2021
DOI: 10.1088/1741-2552/abecc5
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Combining generative adversarial network and multi-output CNN for motor imagery classification

Abstract: Objective. Motor imagery (MI) classification is an important task in the brain–computer interface (BCI) field. MI data exhibit highly dynamic characteristics and are difficult to obtain. Therefore, the performance of the classification model will be challenged. Recently, convolutional neural networks (CNNs) have been employed for MI classification and have demonstrated favorable performances. However, the traditional CNN model uses an end-to-end output method, and part of the feature information is discarded d… Show more

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Cited by 19 publications
(17 citation statements)
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“…In this paper, the LBP method is adopted to extract texture features, as shown in formula (17) [24]:…”
Section: The Style Transfer Methods Of Art Work Based On the Improved...mentioning
confidence: 99%
“…In this paper, the LBP method is adopted to extract texture features, as shown in formula (17) [24]:…”
Section: The Style Transfer Methods Of Art Work Based On the Improved...mentioning
confidence: 99%
“…Motor Imagery (MI) is the activation of motor-related brain regions because of imagining a specific body part’s movement [ 21 ]. The decoding of the MI EEG signals is considered one of the main pillars of BCI studies.…”
Section: Gans For Eeg Tasksmentioning
confidence: 99%
“…e reason is that people will make a judgment on text matching by comprehensively analyzing global semantic information, contextual semantic information, word similarity, as well as other external knowledge, and so on. On the premise of integrating information of different granularity levels, the text matching model is established by using the input layer, presentation layer, and matching layer, and its frame structure is shown in Figure 6 [17].…”
Section: Constructing the Multigranularity Question-answering Matchin...mentioning
confidence: 99%