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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 101–125 of 466 papers

TitleStatusHype
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
Brain informed transfer learning for categorizing construction hazards—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
An Ensemble Learning Based Classification of Individual Finger Movement from EEG—0
Are Brain-Computer Interfaces Feasible with Integrated Photonic Chips?—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
A Dynamic Domain Adaptation Deep Learning Network for EEG-based Motor Imagery Classification—0
Brain-Computer Interface with Corrupted EEG Data: A Tensor Completion Approach—0
Cloud-based Deep Learning of Big EEG Data for Epileptic Seizure Prediction—0
An embedding for EEG signals learned using a triplet loss—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
Comparison of Sub-Scalp EEG and Endovascular Stent-Electrode Array for Visual Evoked Potential Brain-Computer Interface—0
A SPA-based Manifold Learning Framework for Motor Imagery EEG Data Classification—0
Brain Computer Interface Technology for Future Battlefield—0
Constrained Variational Autoencoder for improving EEG based Speech Recognition Systems—0
A Study on Stroke Rehabilitation through Task-Oriented Control of a Haptic Device via Near-Infrared Spectroscopy-Based BCI—0
Covariate Shift Estimation based Adaptive Ensemble Learning for Handling Non-Stationarity in Motor Imagery related EEG-based Brain-Computer Interface—0
CropCat: Data Augmentation for Smoothing the Feature Distribution of EEG Signals—0
Cross-Correlation Based Discriminant Criterion for Channel Selection in Motor Imagery BCI Systems—0
Brain Computer Interface: Deep Learning Approach to Predict Human Emotion Recognition—0
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