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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 376–400 of 466 papers

TitleStatusHype
Navigating in Virtual Reality using Thought: The Development and Assessment of a Motor Imagery based Brain-Computer Interface—0
Deep Learning Decoding of Mental State in Non-invasive Brain Computer Interface—0
Evaluating a Semi-Autonomous Brain-Computer Interface Based on Conformal Geometric Algebra and Artificial Vision—0
Mutual Information-driven Subject-invariant and Class-relevant Deep Representation Learning in BCICode0
Translating neural signals to text using a Brain-Computer Interface—0
On improving learning capability of ELM and an application to brain-computer interface—0
Protecting Privacy of Users in Brain-Computer Interface Applications—0
Domain Adaptation with Optimal Transport on the Manifold of SPD matrices—0
Noisy Neural Language Modeling for Typing Prediction in BCI Communication—0
Simultaneous induction of SSMVEP and SMR Using a Gaiting video stimulus: a novel hybrid brain-computer interface—0
Adaptive Subspace Sampling for Class Imbalance Processing-Some clarifications, algorithm, and further investigation including applications to Brain Computer Interface—0
A Survey on Deep Learning-based Non-Invasive Brain Signals:Recent Advances and New Frontiers—0
Attention-based Transfer Learning for Brain-computer Interface—0
SPEAK YOUR MIND! Towards Imagined Speech Recognition With Hierarchical Deep Learning—0
On the Vulnerability of CNN Classifiers in EEG-Based BCIs—0
Information Theoretic Feature Transformation Learning for Brain Interfaces—0
An Ensemble Learning Based Classification of Individual Finger Movement from EEG—0
Recurrent Neural Networks for P300-based BCICode0
A GA-based feature selection of the EEG signals by classification evaluation: Application in BCI systems—0
PFML-based Semantic BCI Agent for Game of Go Learning and Prediction—0
Real-Time EEG Classification via Coresets for BCI Applications—0
Exploring Embedding Methods in Binary Hyperdimensional Computing: A Case Study for Motor-Imagery based Brain-Computer InterfacesCode0
An Analysis of the Accuracy of the P300 BCI—0
Use of Neural Signals to Evaluate the Quality of Generative Adversarial Network Performance in Facial Image Generation—0
An amplitudes-perturbation data augmentation method in convolutional neural networks for EEG decoding—0
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