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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 151–175 of 466 papers

TitleStatusHype
Reputation-Based Federated Learning Defense to Mitigate Threats in EEG Signal Classification—0
EEG motor imagery decoding: A framework for comparative analysis with channel attention mechanismsCode1
A Convolutional Network Adaptation for Cortical Classification During Mobile Brain Imaging—0
Automatic Control of Reactive Brain Computer Interfaces—0
HappyFeat -- An interactive and efficient BCI framework for clinical applicationsCode1
Is controlling a brain-computer interface just a matter of presence of mind? The limits of cognitive-motor dissociation—0
Unidirectional brain-computer interface: Artificial neural network encoding natural images to fMRI response in the visual cortexCode0
Phase Synchrony Component Self-Organization in Brain Computer Interface—0
A Dynamic Domain Adaptation Deep Learning Network for EEG-based Motor Imagery Classification—0
Brief Architectural Survey of Biopotential Recording Front-Ends since the 1970s—0
Bootstrapping Adaptive Human-Machine Interfaces with Offline Reinforcement Learning—0
Pseudo-online framework for BCI evaluation: A MOABB perspective—0
Advancing Brain-Computer Interface System Performance in Hand Trajectory Estimation with NeuroKinect—0
Aggregating Intrinsic Information to Enhance BCI Performance through Federated LearningCode0
A Brain-Computer Interface Augmented Reality Framework with Auto-Adaptive SSVEP Recognition—0
Deep Learning Architecture for Motor Imaged Words—0
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
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