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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 201–250 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 Classification of Brain Signals using Skip Connections—0
Subject-Independent Brain-Computer Interfaces with Open-Set Subject Recognition—0
Short-length SSVEP data extension by a novel generative adversarial networks based frameworkCode0
AMDET: Attention based Multiple Dimensions EEG Transformer for Emotion Recognition—0
Toward BCI-enabled Metaverse: A Joint Learning and Resource Allocation Approach—0
Hybrid Paradigm-based Brain-Computer Interface for Robotic Arm Control—0
Calibration-Free Driver Drowsiness Classification based on Manifold-Level AugmentationCode0
CropCat: Data Augmentation for Smoothing the Feature Distribution of EEG Signals—0
Classification of Distraction Levels Using Hybrid Deep Neural Networks From EEG Signals—0
A Hybrid Brain-Computer Interface Using Motor Imagery and SSVEP Based on Convolutional Neural Network—0
EEG Opto-processor: epileptic seizure detection using diffractive photonic computing units—0
EEG-NeXt: A Modernized ConvNet for The Classification of Cognitive Activity from EEG—0
Target-centered Subject Transfer Framework for EEG Data Augmentation—0
Brain Computer Interface: Deep Learning Approach to Predict Human Emotion Recognition—0
Brain informed transfer learning for categorizing construction hazards—0
Motor imagery classification using EEG spectrograms—0
Towards Fast Single-Trial Online ERP based Brain-Computer Interface using dry EEG electrodes and neural networks: a pilot studyCode0
Embedding neurophysiological signalsCode0
Stimulus-Informed Generalized Canonical Correlation Analysis of Stimulus-Following Brain ResponsesCode0
Toward the application of XAI methods in EEG-based systems—0
Deep learning for ECoG brain-computer interface: end-to-end vs. hand-crafted features—0
In the realm of hybrid Brain: Human Brain and AI—0
On The Effects Of Data Normalisation For Domain Adaptation On EEG Data—0
Mental arithmetic task classification with convolutional neural network based on spectral-temporal features from EEG—0
A Novel Semi-supervised Meta Learning Method for Subject-transfer Brain-computer Interface—0
Subject-independent trajectory prediction using pre-movement EEG during grasp and lift task—0
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