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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 226–250 of 466 papers

TitleStatusHype
Subject-independent trajectory prediction using pre-movement EEG during grasp and lift task—0
Transfer Learning of an Ensemble of DNNs for SSVEP BCI Spellers without User-Specific TrainingCode1
Zydeco-Style Spike Sorting Low Power VLSI Architecture for IoT BCI Implants—0
Classification of Electroencephalograms during Mathematical Calculations Using Deep Learning—0
EEG-BBNet: a Hybrid Framework for Brain Biometric using Graph Connectivity—0
An intertwined neural network model for EEG classification in brain-computer interfaces—0
Physics-inform attention temporal convolutional network for EEG-based motor imagery classificationCode2
How does artificial intelligence contribute to iEEG research?—0
Toward reliable signals decoding for electroencephalogram: A benchmark study to EEGNeXCode1
EPOC Emotiv EEG Basics—0
Factorization Approach for Sparse Spatio-Temporal Brain-Computer Interface—0
Interaction-Grounded Learning with Action-inclusive Feedback—0
SPD domain-specific batch normalization to crack interpretable unsupervised domain adaptation in EEGCode1
A Neural-Inspired Architecture for EEG-Based Auditory Attention Detection—0
Classification of EEG Motor Imagery Using Deep Learning for Brain-Computer Interface Systems—0
A Low-complexity Brain-computer Interface for High-complexity Robot Swarm Control—0
Bayesian Networks for Brain-Computer Interfaces: A Survey—0
An Adaptive Contrastive Learning Model for Spike Sorting—0
IFTT-PIN: A PIN-Entry Method Leveraging the Self-Calibration Paradigm—0
Human Emotion Classification based on EEG Signals Using Recurrent Neural Network And KNN—0
PreMovNet: Pre-Movement EEG-based Hand Kinematics Estimation for Grasp and Lift task—0
A Subject-Independent Brain-Computer Interface Framework Based on Supervised Autoencoder—0
Unsupervised Motor Imagery Saliency Detection Based on Self-Attention Mechanism—0
Prototype-based Domain Generalization Framework for Subject-Independent Brain-Computer Interfaces—0
Decoding Neural Correlation of Language-Specific Imagined Speech using EEG Signals—0
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