https://github.com/gparisa/eegbciMI
공식 코드Comparison of Classification Algorithms Towards Subject-Specific and Subject-Independent BCI
Papers with Code 아카이브
Brain Decoding
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Motor Brain Decoding is fundamental task for building motor brain computer interfaces (BCI).
Progress in predicting finger movements based on brain activity allows us to restore motor functions and improve rehabilitation process of patients.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/guangyizhangbci/eeg_riemannian
공식 코드Spatio-Temporal EEG Representation Learning on Riemannian Manifold and Euclidean Space
https://github.com/guido151/Conditional-EEG-Diffusion
공식 코드Synthesizing EEG Signals from Event-Related Potential Paradigms with Conditional Diffusion Models
https://github.com/guoruilu/slimseiz
공식 코드SlimSeiz: Efficient Channel-Adaptive Seizure Prediction Using a Mamba-Enhanced Network
https://github.com/gzoumpourlis/ensemble-mi
공식 코드Motor Imagery Decoding Using Ensemble Curriculum Learning and Collaborative Training
https://github.com/hackerbci/eeg-with-jetsonnano
공식 코드Device JNEEG to convert Jetson Nano to brain-Computer interfaces. Short report
https://github.com/hans/nn-decoding
공식 코드Does the brain represent words? An evaluation of brain decoding studies of language understanding
https://github.com/hehe91/LA
공식 코드Different Set Domain Adaptation for Brain-Computer Interfaces: A Label Alignment Approach
https://github.com/huzeyann/MemoryEncodingModel
공식 코드https://github.com/ibm/eeg-ieeg-brain-compressor
공식 코드https://github.com/iis-eth-zurich/eeg-tcnet
공식 코드https://github.com/inria-nerv/happyfeat
공식 코드HappyFeat -- An interactive and efficient BCI framework for clinical applications
https://github.com/iobt-vistec/min2net
공식 코드MIN2Net: End-to-End Multi-Task Learning for Subject-Independent Motor Imagery EEG Classification
https://github.com/irwinherrmann/stochastic-gates
공식 코드https://github.com/islab-sophia/2s-odis
공식 코드2S-ODIS: Two-Stage Omni-Directional Image Synthesis by Geometric Distortion Correction
https://github.com/ispamm/mhyeeg
공식 코드https://github.com/itsnotacie/sepprune
공식 코드SepPrune: Structured Pruning for Efficient Deep Speech Separation
https://github.com/jaewon-lee-b/ltew
공식 코드Learning Local Implicit Fourier Representation for Image Warping
https://github.com/jcwang123/fedlc
공식 코드Personalizing Federated Medical Image Segmentation via Local Calibration
https://github.com/jesus-333/Dynamic-PyTorch-Net
공식 코드https://github.com/jesus-333/FBCSP-Python
공식 코드https://github.com/joezhao527/dm4neuro
공식 코드https://github.com/joshparksj/eeg-wgan
공식 코드Improving EEG Signal Classification Accuracy Using Wasserstein Generative Adversarial Networks
https://github.com/julianagonzalezastudillo/netfeat
공식 코드Feature interpretability in BCIs: exploring the role of network lateralization
https://github.com/kesslerr/m4d
공식 코드https://github.com/kkipngenokoech/Hybrid-EEGNET-CharRNN-predictor
공식 코드Improving SSVEP BCI Spellers With Data Augmentation and Language Models
https://github.com/kolcs/GoPar
공식 코드https://github.com/kolcs/bionic_apps
공식 코드https://github.com/krishnatarun/ssl_dgc
공식 코드https://github.com/kunzhan/brainguard
공식 코드BrainGuard: Privacy-Preserving Multisubject Image Reconstructions from Brain Activities
https://github.com/lbinmeng/adversarial_filter
공식 코드Adversarial Filtering Based Evasion and Backdoor Attacks to EEG-Based Brain-Computer Interfaces
https://github.com/limuhit/pseudocylindrical_convolution
공식 코드Pseudocylindrical Convolutions for Learned Omnidirectional Image Compression
https://github.com/lina-usc/jansen-rit-model-benchmarking-deep-learning
공식 코드Deep Jansen-Rit Parameter Inference for Model-Driven Analysis of Brain Activity
https://github.com/littlepure2333/mindbridge
공식 코드https://github.com/lixujin99/tsformer-sa
공식 코드A Temporal-Spectral Fusion Transformer with Subject-Specific Adapter for Enhancing RSVP-BCI Decoding
https://github.com/lizixing23/AMBOKD
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.