https://github.com/vlawhern/arl-eegmodels
공식 코드EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces
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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.
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces
Motor Imagery Classification for Asynchronous EEG-Based Brain-Computer Interfaces
DTP-Net: Learning to Reconstruct EEG signals in Time-Frequency Domain by Multi-scale Feature Reuse
DBConformer: Dual-Branch Convolutional Transformer for EEG Decoding
MVCNet: Multi-View Contrastive Network for Motor Imagery Classification
EEG-DG: A Multi-Source Domain Generalization Framework for Motor Imagery EEG Classification
Entropy-Reinforced Planning with Large Language Models for Drug Discovery
LGGNet: Learning from Local-Global-Graph Representations for Brain-Computer Interface
Decoding Human Attentive States from Spatial-temporal EEG Patches Using Transformers
Distortion-aware Transformer in 360° Salient Object Detection
Short-length SSVEP data extension by a novel generative adversarial networks based framework
Depth Any Camera: Zero-Shot Metric Depth Estimation from Any Camera
Omni-frequency Channel-selection Representations for Unsupervised Anomaly Detection
HCF-Net: Hierarchical Context Fusion Network for Infrared Small Object Detection
Seeing Beyond the Brain: Conditional Diffusion Model with Sparse Masked Modeling for Vision Decoding
EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces
EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces
EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces
EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces
Brain-Computer-Interface controlled robot via RaspberryPi and PiEEG
EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces
EEG-DCNet: A Fast and Accurate MI-EEG Dilated CNN Classification Method
EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces
Comparative evaluation of state-of-the-art algorithms for SSVEP-based BCIs
Comparative evaluation of state-of-the-art algorithms for SSVEP-based BCIs
EEG-ConvTransformer for Single-Trial EEG based Visual Stimuli Classification
LGGNet: Learning from Local-Global-Graph Representations for Brain-Computer Interface
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.