https://github.com/eeyhsong/EEG-Conformergithub.com/eeyhsong/EEG-Conformer EEG Conformer: Convolutional Transformer for EEG Decoding and Visualization是清华大学研究人员发表在IEEE Transactions on Neural Systems and Rehabilitation Engineering的一项工作,作者提出了 EEG Conformer(Convolutional Transformer) 模...
https://github.com/eeyhsong/EEG-Conformer 方法 EEG-Conformer结构框架图 提出的EEG Conformer模型结构由三个主要部分组成:卷积模块(Convolution Module):以二维的单试次EEG作为输入,通过一维的时间和空间卷积层分别学习EEG在时间和空间维度上的低层次局部特征。而后使用平均池化层降低噪声干扰提高泛化性能。自注意力模...
EEG-Conformer EEG Conformer: Convolutional Transformer for EEG Decoding and Visualization [Paper] Core idea: spatial-temporal conv + pooling + self-attention Abstract We propose a compact convolutional Transformer, named EEG Conformer, to encapsulate local and global features in a unified EEG classificat...
EEG-Conformer EEG Conformer: Convolutional Transformer for EEG Decoding and Visualization [Paper] Core idea: spatial-temporal conv + pooling + self-attention Abstract We propose a compact convolutional Transformer, named EEG Conformer, to encapsulate local and global features in a unified EEG classificat...
EEG Conformer for NMED-T/NMED-H. Contribute to TJ-ACLAB/EEG-Conformer development by creating an account on GitHub.
EEG Conformer for NMED-T/NMED-H. Contribute to TJ-ACLAB/EEG-Conformer development by creating an account on GitHub.
Thank you so much for making your code open source and sharing it with the community! Your contribution has not only saved us a great deal of time and effort in our research, but it has also provided valuable support and reference for our experiments. The spirit of open-source greatly ...
We propose a compact convolutional Transformer, EEG Conformer, to encapsulate local and global features in a unified EEG classification framework. The convolution module learns the low-level local features throughout the one-dimensional temporal and spatial convolution layers. The self-attention module is...
Electroencephalography (EEG) is a non-invasive, cost-effective alternative to advanced neuroimaging for detecting early neural changes. While most studies focus on resting-state EEG or handcrafted features with traditional machine learning, deep learning (DL) offers a promising tool for automated EEG...