DWT and CNN based multi-class motor imagery electroencephalographic signal recognition

Ma, Xunguang; Wang, Dashuai; Liu, Danhua; Yang, Jimin

doi:10.1088/1741-2552/ab6f15

Cited by 34 publications

(18 citation statements)

References 47 publications

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“…In the performance analysis of the proposed model, the prediction performance is compared with other algorithms applied by other scholars in related fields from the perspectives of accuracy, precision, recall, and F 1 values (weighted harmonic mean of precision and recall). The comparative algorithms include LSTM [ 29 ], Bi-directional long short-term memory (BiLSTM) [ 30 ], the AlexNet improved by Visual Geometry Group (VGGNet) [ 31 ], AlexNet [ 32 ], and STGCN [ 33 ]. Furthermore, both hardware and software configurations are considered in the specific simulation experiment.…”

Section: Construction and Evaluation Of Rural Tourism Spatial Pattern Based On Multifactor-weighted Neural Network Algorithmmentioning

confidence: 99%

[Retracted] Spatial Pattern Evaluation of Rural Tourism via the Multifactor‐Weighted Neural Network Model in the Big Data Era

Zhu

Feng

2021

Computational Intelligence and Neuroscience

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The exploration of the evaluation effect of rural tourism spatial pattern based on the multifactor-weighted neural network model in the era of big data aims to optimize the spatial layout of rural tourist attractions. There are plenty of problems such as improper site selection, layout dispersion, and market competition disorder of rural tourism caused by insufficient consideration of planning and tourist market. Hence, the multifactor model after simple weighting is combined with the neural network to construct a spatiotemporal convolution neural network model based on multifactor weighting here to solve these problems. Moreover, the simulation experiment is conducted on the spatial pattern of rural tourism in the Ningxia Hui Autonomous Region to verify the evaluation performance of the constructed model. The results show that the prediction accuracy of the model is 97.69%, which is at least 2.13% higher than that of the deep learning algorithm used by other scholars. Through the evaluation and analysis of the spatial pattern of rural tourist attractions, the spatial distribution of scenic spots in Ningxia has strong stability from 2009 to 2019. Meanwhile, the number of scenic spots in the seven plates has increased and the time cost of scenic spot accessibility has changed significantly. Besides, the change rate of the one-hour isochronous cycle reaches 41.67%. This indicates that the neural network model has high prediction accuracy in evaluating the spatial pattern of rural tourist attractions, which can provide experimental reference for the digital development of the spatial pattern of rural tourism.

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Section: Construction and Evaluation Of Rural Tourism Spatial Pattern Based On Multifactor-weighted Neural Network Algorithmmentioning

confidence: 99%

[Retracted] Spatial Pattern Evaluation of Rural Tourism via the Multifactor‐Weighted Neural Network Model in the Big Data Era

Zhu

Feng

2021

Computational Intelligence and Neuroscience

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“…Recently, instead of end-to-end network designs, Ravi et al (2020) proposed a CNN network with complex spectrum features as inputs and compared its performance with FBCCA and TRCA, showing an improved performance in both user-dependent and use-independent training scenarios. Ma et al (2020)…”

Section: Deep Learningmentioning

confidence: 99%

Review of brain encoding and decoding mechanisms for EEG-based brain–computer interface

Jung

et al. 2021

Cogn Neurodyn

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A brain-computer interface (BCI) can connect humans and machines directly and has achieved successful applications in the past few decades. Many new BCI paradigms and algorithms have been developed in recent years. Therefore, it is necessary to review new progress in BCIs. This paper summarizes progress for EEG-based BCIs from the perspective of encoding paradigms and decoding algorithms, which are two key elements of BCI systems. Encoding paradigms are grouped by their underlying neural meachanisms, namely sensory-and motor-related, vision-related, cognition-related and hybrid paradigms. Decoding algorithms are reviewed in four categories, namely decomposition algorithms, Riemannian geometry, deep learning and transfer learning. This review will provide a comprehensive overview of both modern primary paradigms and algorithms, making it helpful for those who are developing BCI systems.

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“…VGGNet has been applied in EEG sleep patterns signals research. The main features of VGGNet include three points: the convolutional layer is followed by the max pooling layer to reduce the dimension, the number of convolution kernels is gradually increasing, and the convolutional layer stacking is used [68] . Considering VGGNets’ simplicity, we selected an improved VGGNet network in this article.…”

Section: Methods and Proceduresmentioning

confidence: 99%

Massage Therapy’s Effectiveness on the Decoding EEG Rhythms of Left/Right Motor Imagery and Motion Execution in Patients With Skeletal Muscle Pain

Fan

et al. 2021

IEEE J. Transl. Eng. Health Med.

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Objective: Most of effectiveness assessments of the widely-used Massage therapy were based on subjective routine clinical assessment tools, such as Visual Analogue Scale (VAS) score. However, few studies demonstrated the impact of massage on the Electroencephalograph (EEG) rhythm decoding of Motor imagery (MI) and motion execution (ME) with trunk left/right bending in patients with skeletal muscle pain. Method: We used the sample entropy (SampEn), permutation entropy (PermuEn), common spatial pattern (CSP) features, support vector machine (SVM) and logic regression (LR) classifiers. We also used the convolutional neural network (CNN) and attention-based bi-directional long short-term memory (BiLSTM) for classification. Results: The averaged SampEn and PermuEn values of alpha rhythm decreased in almost fourteen channels for five statuses (quiet, MI with left/right bending, ME with left/right bending). It indicated that massage alleviates the pain for the patients of skeletal pain. Furthermore, compared with the SVM and LR classifiers, the BiLSTM method achieved a better area under curve (AUC) of 0.89 for the classification of MI with trunk left/right bending before massage. The AUC became smaller after massage than that before massage for the classification of MI with trunk left/right bending using CNN and BiLSTM methods. The Permutation direct indicator (PDI) score showed the significant difference for patients in different statuses (before vs after massage, and MI vs ME). Conclusions: Massage not only affects the quiet status, but also affects the MI and ME. Clinical Impact: Massage therapy may affect a bit on the accuracy of MI with trunk left/right bending and it change the topography of MI and ME with trunk left/right bending for the patients with skeletal muscle pain.

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DWT and CNN based multi-class motor imagery electroencephalographic signal recognition

Cited by 34 publications

References 47 publications

[Retracted] Spatial Pattern Evaluation of Rural Tourism via the Multifactor‐Weighted Neural Network Model in the Big Data Era

[Retracted] Spatial Pattern Evaluation of Rural Tourism via the Multifactor‐Weighted Neural Network Model in the Big Data Era

Review of brain encoding and decoding mechanisms for EEG-based brain–computer interface

Massage Therapy’s Effectiveness on the Decoding EEG Rhythms of Left/Right Motor Imagery and Motion Execution in Patients With Skeletal Muscle Pain

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