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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

Showing 125 of 466 papers

TitleStatusHype
CTNet: A Convolutional Transformer Network for EEG-Based Motor Imagery ClassificationCode3
NeuSpeech: Decode Neural signal as SpeechCode3
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
https://arxiv.org/pdf/2409.07491Code2
PHemoNet: A Multimodal Network for Physiological SignalsCode2
PiEEG-16 to Measure 16 EEG Channels with Raspberry Pi for Brain-Computer Interfaces and EEG devicesCode2
Neuro-GPT: Towards A Foundation Model for EEGCode2
Seeing Beyond the Brain: Conditional Diffusion Model with Sparse Masked Modeling for Vision DecodingCode2
Physics-inform attention temporal convolutional network for EEG-based motor imagery classificationCode2
Raspberry PI Shield - for measure EEG (PIEEG)Code2
Brain-Computer-Interface controlled robot via RaspberryPi and PiEEGCode2
GCNs-Net: A Graph Convolutional Neural Network Approach for Decoding Time-resolved EEG Motor Imagery SignalsCode2
AGTCNet: A Graph-Temporal Approach for Principled Motor Imagery EEG ClassificationCode1
TCANet: A Temporal Convolutional Attention Network for Motor Imagery EEG DecodingCode1
Spatial Distillation based Distribution Alignment (SDDA) for Cross-Headset EEG ClassificationCode1
MVCNet: Multi-View Contrastive Network for Motor Imagery ClassificationCode1
Decoding Human Attentive States from Spatial-temporal EEG Patches Using TransformersCode1
T-TIME: Test-Time Information Maximization Ensemble for Plug-and-Play BCIsCode1
Dareplane: A modular open-source software platform for BCI research with application in closed-loop deep brain stimulationCode1
MAD: Multi-Alignment MEG-to-Text DecodingCode1
Subject-Adaptive Transfer Learning Using Resting State EEG Signals for Cross-Subject EEG Motor Imagery ClassificationCode1
Synthesizing EEG Signals from Event-Related Potential Paradigms with Conditional Diffusion ModelsCode1
Towards gaze-independent c-VEP BCI: A pilot studyCode1
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