https://github.com/msuimpress/hardmax
공식 코드Learning Binary Color Filter Arrays with Trainable Hard Thresholding
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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.
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Learning Binary Color Filter Arrays with Trainable Hard Thresholding
Evaluation Of P300 Speller Performance Using Large Language Models Along With Cross-Subject Training
Automated Pipeline for EEG Artifact Reduction (APPEAR) Recorded during fMRI
A Deep Neural Network for SSVEP-based Brain-Computer Interfaces
Transfer Learning of an Ensemble of DNNs for SSVEP BCI Spellers without User-Specific Training
Source-Free Domain Adaptation for SSVEP-based Brain-Computer Interfaces
Natural scene reconstruction from fMRI signals using generative latent diffusion
Uncertainty Quantification for cross-subject Motor Imagery classification
FBDNN: Filter Banks and Deep Neural Networks for Portable and Fast Brain-Computer Interfaces
FBCNet: A Multi-view Convolutional Neural Network for Brain-Computer Interface
Mining Java Memory Errors using Subjective Interesting Subgroups with Hierarchical Targets
SPD domain-specific batch normalization to crack interpretable unsupervised domain adaptation in EEG
Probabilistic Decomposed Linear Dynamical Systems for Robust Discovery of Latent Neural Dynamics
Differentiable Channel Selection in Self-Attention For Person Re-Identification
T-TIME: Test-Time Information Maximization Ensemble for Plug-and-Play BCIs
Exploring new territory: Calibration-free decoding for c-VEP BCI
Uncertainty Detection and Reduction in Neural Decoding of EEG Signals
End-to-end P300 BCI using Bayesian accumulation of Riemannian probabilities
Toward asynchronous EEG-based BCI: Detecting imagined words segments in continuous EEG signals
An algorithm for onset detection of linguistic segments in continuous electroencephalogram signals
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.