An Improved Composite Multiscale Fuzzy Entropy for Feature Extraction of MI-EEG
Abstract
:1. Introduction
2. Feature Extraction Based on WCMFE
2.1. Preprocessing of MI-EEG Times Series
2.2. Coarse-Graining for WCMFE
2.3. The Calculation of WCMFE
- (1)
- Given the embedding dimension m, the vectors are calculated, where and .
- (2)
- For and , the distance between and is described as
- (3)
- For a given boundary gradient n and boundary width r, is calculated from Equation (3).
- (4)
- Repeat the steps (1)–(3), can be obtained. Then, is defined as
2.4. Construction of Feature Vector
3. Experimental Research
3.1. Data Source
3.2. Interval Selection of MI-EEG
3.3. Comparative Study of WCMFE and CMFE
3.3.1. Selection of Weight Factors
3.3.2. Selection of Scale Factor
3.3.3. Selection of Parameters in FE
3.3.4. Comparison of WCMFE and CMFE
3.3.5. Statistical Analysis
3.4. Comparison with Multiple Traditional Feature Extraction Methods Based on BP Neural Network
3.5. Comparison of Multiple Entropy-Based Feature Extraction Methods
3.6. Comparison with Multiple Traditional Recognition Methods
4. Discussion
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Feature Extraction Method | Classification Method | Top Recognition Rate (%) | Average Recognition Rate with 10 × 10-fold CV (%) |
---|---|---|---|
CMFE | BP | 100.00 | 93.18 |
WCMFE | BP | 100.00 | 93.86 |
Type | Group | Count | Mean | h | p-Value |
---|---|---|---|---|---|
Normal distribution test | Population 1 | 100 | 93.86 | 0 | 0.50 |
Population 2 | 100 | 93.18 | 0 | 0.27 | |
Homogeneity test of variance | Pooled | 200 | 93.52 | - | 0.09 |
Reference Number | Feature Extraction Method | Top Recognition Rate (%) | Average Recognition Rate with 10 × 10-fold CV (%) |
---|---|---|---|
[5] | HHT | 87.14 | - |
[9] | DWT | 92.40 | - |
[12] | WPE | 88.57 | - |
[15] | WT+ICA | 95.30 | - |
This paper | WCMFE | 100.00 | 93.86 |
Reference Number | Feature Extraction Method | Classification Method | Top Classification Rate (%) | Average Classification Rate with 10 × 10-fold CV (%) |
---|---|---|---|---|
[5] | HHT | BP | 87.14 | - |
[6] | EMD | POS+SVM | 87.60 | - |
[7] | EMD | SVM | 99.48 | - |
[7] | EMD+FE | KNN | 99.39 | - |
[8] | MEMD+STFT | KNN | 90.71 | - |
[10] | DWT+AR | LDA | 90.00 | - |
[11] | DWT+FE | SVM | 98.44 | - |
[12] | WPE | BP | 88.57 | - |
[15] | CSP | SVM | 82.86 | - |
[42] | WT | Bayes | 89.29 | - |
[43] | ERD | LDA | 86.43 | - |
[44] | AR | LDA | 84.29 | - |
This paper | WCMFE | BP | 100.00 | 93.86 |
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Li, M.; Wang, R.; Xu, D. An Improved Composite Multiscale Fuzzy Entropy for Feature Extraction of MI-EEG. Entropy 2020, 22, 1356. https://doi.org/10.3390/e22121356
Li M, Wang R, Xu D. An Improved Composite Multiscale Fuzzy Entropy for Feature Extraction of MI-EEG. Entropy. 2020; 22(12):1356. https://doi.org/10.3390/e22121356
Chicago/Turabian StyleLi, Mingai, Ruotu Wang, and Dongqin Xu. 2020. "An Improved Composite Multiscale Fuzzy Entropy for Feature Extraction of MI-EEG" Entropy 22, no. 12: 1356. https://doi.org/10.3390/e22121356
APA StyleLi, M., Wang, R., & Xu, D. (2020). An Improved Composite Multiscale Fuzzy Entropy for Feature Extraction of MI-EEG. Entropy, 22(12), 1356. https://doi.org/10.3390/e22121356