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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 176–200 of 466 papers

TitleStatusHype
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
LMDA-Net:A lightweight multi-dimensional attention network for general EEG-based brain-computer interface paradigms and interpretabilityCode1
An embedding for EEG signals learned using a triplet loss—0
Sliced-Wasserstein on Symmetric Positive Definite Matrices for M/EEG SignalsCode0
Natural scene reconstruction from fMRI signals using generative latent diffusionCode1
EEG Synthetic Data Generation Using Probabilistic Diffusion ModelsCode1
Benchmarking framework for machine learning classification from fNIRS dataCode0
Deep comparisons of Neural Networks from the EEGNet familyCode1
Cross-Subject Deep Transfer Models for Evoked Potentials in Brain-Computer Interface—0
Neurorehab: An Interface for Rehabilitation—0
Device JNEEG to convert Jetson Nano to brain-Computer interfaces. Short reportCode1
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
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