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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
NeuSpeech: Decode Neural signal as SpeechCode3
CTNet: A Convolutional Transformer Network for EEG-Based Motor Imagery ClassificationCode3
GCNs-Net: A Graph Convolutional Neural Network Approach for Decoding Time-resolved EEG Motor Imagery SignalsCode2
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
Guess What I Think: Streamlined EEG-to-Image Generation with Latent Diffusion ModelsCode2
Neuro-GPT: Towards A Foundation Model for EEGCode2
https://arxiv.org/pdf/2409.07491Code2
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
PHemoNet: A Multimodal Network for Physiological SignalsCode2
PiEEG-16 to Measure 16 EEG Channels with Raspberry Pi for Brain-Computer Interfaces and EEG devicesCode2
Multi-scale convolutional transformer network for motor imagery brain-computer interfaceCode2
Brain-Computer-Interface controlled robot via RaspberryPi and PiEEGCode2
EEG-Inception: An Accurate and Robust End-to-End Neural Network for EEG-based Motor Imagery ClassificationCode1
A 1D CNN for high accuracy classification and transfer learning in motor imagery EEG-based brain-computer interfaceCode1
EEG motor imagery decoding: A framework for comparative analysis with channel attention mechanismsCode1
DTP-Net: Learning to Reconstruct EEG signals in Time-Frequency Domain by Multi-scale Feature ReuseCode1
Device JNEEG to convert Jetson Nano to brain-Computer interfaces. Short reportCode1
EEG-ConvTransformer for Single-Trial EEG based Visual Stimuli ClassificationCode1
EEG Synthetic Data Generation Using Probabilistic Diffusion ModelsCode1
Cross Task Neural Architecture Search for EEG Signal ClassificationsCode1
AGTCNet: A Graph-Temporal Approach for Principled Motor Imagery EEG ClassificationCode1
Deep comparisons of Neural Networks from the EEGNet familyCode1
CNN-based Approaches For Cross-Subject Classification in Motor Imagery: From The State-of-The-Art to DynamicNetCode1
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