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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 401–450 of 466 papers

TitleStatusHype
ArEEG_Words: Dataset for Envisioned Speech Recognition using EEG for Arabic Words—0
ART: Artifact Removal Transformer for Reconstructing Noise-Free Multichannel Electroencephalographic Signals—0
A self-paced BCI system with low latency for motor imagery onset detection based on time series prediction paradigm—0
A SPA-based Manifold Learning Framework for Motor Imagery EEG Data Classification—0
A Spiking Neural Network based on Neural Manifold for Augmenting Intracortical Brain-Computer Interface Data—0
A Study on Stroke Rehabilitation through Task-Oriented Control of a Haptic Device via Near-Infrared Spectroscopy-Based BCI—0
A Subject-Independent Brain-Computer Interface Framework Based on Supervised Autoencoder—0
A Survey on Brain-Computer Interaction—0
A Survey on Deep Learning-based Non-Invasive Brain Signals:Recent Advances and New Frontiers—0
A Systematic Evaluation of Euclidean Alignment with Deep Learning for EEG Decoding—0
A Technique Based on Chaos for Brain Computer Interfacing—0
Attention-based Transfer Learning for Brain-computer Interface—0
Attention Patterns Detection using Brain Computer Interfaces—0
Automated Classification of L/R Hand Movement EEG Signals using Advanced Feature Extraction and Machine Learning—0
Automatic Control of Reactive Brain Computer Interfaces—0
Automatic Muscle Artifacts Identification and Removal from Single-Channel EEG Using Wavelet Transform with Meta-heuristically Optimized Non-local Means Filter—0
Autoregressive models for biomedical signal processing—0
Bandit Algorithms boost Brain Computer Interfaces for motor-task selection of a brain-controlled button—0
Bayesian Networks for Brain-Computer Interfaces: A Survey—0
Bayesian Nonparametric Models for Synchronous Brain-Computer Interfaces—0
Beware of Overestimated Decoding Performance Arising from Temporal Autocorrelations in Electroencephalogram Signals—0
Bi-Band ECoGNet for ECoG Decoding on Classification Task—0
Binarization Methods for Motor-Imagery Brain-Computer Interface Classification—0
BioGAP: a 10-Core FP-capable Ultra-Low Power IoT Processor, with Medical-Grade AFE and BLE Connectivity for Wearable Biosignal Processing—0
Bootstrapping Adaptive Human-Machine Interfaces with Offline Reinforcement Learning—0
Brain-based control of car infotainment—0
Brain Computer Interface: Deep Learning Approach to Predict Human Emotion Recognition—0
Brain Computer Interface Technology for Future Battlefield—0
Brain-Computer Interface with Corrupted EEG Data: A Tensor Completion Approach—0
Brain informed transfer learning for categorizing construction hazards—0
BrainStratify: Coarse-to-Fine Disentanglement of Intracranial Neural Dynamics—0
Brief Architectural Survey of Biopotential Recording Front-Ends since the 1970s—0
Canonical Polyadic Decomposition with Auxiliary Information for Brain Computer Interface—0
Capsule Attention for Multimodal EEG-EOG Representation Learning with Application to Driver Vigilance Estimation—0
Channel Reflection: Knowledge-Driven Data Augmentation for EEG-Based Brain-Computer Interfaces—0
Chromatic and High-frequency cVEP-based BCI Paradigm—0
Classification and Recognition of Encrypted EEG Data Neural Network—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
Classification of EEG Signal based on non-Gaussian Neutral Vector—0
Classification of Electroencephalograms during Mathematical Calculations Using Deep Learning—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
Classification of Upper Arm Movements from EEG signals using Machine Learning with ICA Analysis—0
Classification of Visual Perception and Imagery based EEG Signals Using Convolutional Neural Networks—0
Classifying Single-Trial EEG during Motor Imagery with a Small Training Set—0
Cloud-based Deep Learning of Big EEG Data for Epileptic Seizure Prediction—0
Common Spatial Generative Adversarial Networks based EEG Data Augmentation for Cross-Subject Brain-Computer Interface—0
Comparison of Sub-Scalp EEG and Endovascular Stent-Electrode Array for Visual Evoked Potential Brain-Computer Interface—0
Confidence-Aware Subject-to-Subject Transfer Learning for Brain-Computer Interface—0
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