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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 351–375 of 466 papers

TitleStatusHype
Non-invasive two-step strategy BCI: brain-muscle-hand interface—0
Offline EEG-Based Driver Drowsiness Estimation Using Enhanced Batch-Mode Active Learning (EBMAL) for Regression—0
On-device Learning of EEGNet-based Network For Wearable Motor Imagery Brain-Computer Interface—0
On improving learning capability of ELM and an application to brain-computer interface—0
Online and Offline Domain Adaptation for Reducing BCI Calibration Effort—0
Online LDA based brain-computer interface system to aid disabled people—0
Online Optimization of Stimulation Speed in an Auditory Brain-Computer Interface under Time Constraints—0
On Questions of Predictability and Control of an Intelligent System Using Probabilistic State-Transitions—0
On The Effects Of Data Normalisation For Domain Adaptation On EEG Data—0
On the Handwriting Tasks' Analysis to Detect Fatigue—0
On the Vulnerability of CNN Classifiers in EEG-Based BCIs—0
Optimized EEG based mood detection with signal processing and deep neural networks for brain-computer interface—0
Optimized Feature Selection and Neural Network-Based Classification of Motor Imagery Using EEG Signals—0
Optimizing Brain-Computer Interface Performance: Advancing EEG Signals Channel Selection through Regularized CSP and SPEA II Multi-Objective Optimization—0
Ownership and Agency of an Independent Supernumerary Hand Induced by an Imitation Brain-Computer Interface—0
Partial Maximum Correntropy Regression for Robust Trajectory Decoding from Noisy Epidural Electrocorticographic Signals—0
Personalized Continual EEG Decoding: Retaining and Transferring Knowledge—0
PFML-based Semantic BCI Agent for Game of Go Learning and Prediction—0
Phase Synchrony Component Self-Organization in Brain Computer Interface—0
Physics-informed and Unsupervised Riemannian Domain Adaptation for Machine Learning on Heterogeneous EEG Datasets—0
Precision Enhancement in Sustained Visual Attention Training Platforms: Offline EEG Signal Analysis for Classifier Fine-Tuning—0
Prediction of Memory Retrieval Performance Using Ear-EEG Signals—0
PreMovNet: Pre-Movement EEG-based Hand Kinematics Estimation for Grasp and Lift task—0
Pretraining Large Brain Language Model for Active BCI: Silent Speech—0
Protecting Multiple Types of Privacy Simultaneously in EEG-based Brain-Computer Interfaces—0
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