensemble Classifier Aggregation and Its Application in Brain-Computer Interface—0
A Hybrid Brain-Computer Interface Using Motor Imagery and SSVEP Based on Convolutional Neural Network—0
A Literature Review on the Smart Wheelchair Systems—0
A Low-complexity Brain-computer Interface for High-complexity Robot Swarm Control—0
AMDET: Attention based Multiple Dimensions EEG Transformer for Emotion Recognition—0
AM-MTEEG: Multi-task EEG classification based on impulsive associative memory—0
A multi-agent control framework for co-adaptation in brain-computer interfaces—0
A Multi-Context Character Prediction Model for a Brain-Computer Interface—0
An Accurate EEGNet-based Motor-Imagery Brain-Computer Interface for Low-Power Edge Computing—0
An Adaptive Contrastive Learning Model for Spike Sorting—0
Adaptive Subspace Sampling for Class Imbalance Processing-Some clarifications, algorithm, and further investigation including applications to Brain Computer Interface—0
Analysis of artifacts in EEG signals for building BCIs—0
An amplitudes-perturbation data augmentation method in convolutional neural networks for EEG decoding—0
An Analysis of the Accuracy of the P300 BCI—0
An embedding for EEG signals learned using a triplet loss—0
An Ensemble Learning Based Classification of Individual Finger Movement from EEG—0
A Neural-Inspired Architecture for EEG-Based Auditory Attention Detection—0
A neuro-inspired system for online learning and recognition of parallel spike trains, based on spike latency and heterosynaptic STDP—0
A New Method for Features Normalization in Motor Imagery Few-Shot Learning using Resting-State—0
An intertwined neural network model for EEG classification in brain-computer interfaces—0
A Novel Semi-supervised Meta Learning Method for Subject-transfer Brain-computer Interface—0
Application of Common Spatial Patterns in Gravitational Waves Detection—0
An overview of deep learning techniques for epileptic seizures detection and prediction based on neuroimaging modalities: Methods, challenges, and future works—0
Are Brain-Computer Interfaces Feasible with Integrated Photonic Chips?—0
ArEEG_Chars: Dataset for Envisioned Speech Recognition using EEG for Arabic Characters—0
Show:102550
← PrevPage 16 of 19Next →

No leaderboard results yet.