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。自注意力模块(Self-attention Module):直接连接到卷积模块之后,用于提取局部时间特征内的...
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...
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.
We propose a compact convolutional Transformer, named 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 mod...
EEG Transformer 2.0. i. Convolutional Transformer for EEG Decoding. ii. Novel visualization - Class Activation Topography. - eeyhsong/EEG-Conformer
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 ...
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...