Brain Computer Interface
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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.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/MindSpore-scientific/code-2/tree/main/TSFF
Time-space-frequency feature Fusion for 3-channel motor imagery classification
https://github.com/Nikeshbajaj/Notebooks/tree/master/PhyAAt_Notebooks
https://github.com/Nikeshbajaj/phyaat
https://github.com/PSCLab-ASU/Learning-in-the-Frequency-Domain
https://github.com/RuiLiu-cc/FLEEG
Aggregating Intrinsic Information to Enhance BCI Performance through Federated Learning
https://github.com/TakShimoda/CP8322-Deep-Learning-in-Computer-Vision-Course-Project
https://github.com/YundongWang/BCI_Challenge
EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces
https://github.com/adwaykanhere/FYP
EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces
https://github.com/akhilmurali013/project
Comparative evaluation of state-of-the-art algorithms for SSVEP-based BCIs
https://github.com/alexandrebarachant/bci-challenge-ner-2015
https://github.com/alexandrebarachant/pyRiemann
https://github.com/amrzhd/EEGNet
EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces
https://github.com/drwuHUST/TLBCI
Transfer Learning for Motor Imagery Based Brain-Computer Interfaces: A Complete Pipeline
https://github.com/eeyhsong/eeg-transformer
Transformer-based Spatial-Temporal Feature Learning for EEG Decoding
https://github.com/emmanuelkalunga/Offline-Riemannian-SSVEP
Using Riemannian geometry for SSVEP-based Brain Computer Interface
https://github.com/emotionlab/eegain
EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces
https://github.com/esantamariavazquez/EEG-Inception
https://github.com/gasiki/eeg-reptile
S-JEPA: towards seamless cross-dataset transfer through dynamic spatial attention
https://github.com/jinglescode/python-signal-processing
https://github.com/karaposu/Brain_typing
Converting Your Thoughts to Texts: Enabling Brain Typing via Deep Feature Learning of EEG Signals
https://github.com/kusumikakd/EEG_Multiclass
https://github.com/miaozhengqing/weightfreezing
Priming Cross-Session Motor Imagery Classification with A Universal Deep Domain Adaptation Framework
https://github.com/mikewangwzhl/eeg-to-text
https://github.com/nqian1/survey-on-odisr-and-odvsr
https://github.com/okbalefthanded/ssvepformer
A Transformer-based deep neural network model for SSVEP classification
https://github.com/pulp-platform/multispectral-riemannian
https://github.com/pyRiemann/pyRiemann
https://github.com/teptwomey/deep_learning_architectures_for_fscv
Transformer-based Spatial-Temporal Feature Learning for EEG Decoding
https://github.com/xiangzhang1015/Brain_typing
Converting Your Thoughts to Texts: Enabling Brain Typing via Deep Feature Learning of EEG Signals
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.