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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 126–150 of 466 papers

TitleStatusHype
AM-MTEEG: Multi-task EEG classification based on impulsive associative memory—0
Method for Evaluating the Number of Signal Sources and Application to Non-invasive Brain-computer Interface—0
Translating Mental Imaginations into Characters with Codebooks and Dynamics-Enhanced Decoding—0
Enhancing EEG Signal Generation through a Hybrid Approach Integrating Reinforcement Learning and Diffusion Models—0
SEE: Semantically Aligned EEG-to-Text Translation—0
ART: Artifact Removal Transformer for Reconstructing Noise-Free Multichannel Electroencephalographic Signals—0
MixNet: Joining Force of Classical and Modern Approaches Toward the Comprehensive Pipeline in Motor Imagery EEG ClassificationCode0
Probabilistic Decomposed Linear Dynamical Systems for Robust Discovery of Latent Neural DynamicsCode0
Research Advances and New Paradigms for Biology-inspired Spiking Neural Networks—0
On-device Learning of EEGNet-based Network For Wearable Motor Imagery Brain-Computer Interface—0
Emotion-Agent: Unsupervised Deep Reinforcement Learning with Distribution-Prototype Reward for Continuous Emotional EEG Analysis—0
EEG Right & Left Voluntary Hand Movement-based Virtual Brain-Computer Interfacing Keyboard Using Hybrid Deep Learning Approach—0
RISE-iEEG: Robust to Inter-Subject Electrodes Implantation Variability iEEG ClassifierCode0
EEGMobile: Enhancing Speed and Accuracy in EEG-Based Gaze Prediction with Advanced Mobile ArchitecturesCode0
Domain Adaptation-Enhanced Searchlight: Enabling classification of brain states from visual perception to mental imageryCode0
Decoding Linguistic Representations of Human Brain—0
How Does a Single EEG Channel Tell Us About Brain States in Brain-Computer Interfaces ?—0
L-Sort: An Efficient Hardware for Real-time Multi-channel Spike Sorting with Localization—0
ESI-GAL: EEG Source Imaging-based Trajectory Estimation for Grasp and Lift Task—0
GET: A Generative EEG Transformer for Continuous Context-Based Neural Signals—0
Speech Imagery BCI Training Using Game with a PurposeCode0
Beware of Overestimated Decoding Performance Arising from Temporal Autocorrelations in Electroencephalogram Signals—0
High Performance P300 Spellers Using GPT2 Word Prediction With Cross-Subject Training—0
NeuroAssist: Enhancing Cognitive-Computer Synergy with Adaptive AI and Advanced Neural Decoding for Efficient EEG Signal Classification—0
JNEEG shield for Jetson Nano for real-time EEG signal processing with deep learning—0
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