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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 201–225 of 466 papers

TitleStatusHype
SSVEP-Based BCI Wheelchair Control System—0
UniCoRN: Unified Cognitive Signal ReconstructioN bridging cognitive signals and human language—0
BioGAP: a 10-Core FP-capable Ultra-Low Power IoT Processor, with Medical-Grade AFE and BLE Connectivity for Wearable Biosignal Processing—0
EEG Decoding for Datasets with Heterogenous Electrode Configurations using Transfer Learning Graph Neural Networks—0
UMM: Unsupervised Mean-difference Maximization—0
Decoding Brain Motor Imagery with various Machine Learning techniques—0
The feasibility of combining communication BCIs with FES for individuals with locked-in syndrome—0
Source-Free Domain Adaptation for SSVEP-based Brain-Computer InterfacesCode0
SG-GAN: Fine Stereoscopic-Aware Generation for 3D Brain Point Cloud Up-sampling from a Single Image—0
Sequential Best-Arm Identification with Application to Brain-Computer Interface—0
Hyper-automation-The next peripheral for automation in IT industries—0
Comparison of Sub-Scalp EEG and Endovascular Stent-Electrode Array for Visual Evoked Potential Brain-Computer Interface—0
Time delay multi-feature correlation analysis to extract subtle dependencies from EEG signals—0
Decoding Neural Activity to Assess Individual Latent State in Ecologically Valid Contexts—0
Autoregressive models for biomedical signal processing—0
EEG Cortical Source Feature based Hand Kinematics Decoding using Residual CNN-LSTM Neural Network—0
Multimodal Brain-Computer Interface for In-Vehicle Driver Cognitive Load Measurement: Dataset and Baselines—0
Upper Limb Movement Execution Classification using Electroencephalography for Brain Computer Interface—0
Optimized EEG based mood detection with signal processing and deep neural networks for brain-computer interface—0
An embedding for EEG signals learned using a triplet loss—0
Sliced-Wasserstein on Symmetric Positive Definite Matrices for M/EEG SignalsCode0
Benchmarking framework for machine learning classification from fNIRS dataCode0
Cross-Subject Deep Transfer Models for Evoked Potentials in Brain-Computer Interface—0
Neurorehab: An Interface for Rehabilitation—0
Subject-Independent Brain-Computer Interfaces with Open-Set Subject Recognition—0
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