https://bitbucket.org/lvareka/cnnforgtn
공식 코드Evaluation of convolutional neural networks using a large multi-subject P300 dataset
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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.
Evaluation of convolutional neural networks using a large multi-subject P300 dataset
ExBrainable: An Open-Source GUI for CNN-based EEG Decoding and Model Interpretation
Spatial Distillation based Distribution Alignment (SDDA) for Cross-Headset EEG Classification
Multiple Time Series Fusion Based on LSTM An Application to CAP A Phase Classification Using EEG
Tensor-CSPNet: A Novel Geometric Deep Learning Framework for Motor Imagery Classification
Quantifying Spatial Domain Explanations in BCI using Earth Mover's Distance
Multi-Channel Attention Selection GANs for Guided Image-to-Image Translation
Early Abandoning and Pruning for Elastic Distances including Dynamic Time Warping
Calibration-Free Driver Drowsiness Classification based on Manifold-Level Augmentation
Viewport-Unaware Blind Omnidirectional Image Quality Assessment: A Flexible and Effective Paradigm
RISE-iEEG: Robust to Inter-Subject Electrodes Implantation Variability iEEG Classifier
Early Abandoning and Pruning for Elastic Distances including Dynamic Time Warping
The largest EEG-based BCI reproducibility study for open science: the MOABB benchmark
Towards Optimal Filter Pruning with Balanced Performance and Pruning Speed
Interpretable Summaries of Black Box Incident Triaging with Subgroup Discovery
Discrimination-aware Channel Pruning for Deep Neural Networks
Towards Real-World BCI: CCSPNet, A Compact Subject-Independent Motor Imagery Framework
Federated Motor Imagery Classification for Privacy-Preserving Brain-Computer Interfaces
Classification of Motor Imagery EEG Signals by Using a Divergence Based Convolutional Neural Network
Joint Group Feature Selection and Discriminative Filter Learning for Robust Visual Object Tracking
EEG-ITNet: An Explainable Inception Temporal Convolutional Network for Motor Imagery Classification
Stimulus-Informed Generalized Canonical Correlation Analysis of Stimulus-Following Brain Responses
Evaluating Fast Adaptability of Neural Networks for Brain-Computer Interface
Transformer-based Spatial-Temporal Feature Learning for EEG Decoding
Direct information transfer rate optimisation for SSVEP-based BCI
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.