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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 1–25 of 466 papers

TitleStatusHype
CTNet: A Convolutional Transformer Network for EEG-Based Motor Imagery ClassificationCode3
NeuSpeech: Decode Neural signal as SpeechCode3
PHemoNet: A Multimodal Network for Physiological SignalsCode2
Seeing Beyond the Brain: Conditional Diffusion Model with Sparse Masked Modeling for Vision DecodingCode2
PiEEG-16 to Measure 16 EEG Channels with Raspberry Pi for Brain-Computer Interfaces and EEG devicesCode2
GCNs-Net: A Graph Convolutional Neural Network Approach for Decoding Time-resolved EEG Motor Imagery SignalsCode2
Multi-scale convolutional transformer network for motor imagery brain-computer interfaceCode2
Guess What I Think: Streamlined EEG-to-Image Generation with Latent Diffusion ModelsCode2
PiEEG-16 to Measure 16 EEG Channels with Raspberry Pi for Brain-Computer Interfaces and EEG devicesCode2
Raspberry PI Shield - for measure EEG (PIEEG)Code2
Neuro-GPT: Towards A Foundation Model for EEGCode2
Physics-inform attention temporal convolutional network for EEG-based motor imagery classificationCode2
https://arxiv.org/pdf/2409.07491Code2
Brain-Computer-Interface controlled robot via RaspberryPi and PiEEGCode2
Closed loop BCI System for Cybathlon 2020Code1
A 1D CNN for high accuracy classification and transfer learning in motor imagery EEG-based brain-computer interfaceCode1
CNN-based Approaches For Cross-Subject Classification in Motor Imagery: From The State-of-The-Art to DynamicNetCode1
EEG-Inception: An Accurate and Robust End-to-End Neural Network for EEG-based Motor Imagery ClassificationCode1
EEG-ConvTransformer for Single-Trial EEG based Visual Stimuli ClassificationCode1
EEG motor imagery decoding: A framework for comparative analysis with channel attention mechanismsCode1
EEG Synthetic Data Generation Using Probabilistic Diffusion ModelsCode1
BENDR: using transformers and a contrastive self-supervised learning task to learn from massive amounts of EEG dataCode1
Natural scene reconstruction from fMRI signals using generative latent diffusionCode1
DTP-Net: Learning to Reconstruct EEG signals in Time-Frequency Domain by Multi-scale Feature ReuseCode1
Decoding Human Attentive States from Spatial-temporal EEG Patches Using TransformersCode1
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