Landscape Perception Identification and Classification Based on Electroencephalogram (EEG) Features
Abstract
:1. Introduction
2. Materials and Methods
2.1. Materials
2.2. Subjects
2.3. Method
2.4. Statistical Analysis
3. Results
3.1. The Classification Effect of Different Classifiers
3.2. The Classification Effect of Different EEG Features
3.3. The Classification Effect of Different Brainwave Bands
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Feature | Delta | Theta | Alpha | Beta | Gamma | Total |
---|---|---|---|---|---|---|
MAS | 29 | 29 | 29 | 29 | 29 | 145 |
PSD | 29 | 29 | 29 | 29 | 29 | 145 |
DE | 29 | 29 | 29 | 29 | 29 | 145 |
DASM | 13 | 13 | 13 | 13 | 13 | 65 |
RASM | 13 | 13 | 13 | 13 | 13 | 65 |
DCAU | 11 | 11 | 11 | 11 | 11 | 55 |
Feature | Classifier | Delta (%) | Theta (%) | Alpha (%) | Beta (%) | Gamma (%) | Total (%) |
---|---|---|---|---|---|---|---|
MAS | BP | 20.38 ± 5.74 | 19.08 ± 5.82 | 22.67 ± 7.18 | 51.45 ± 11.49 | 69.2 ± 11.15 | 48.69 ± 10.25 |
KNN | 40.07 ± 11.85 | 33.39 ± 11.2 | 44.79 ± 15.1 | 83.91 ± 11.98 | 95.28 ± 4.17 | 69.54 ± 18.81 | |
RF | 47.13 ± 10.54 | 40.27 ± 12.56 | 52.63 ± 14.69 | 90.33 ± 6.77 | 96.36 ± 3.32 | 96.51 ± 3.33 | |
SVM | 53.02 ± 11.51 | 45.96 ± 12.74 | 62.62 ± 15.82 | 94.62 ± 4.95 | 97.63 ± 2.82 | 96.13 ± 4.38 | |
PSD | BP | 19.08 ± 5.41 | 21.09 ± 6.09 | 24.55 ± 8.11 | 52.01 ± 12.16 | 69.36 ± 11.39 | 47.12 ± 12.28 |
KNN | 40.21 ± 10.63 | 42.1 ± 12.61 | 48.63 ± 17.1 | 81.28 ± 14.66 | 94.13 ± 5.48 | 60.47 ± 18.52 | |
RF | 48.02 ± 11.85 | 51.07 ± 14.58 | 57.64 ± 16.24 | 88.84 ± 8.12 | 96.72 ± 3.10 | 96.52 ± 3.34 | |
SVM | 49.47 ± 8.95 | 58.23 ± 12.38 | 67.62 ± 14.36 | 92.55 ± 6.63 | 96.90 ± 3.13 | 93.98 ± 6.79 | |
DE | BP | 19.86 ± 5.93 | 21.84 ± 6.79 | 24.14 ± 8.27 | 51.41 ± 12.22 | 69.65 ± 11.51 | 50.71 ± 9.21 |
KNN | 39.36 ± 11.32 | 41.29 ± 15.24 | 49.2 ± 17.41 | 83.13 ± 12.83 | 94.62 ± 4.71 | 86.14 ± 11.32 | |
RF | 48.21 ± 10.84 | 50.71 ± 13.91 | 57.73 ± 15.94 | 89.03 ± 8.67 | 96.51 ± 3.15 | 96.61 ± 3.33 | |
SVM | 55.56 ± 9.72 | 60.17 ± 14.13 | 69.04 ± 14.76 | 94.83 ± 4.7 | 98.24 ± 2.31 | 97.29 ± 3.09 | |
DASM | BP | 18.41 ± 4.83 | 19.39 ± 6.12 | 22.24 ± 7.46 | 38.82 ± 11.99 | 56.13 ± 13.26 | 37.74 ± 10.63 |
KNN | 33.09 ± 8.71 | 33.64 ± 13.03 | 40.80 ± 18.19 | 75.36 ± 14.15 | 89.93 ± 8.53 | 81.32 ± 11.64 | |
RF | 42.57 ± 8.2 | 40.87 ± 12.86 | 47.01 ± 16.3 | 80.59 ± 9.46 | 92.08 ± 5.86 | 93.45 ± 5.52 | |
SVM | 42.44 ± 8.12 | 45.82 ± 12.67 | 55.14 ± 15.99 | 83.88 ± 9.49 | 93.26 ± 6.29 | 92.08 ± 7.04 | |
RASM | BP | 14.83 ± 3.78 | 14.54 ± 3.29 | 14.54 ± 3.87 | 24.93 ± 10.84 | 41.38 ± 14.35 | 26.71 ± 10.20 |
KNN | 24.23 ± 6.90 | 23.78 ± 8.03 | 27.62 ± 10.12 | 70.59 ± 13.21 | 85.46 ± 8.79 | 41.49 ± 13.01 | |
RF | 37.99 ± 8.42 | 34.66 ± 10.43 | 41.42 ± 14.73 | 80.02 ± 11.95 | 91.35 ± 6.59 | 93.49 ± 5.70 | |
SVM | 26.42 ± 6.63 | 20.43 ± 6.72 | 23.19 ± 10.37 | 65.88 ± 14.20 | 96.52 ± 3.35 | 75.27 ± 12.52 | |
DCAU | BP | 14.22 ± 2.72 | 14.88 ± 3.16 | 14.54 ± 2.99 | 24.23 ± 9.06 | 39.96 ± 13.42 | 26.37 ± 10.53 |
KNN | 25.25 ± 9.00 | 25.13 ± 8.56 | 27.39 ± 8.04 | 54.62 ± 16.93 | 80.01 ± 8.11 | 38.36 ± 18.20 | |
RF | 31.81 ± 11.14 | 33.64 ± 11.57 | 38.60 ± 10.90 | 71.76 ± 13.07 | 85.86 ± 9.42 | 86.16 ± 10.63 | |
SVM | 20.71 ± 6.28 | 20.81 ± 7.49 | 19.96 ± 6.81 | 66.71 ± 10.82 | 87.46 ± 7.29 | 79.09 ± 8.97 |
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Wang, Y.; Wang, S.; Xu, M. Landscape Perception Identification and Classification Based on Electroencephalogram (EEG) Features. Int. J. Environ. Res. Public Health 2022, 19, 629. https://doi.org/10.3390/ijerph19020629
Wang Y, Wang S, Xu M. Landscape Perception Identification and Classification Based on Electroencephalogram (EEG) Features. International Journal of Environmental Research and Public Health. 2022; 19(2):629. https://doi.org/10.3390/ijerph19020629
Chicago/Turabian StyleWang, Yuting, Shujian Wang, and Ming Xu. 2022. "Landscape Perception Identification and Classification Based on Electroencephalogram (EEG) Features" International Journal of Environmental Research and Public Health 19, no. 2: 629. https://doi.org/10.3390/ijerph19020629
APA StyleWang, Y., Wang, S., & Xu, M. (2022). Landscape Perception Identification and Classification Based on Electroencephalogram (EEG) Features. International Journal of Environmental Research and Public Health, 19(2), 629. https://doi.org/10.3390/ijerph19020629