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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 51–100 of 466 papers

TitleStatusHype
CNN-based Approaches For Cross-Subject Classification in Motor Imagery: From The State-of-The-Art to DynamicNetCode1
LGGNet: Learning from Local-Global-Graph Representations for Brain-Computer InterfaceCode1
A magnetoencephalography dataset for motor and cognitive imagery-based brain-computer interfaceCode1
FBCNet: A Multi-view Convolutional Neural Network for Brain-Computer InterfaceCode1
BENDR: using transformers and a contrastive self-supervised learning task to learn from massive amounts of EEG dataCode1
EEG-Inception: An Accurate and Robust End-to-End Neural Network for EEG-based Motor Imagery ClassificationCode1
A Deep Neural Network for SSVEP-based Brain-Computer InterfacesCode1
The Discriminative Kalman Filter for Bayesian Filtering with Nonlinear and Nongaussian Observation ModelsCode1
Tiny noise, big mistakes: Adversarial perturbations induce errors in Brain-Computer Interface spellersCode1
Different Set Domain Adaptation for Brain-Computer Interfaces: A Label Alignment ApproachCode1
The 2025 PNPL Competition: Speech Detection and Phoneme Classification in the LibriBrain Dataset—0
Dataset combining EEG, eye-tracking, and high-speed video for ocular activity analysis across BCI paradigmsCode0
Decoding Saccadic Eye Movements from Brain Signals Using an Endovascular Neural Interface—0
Sub-Scalp EEG for Sensorimotor Brain-Computer Interface—0
Minimally Invasive Brain Computer Interfaces: Evaluating the Impact of Tissue Layers on Signal Quality of Sub-Scalp EEG—0
EEG Foundation Models for BCI Learn Diverse Features of Electrophysiology—0
From Theory to Application: Fine-Tuning Large EEG Model with Real-World Stress Data—0
Non-invasive two-step strategy BCI: brain-muscle-hand interface—0
AbsoluteNet: A Deep Learning Neural Network to Classify Cerebral Hemodynamic Responses of Auditory Processing—0
BrainStratify: Coarse-to-Fine Disentanglement of Intracranial Neural Dynamics—0
The Study of Human Preference Based on Integrated Analysis of N1 and LPP Components—0
QSVM-QNN: Quantum Support Vector Machine Based Quantum Neural Network Learning Algorithm for Brain-Computer Interfacing Systems—0
Unlocking Non-Invasive Brain-to-Text—0
Covariance Density Neural Networks—0
Real-Time Brain-Computer Interface Control of Walking Exoskeleton with Bilateral Sensory Feedback—0
Pretraining Large Brain Language Model for Active BCI: Silent Speech—0
Sub-Scalp Brain-Computer Interface Device Design and Fabrication—0
Siamese Network with Dual Attention for EEG-Driven Social Learning: Bridging the Human-Robot Gap in Long-Tail Autonomous Driving—0
Riemannian Geometry for the classification of brain states with intracortical brain-computer interfaces—0
Optimized Feature Selection and Neural Network-Based Classification of Motor Imagery Using EEG Signals—0
EEG2GAIT: A Hierarchical Graph Convolutional Network for EEG-based Gait Decoding—0
Edge-Fog Computing-Enabled EEG Data Compression via Asymmetrical Variational Discrete Cosine Transform Network—0
On Questions of Predictability and Control of an Intelligent System Using Probabilistic State-Transitions—0
Integrating Biological and Machine Intelligence: Attention Mechanisms in Brain-Computer Interfaces—0
Motor Imagery EEG Signals: Multi-Task Classification and Subject Identification with a Lightweight CNN—0
Minima Possible Weights: A Homogenous Deep Ensemble Method for Cross-Subject Motor Imagery ClassificationCode0
SSVEP-BiMA: Bifocal Masking Attention Leveraging Native and Symmetric-Antisymmetric Components for Robust SSVEP Decoding—0
CSSSTN: A Class-sensitive Subject-to-subject Semantic Style Transfer Network for EEG Classification in RSVP TasksCode0
ISAM-MTL: Cross-subject multi-task learning model with identifiable spikes and associative memory networks—0
Interpretable Dual-Filter Fuzzy Neural Networks for Affective Brain-Computer Interfaces—0
Easing Seasickness through Attention Redirection with a Mindfulness-Based Brain--Computer Interface—0
Towards Probabilistic Inference of Human Motor Intentions by Assistive Mobile Robots Controlled via a Brain-Computer Interface—0
Integrating Language-Image Prior into EEG Decoding for Cross-Task Zero-Calibration RSVP-BCI—0
Human-AI Teaming Using Large Language Models: Boosting Brain-Computer Interfacing (BCI) and Brain Research—0
Improving SSVEP BCI Spellers With Data Augmentation and Language ModelsCode0
Low count of optically pumped magnetometers furnishes a reliable real-time access to sensorimotor rhythm—0
Canine EEG Helps Human: Cross-Species and Cross-Modality Epileptic Seizure Detection via Multi-Space Alignment—0
A3E: Aligned and Augmented Adversarial Ensemble for Accurate, Robust and Privacy-Preserving EEG Decoding—0
EEG-GMACN: Interpretable EEG Graph Mutual Attention Convolutional Network—0
Imagined Speech State Classification for Robust Brain-Computer Interface—0
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