ExBrainable: An Open-Source GUI for CNN-based EEG Decoding and Model Interpretation
원문 초록
We have developed a graphic user interface (GUI), ExBrainable, dedicated to convolutional neural networks (CNN) model training and visualization in electroencephalography (EEG) decoding. Available functions include model training, evaluation, and parameter visualization in terms of temporal and spatial representations. We demonstrate these functions using a well-studied public dataset of motor-imagery EEG and compare the results with existing knowledge of neuroscience. The primary objective of ExBrainable is to provide a fast, simplified, and user-friendly solution of EEG decoding for investigators across disciplines to leverage cutting-edge methods in brain/neuroscience research.
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AFPM: Alignment-based Frame Patch Modeling for Cross-Dataset EEG Decoding
Physics-inform attention temporal convolutional network for EEG-based motor imagery classification
DBConformer: Dual-Branch Convolutional Transformer for EEG Decoding
Dataset combining EEG, eye-tracking, and high-speed video for ocular activity analysis across BCI paradigms
EEG-CLIP : Learning EEG representations from natural language descriptions
MVCNet: Multi-View Contrastive Network for Motor Imagery Classification
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