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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 201–225 of 466 papers

TitleStatusHype
Evaluating a Semi-Autonomous Brain-Computer Interface Based on Conformal Geometric Algebra and Artificial Vision—0
Classification and Recognition of Encrypted EEG Data Neural Network—0
A Multi-Context Character Prediction Model for a Brain-Computer Interface—0
Classification of Distraction Levels Using Hybrid Deep Neural Networks From EEG Signals—0
Classification of EEG Motor Imagery Using Deep Learning for Brain-Computer Interface Systems—0
Factorization Approach for Sparse Spatio-Temporal Brain-Computer Interface—0
Fast and accurate decoding of finger movements from ECoG through Riemannian features and modern machine learning techniques—0
Classification of EEG Signal based on non-Gaussian Neutral Vector—0
Deep learning approaches for neural decoding: from CNNs to LSTMs and spikes to fMRI—0
Feature Learning from Incomplete EEG with Denoising Autoencoder—0
Feature Selection via Dynamic Graph-based Attention Block in MI-based EEG Signals—0
Classification of Emerging Neural Activity from Planning to Grasp Execution using a Novel EEG-Based BCI Platform—0
Classification of fNIRS Data Under Uncertainty: A Bayesian Neural Network Approach—0
Deep Feature Mining via Attention-based BiLSTM-GCN for Human Motor Imagery Recognition—0
Automatic Muscle Artifacts Identification and Removal from Single-Channel EEG Using Wavelet Transform with Meta-heuristically Optimized Non-local Means Filter—0
FingerNet: EEG Decoding of A Fine Motor Imagery with Finger-tapping Task Based on A Deep Neural Network—0
From Theory to Application: Fine-Tuning Large EEG Model with Real-World Stress Data—0
DeepBrain: Towards Personalized EEG Interaction through Attentional and Embedded LSTM Learning—0
Fuzzy temporal convolutional neural networks in P300-based Brain-computer interface for smart home interaction—0
Automatic Control of Reactive Brain Computer Interfaces—0
Generating Music and Generative Art from Brain activity—0
Generating Ten BCI Commands Using Four Simple Motor Imageries—0
A multi-agent control framework for co-adaptation in brain-computer interfaces—0
Geometry-aware stationary subspace analysis—0
Active Semi-supervised Transfer Learning (ASTL) for Offline BCI Calibration—0
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