A deep learning method that combined CNN and LSTM to classify 4-class motor imagery tasks is proposed in this paper. In this method, CNN is used to extract time domain features of MI tasks, LSTM is used to further extract time domain features that is more abstract.
Jan 23, 2020 · In this paper, we introduce a method, combined the one-dimensional convolutional neural network (1D CNN) with long short-term memory (LSTM) to ...
A method is introduced that combined the one-dimensional convolutional neural network (1D CNN) with long short-term memory (LSTM) to classify MI tasks, ...
A neural network feature fusion algorithm is proposed by combining the convolutional neural network (CNN) and the long-short-term memory network (LSTM).
(Lu et al. 2019 ) put forward a method of combining CNN and long short-term memory (LSTM) framework for MI classification, a four-class task public dataset was ...
In this study, we designed a hybrid neural network consisting of the CNN and LSTM for the classification of four motor imagery tasks in BCI Competition IV ...
Dec 8, 2023 · In this study, we propose LConvNet, a multi-channel EEG classification model that combines CNN for spatial feature extraction and LSTM for ...
We propose a preliminary solution, based on activation function, to improve the robustness of the deep learning models. By using saturating nonlinearities, the ...
Feb 10, 2022 · A transfer learning-based CNN and LSTM hybrid deep learning model to classify motor imagery EEG signals. Comput Biol Med. 2022 Apr:143:105288 ...
Dec 20, 2023 · This paper proposed a model that combined a three-dimensional convolutional neural network (CNN) with a long short-term memory (LSTM) network with attention to ...