SOTAVerified

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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1–50 of 466 papers

TitleStatusHype
NeuSpeech: Decode Neural signal as SpeechCode3
CTNet: A Convolutional Transformer Network for EEG-Based Motor Imagery ClassificationCode3
Multi-scale convolutional transformer network for motor imagery brain-computer interfaceCode2
Neuro-GPT: Towards A Foundation Model for EEGCode2
PiEEG-16 to Measure 16 EEG Channels with Raspberry Pi for Brain-Computer Interfaces and EEG devicesCode2
https://arxiv.org/pdf/2409.07491Code2
Brain-Computer-Interface controlled robot via RaspberryPi and PiEEGCode2
Guess What I Think: Streamlined EEG-to-Image Generation with Latent Diffusion ModelsCode2
Seeing Beyond the Brain: Conditional Diffusion Model with Sparse Masked Modeling for Vision DecodingCode2
PHemoNet: A Multimodal Network for Physiological SignalsCode2
Physics-inform attention temporal convolutional network for EEG-based motor imagery classificationCode2
Raspberry PI Shield - for measure EEG (PIEEG)Code2
GCNs-Net: A Graph Convolutional Neural Network Approach for Decoding Time-resolved EEG Motor Imagery SignalsCode2
PiEEG-16 to Measure 16 EEG Channels with Raspberry Pi for Brain-Computer Interfaces and EEG devicesCode2
Priming Cross-Session Motor Imagery Classification with A Universal Deep Domain Adaptation FrameworkCode1
A 1D CNN for high accuracy classification and transfer learning in motor imagery EEG-based brain-computer interfaceCode1
S-JEPA: towards seamless cross-dataset transfer through dynamic spatial attentionCode1
AGTCNet: A Graph-Temporal Approach for Principled Motor Imagery EEG ClassificationCode1
Low-cost brain computer interface for everyday useCode1
MVCNet: Multi-View Contrastive Network for Motor Imagery ClassificationCode1
Spatial Distillation based Distribution Alignment (SDDA) for Cross-Headset EEG ClassificationCode1
HappyFeat -- An interactive and efficient BCI framework for clinical applicationsCode1
LGGNet: Learning from Local-Global-Graph Representations for Brain-Computer InterfaceCode1
MAD: Multi-Alignment MEG-to-Text DecodingCode1
Functional connectivity ensemble method to enhance BCI performance (FUCONE)Code1
Different Set Domain Adaptation for Brain-Computer Interfaces: A Label Alignment ApproachCode1
EEG motor imagery decoding: A framework for comparative analysis with channel attention mechanismsCode1
A Deep Neural Network for SSVEP-based Brain-Computer InterfacesCode1
DTP-Net: Learning to Reconstruct EEG signals in Time-Frequency Domain by Multi-scale Feature ReuseCode1
EEG Synthetic Data Generation Using Probabilistic Diffusion ModelsCode1
Cross Task Neural Architecture Search for EEG Signal ClassificationsCode1
Natural scene reconstruction from fMRI signals using generative latent diffusionCode1
CNN-based Approaches For Cross-Subject Classification in Motor Imagery: From The State-of-The-Art to DynamicNetCode1
Decoding Human Attentive States from Spatial-temporal EEG Patches Using TransformersCode1
Dareplane: A modular open-source software platform for BCI research with application in closed-loop deep brain stimulationCode1
Closed loop BCI System for Cybathlon 2020Code1
BENDR: using transformers and a contrastive self-supervised learning task to learn from massive amounts of EEG dataCode1
A Transformer-based deep neural network model for SSVEP classificationCode1
A magnetoencephalography dataset for motor and cognitive imagery-based brain-computer interfaceCode1
Device JNEEG to convert Jetson Nano to brain-Computer interfaces. Short reportCode1
EEG-ConvTransformer for Single-Trial EEG based Visual Stimuli ClassificationCode1
EEG-Inception: An Accurate and Robust End-to-End Neural Network for EEG-based Motor Imagery ClassificationCode1
FBCNet: A Multi-view Convolutional Neural Network for Brain-Computer InterfaceCode1
FingerFlex: Inferring Finger Trajectories from ECoG signalsCode1
BEATS: An Open-Source, High-Precision, Multi-Channel EEG Acquisition Tool SystemCode1
Brain-Conditional Multimodal Synthesis: A Survey and TaxonomyCode1
Deep comparisons of Neural Networks from the EEGNet familyCode1
LMDA-Net:A lightweight multi-dimensional attention network for general EEG-based brain-computer interface paradigms and interpretabilityCode1
Improving EEG Signal Classification Accuracy Using Wasserstein Generative Adversarial NetworksCode1
SPD domain-specific batch normalization to crack interpretable unsupervised domain adaptation in EEGCode1
Show:102550
← PrevPage 1 of 10Next →

No leaderboard results yet.