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/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/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/devjake/eeg-diffusion-pytorch
공식 코드EEG Synthetic Data Generation Using Probabilistic Diffusion Models
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/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/gparisa/eegbciMI
공식 코드Comparison of Classification Algorithms Towards Subject-Specific and Subject-Independent BCI
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/hehe91/LA
공식 코드Different Set Domain Adaptation for Brain-Computer Interfaces: A Label Alignment Approach
https://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 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.