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/abbassalami/eeg-itnet
공식 코드EEG-ITNet: An Explainable Inception Temporal Convolutional Network for Motor Imagery Classification
https://github.com/akmeunier/eegchat
공식 코드https://github.com/alexanderbertrandlab/si-gcca
공식 코드Stimulus-Informed Generalized Canonical Correlation Analysis of Stimulus-Following Brain Responses
https://github.com/anp-scp/fast_bci
공식 코드Evaluating Fast Adaptability of Neural Networks for Brain-Computer Interface
https://github.com/anranknight/EEG-Transformer
공식 코드Transformer-based Spatial-Temporal Feature Learning for EEG Decoding
https://github.com/antiingel/ITR-optimisation
공식 코드Direct information transfer rate optimisation for SSVEP-based BCI
https://github.com/bafanas/dim-reduction-with-cnn-lstm
공식 코드https://github.com/bingzant/beats
공식 코드BEATS: An Open-Source, High-Precision, Multi-Channel EEG Acquisition Tool System
https://github.com/bojue-wang/ccm-lrr
공식 코드Filter Pruning For CNN With Enhanced Linear Representation Redundancy
https://github.com/brainml-gt/jgat
공식 코드JGAT: a joint spatio-temporal graph attention model for brain decoding
https://github.com/bsdlab/dareplane
공식 코드https://github.com/bsdlab/func_mining
공식 코드https://github.com/buptanteeg/beats
공식 코드BEATS: An Open-Source, High-Precision, Multi-Channel EEG Acquisition Tool System
https://github.com/calmevtime/DCTNet
공식 코드https://github.com/calmevtime1990/supp
공식 코드https://github.com/carraraig/Phase-SPDNet
공식 코드Geometric Neural Network based on Phase Space for BCI-EEG decoding
https://github.com/carraraig/augmented_covariance_bci
공식 코드Classification of BCI-EEG based on augmented covariance matrix
https://github.com/cecnl/eeg-kd
공식 코드https://github.com/cecnl/ssvep-dan
공식 코드SSVEP-DAN: A Data Alignment Network for SSVEP-based Brain Computer Interfaces
https://github.com/chenxiachan/eegnex
공식 코드Toward reliable signals decoding for electroencephalogram: A benchmark study to EEGNeX
https://github.com/clbonet/Sliced-Wasserstein_on_Symmetric_Positive_Definite_Matrices
공식 코드Sliced-Wasserstein on Symmetric Positive Definite Matrices for M/EEG Signals
https://github.com/clbonet/spdsw
공식 코드Sliced-Wasserstein on Symmetric Positive Definite Matrices for M/EEG Signals
https://github.com/coastalcph/brain2llm
공식 코드Structural Similarities Between Language Models and Neural Response Measurements
https://github.com/devjake/eeg-diffusion-pytorch
공식 코드EEG Synthetic Data Generation Using Probabilistic Diffusion Models
https://github.com/dslaborg/frequency-pretraining
공식 코드Data-Efficient Sleep Staging with Synthetic Time Series Pretraining
https://github.com/duanyiqun/ctnas-eeg
공식 코드Cross Task Neural Architecture Search for EEG Signal Classifications
https://github.com/eeg-augmentation-benchmark/eeg-augmentation-benchmark-2022
공식 코드Data augmentation for learning predictive models on EEG: a systematic comparison
https://github.com/elouayas/eegsimpleconv
공식 코드A Strong and Simple Deep Learning Baseline for BCI MI Decoding
https://github.com/emmanuelkalunga/Online-SSVEP
공식 코드Using Riemannian geometry for SSVEP-based Brain Computer Interface
https://github.com/epflneuroailab/scaling-primate-vvs
공식 코드Scaling Laws for Task-Optimized Models of the Primate Visual Ventral Stream
https://github.com/eunjin93/SICR_BCI
공식 코드Mutual Information-driven Subject-invariant and Class-relevant Deep Representation Learning in BCI
https://github.com/fanghua-yu/osrt
공식 코드OSRT: Omnidirectional Image Super-Resolution with Distortion-aware Transformer
https://github.com/galvinlim/agtcnet
공식 코드AGTCNet: A Graph-Temporal Approach for Principled Motor Imagery EEG Classification
https://github.com/geometricbci/deep-optimal-transport-for-domain-adaptation-on-spd-manifolds
공식 코드Deep Optimal Transport for Domain Adaptation on SPD Manifolds
https://github.com/gongzix/lite-mind
공식 코드Lite-Mind: Towards Efficient and Robust Brain Representation Network
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.