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In this paper, we propose a deep learning method to use long short-term memory (LSTM) recurrent neural networks for learning representations from ECoG and ...
This study introduces a long short-term memory recurrent neural network to decode the multichannel electroencephalogram or electrocorticogram
Using Long Short-Term Memory Network for. Recognizing Motor Imagery Tasks. Xiaoyan Xu. School of Electrical Engineering and. Automation, Qilu University of.
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Jun 24, 2019 · In this study, we introduce a novel scheme for classifying motor imagery (MI) tasks using electroencephalography (EEG) signal that can be ...
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Methods: This study introduces a long short-term memory recurrent neural network to decode the multichannel electroencephalogram or electrocorticogram for ...
Missing: Recognizing Tasks.
Finally, long short-term memory (LSTM) deep neural networks are used to classify EEG data. The alpha and beta waves are considered in this paper. For pre ...
A neural network feature fusion algorithm is proposed by combining the convolutional neural network (CNN) and the long-short-term memory network (LSTM).
This paper presents a novel deep learning approach designed toward both remarkably accurate and responsive motor imagery (MI) recognition based on scalp EEG.
Methods : This study introduces a long short-term memory recurrent neural network to decode the multichannel electroencephalogram or electrocorticogram for ...
May 9, 2022 · Tortora et al. (2020) used a trained long short term memory deep neural network to decode EEG gait, and the proposed decoding method obtains ...