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/luigisigillo/gwit
공식 코드Guess What I Think: Streamlined EEG-to-Image Generation with Latent Diffusion Models
https://github.com/martinwimpff/channel-attention
공식 코드EEG motor imagery decoding: A framework for comparative analysis with channel attention mechanisms
https://github.com/martinwimpff/eeg-continual
공식 코드https://github.com/martinwimpff/eeg-otta
공식 코드Calibration-free online test-time adaptation for electroencephalography motor imagery decoding
https://github.com/matteoferrante/semantic-brain-decoding
공식 코드Semantic Brain Decoding: from fMRI to conceptually similar image reconstruction of visual stimuli
https://github.com/max-phairot-a/mixnet
공식 코드https://github.com/mccorsi/fucone
공식 코드Functional connectivity ensemble method to enhance BCI performance (FUCONE)
https://github.com/mcd4874/neurips_competition
공식 코드https://github.com/medmaxlab/eegprepro
공식 코드The more, the better? Evaluating the role of EEG preprocessing for deep learning applications
https://github.com/merantix/acosp
공식 코드Auto-Compressing Subset Pruning for Semantic Image Segmentation
https://github.com/metrics-lab/sim
공식 코드https://github.com/miaozhengqing/lmda-code
공식 코드https://github.com/miaozhengqing/tsff
공식 코드Time-space-frequency feature Fusion for 3-channel motor imagery classification
https://github.com/michaelmaiii/aigc-brain
공식 코드Brain-Conditional Multimodal Synthesis: A Survey and Taxonomy
https://github.com/milaniusz/speech2brain2speech
공식 코드Towards Decoding Brain Activity During Passive Listening of Speech
https://github.com/mlgig/channelselectionmtsc
공식 코드Scalable Classifier-Agnostic Channel Selection for Multivariate Time Series Classification
https://github.com/mlgig/xai4mtsc_eval_actionability
공식 코드https://github.com/mlsys-seo/FtGAN
공식 코드https://github.com/msuimpress/hardmax
공식 코드Learning Binary Color Filter Arrays with Trainable Hard Thresholding
https://github.com/neuspeech/mad-meg2text
공식 코드https://github.com/neuspeech/neuspeech1
공식 코드https://github.com/nicolaffETHZ/Brain2Word_paper
공식 코드https://github.com/nik-sm/bci-disc-models
공식 코드https://github.com/nithinparthasarthy/P300Speller
공식 코드Evaluation Of P300 Speller Performance Using Large Language Models Along With Cross-Subject Training
https://github.com/obada-alzoubi/appear
공식 코드Automated Pipeline for EEG Artifact Reduction (APPEAR) Recorded during fMRI
https://github.com/okbalefthanded/pylpov
공식 코드https://github.com/okbalefthanded/stimusto
공식 코드https://github.com/osmanberke/Deep-SSVEP-BCI
공식 코드A Deep Neural Network for SSVEP-based Brain-Computer Interfaces
https://github.com/osmanberke/ensemble-of-dnns
공식 코드Transfer Learning of an Ensemble of DNNs for SSVEP BCI Spellers without User-Specific Training
https://github.com/osmanberke/sfda-ssvep-bci
공식 코드Source-Free Domain Adaptation for SSVEP-based Brain-Computer Interfaces
https://github.com/ozcelikfu/brain-diffuser
공식 코드Natural scene reconstruction from fMRI signals using generative latent diffusion
https://github.com/p-manivannan/uq-motor-imagery
공식 코드Uncertainty Quantification for cross-subject Motor Imagery classification
https://github.com/pedrorasb/fbcnn
공식 코드FBDNN: Filter Banks and Deep Neural Networks for Portable and Fast Brain-Computer Interfaces
https://github.com/pieeg-club/PiEEG-16
공식 코드https://github.com/pmh9960/spherediff
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.