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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 126–150 of 466 papers

TitleStatusHype
Cross-Subject Deep Transfer Models for Evoked Potentials in Brain-Computer Interface—0
A SPA-based Manifold Learning Framework for Motor Imagery EEG Data Classification—0
Common Spatial Generative Adversarial Networks based EEG Data Augmentation for Cross-Subject Brain-Computer Interface—0
A self-paced BCI system with low latency for motor imagery onset detection based on time series prediction paradigm—0
Cloud-based Deep Learning of Big EEG Data for Epileptic Seizure Prediction—0
ART: Artifact Removal Transformer for Reconstructing Noise-Free Multichannel Electroencephalographic Signals—0
Classifying Single-Trial EEG during Motor Imagery with a Small Training Set—0
ArEEG_Words: Dataset for Envisioned Speech Recognition using EEG for Arabic Words—0
A Hybrid Brain-Computer Interface Using Motor Imagery and SSVEP Based on Convolutional Neural Network—0
A Consumer BCI for Automated Music Evaluation Within a Popular On-Demand Music Streaming Service - Taking Listener's Brainwaves to Extremes—0
Classification of Visual Perception and Imagery based EEG Signals Using Convolutional Neural Networks—0
Classification of Upper Arm Movements from EEG signals using Machine Learning with ICA Analysis—0
ArEEG_Chars: Dataset for Envisioned Speech Recognition using EEG for Arabic Characters—0
Are Brain-Computer Interfaces Feasible with Integrated Photonic Chips?—0
Classification of fNIRS Data Under Uncertainty: A Bayesian Neural Network Approach—0
Classification of Emerging Neural Activity from Planning to Grasp Execution using a Novel EEG-Based BCI Platform—0
An overview of deep learning techniques for epileptic seizures detection and prediction based on neuroimaging modalities: Methods, challenges, and future works—0
Classification of Electroencephalograms during Mathematical Calculations Using Deep Learning—0
Classification of EEG Signal based on non-Gaussian Neutral Vector—0
Application of Common Spatial Patterns in Gravitational Waves Detection—0
Agreement Rate Initialized Maximum Likelihood Estimator for Ensemble Classifier Aggregation and Its Application in Brain-Computer Interface—0
A Computationally Efficient Multiclass Time-Frequency Common Spatial Pattern Analysis on EEG Motor Imagery—0
Classification of EEG Motor Imagery Using Deep Learning for Brain-Computer Interface Systems—0
Classification of Distraction Levels Using Hybrid Deep Neural Networks From EEG Signals—0
A Novel Semi-supervised Meta Learning Method for Subject-transfer Brain-computer Interface—0
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