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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 226–250 of 466 papers

TitleStatusHype
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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