EEG-Inception网络结构 如图所示,EEG-Inception网络结构上包含了2个Inception模块后接了两个卷积层,最后全连接层进行分类。 第一个Inception模块对信号进行三种时间维度的特征提取,对应三个卷积核C1、C2和C3大小为64 × 1, 32 × 1 和 16 × 1。输入数据的采样率为128Hz,对应每个卷积分别对应了500m、250ms和125...
EEG-Inception网络总体结构 EEG-Inception网络结构 如图所示,EEG-Inception网络结构上包含了6个Inception模块和2个Residual模块,全连接层进行分类,激活函数为ReLU。数据处理上包含了提出的数据增广方法。 Inception模块受到图像分了的Inception网络启发,从深度(序列性)和宽度(并行)两个方面进行特征提取。深度通过增加Inception...
The proposed CNN model, namely EEG-Inception, is built on the backbone of the Inception-Time network, which showed to be highly efficient and accurate for time-series classification. Also, the proposed network is an end-to-end classification, for it takes the raw EEG signals as the input ...
This paper proposes a subject-independent model multi-scale EEG-inception integrated encoder Network (MSEI-ENet) for multi-task MI-EEG decoding. Firstly, the multi-scale EEG-inception (MSEI) module is employed to extract the spectral and spatial features of an MI-EEG. Furthermore, its multiple...
This paper proposes a subject-independent model multi-scale EEG-inception integrated encoder Network (MSEI-ENet) for multi-task MI-EEG decoding. Firstly, the multi-scale EEG-inception (MSEI) module is employed to extract the spectral and spatial features of an MI-EEG. Furthermore, its multiple...
neonatal EEG analysisIn this paper, we introduce a new variation of the Convolutional Neural Network Inception block, called Sinc, for sleep stage classification in premature newborn babies using electroencephalogram (EEG). In practice, there are many medical centres where only a limited number of ...
Thus, this paper proposes to design a domain invariant model for EEG-network based emotion identification.Methods A novel brain-inception-network based deep learning model is proposed to extract discriminative graph features from EEG brain networks. To verify its efficiency, we compared our proposed ...
To the best of our knowledge, Epilepsy-Net is the first EEG signal processing work to detect epileptic seizures by combining the attention mechanism with the Inception deep network algorithm.We validate our Epilepsy-Net through several large public EEG signal datasets. The results of our experiments...
This paper proposes a subject-independent model multi-scale EEG-inception integrated encoder Network (MSEI-ENet) for multi-task MI-EEG decoding. Firstly, the multi-scale EEG-inception (MSEI) module is employed to extract the spectral and spatial features of an MI-EEG. Furthermore, its multiple...
It employs a specially designed multi-scale structure EEG-inception module (MSEI) for comprehensive feature learning. The encoder module further helps to detect discriminative information by its multi-head self-attention layer with a larger receptive field, which enhances feature representation and ...