Еще одна сверточная сеть для ИМК с замахом на универсальность:
"Making full use of deep learning technology to design a robust algorithm that is capable of analyzing EEG across BCI paradigms is our main work in this paper. ... Inspired by InceptionV4 and InceptionTime architecture, we introduce a neural network ensemble named InceptionEEG-Net (IENet), where multi-scale convolutional layer and convolution of length 1 enable model to extract rich high-dimensional features with limited parameters. In addition, we propose the average receptive field gain for convolutional neural networks (CNNs), which optimizes IENet to detect long patterns at a smaller cost."
Du Y, Liu J. IENet: a robust convolutional neural network for EEG based brain-computer interfaces. Journal of Neural Engineering. 2022 May 23. (Open access) https://doi.org/10.1088/1741-2552/ac7257
"Making full use of deep learning technology to design a robust algorithm that is capable of analyzing EEG across BCI paradigms is our main work in this paper. ... Inspired by InceptionV4 and InceptionTime architecture, we introduce a neural network ensemble named InceptionEEG-Net (IENet), where multi-scale convolutional layer and convolution of length 1 enable model to extract rich high-dimensional features with limited parameters. In addition, we propose the average receptive field gain for convolutional neural networks (CNNs), which optimizes IENet to detect long patterns at a smaller cost."
Du Y, Liu J. IENet: a robust convolutional neural network for EEG based brain-computer interfaces. Journal of Neural Engineering. 2022 May 23. (Open access) https://doi.org/10.1088/1741-2552/ac7257