Quaternion-Based Signal Analysis for Motor Imagery Classification from Electroencephalographic Signals
Abstract
:1. Introduction
2. Preliminaries
2.1. Quaternions
2.2. BCI System
2.3. Description of Classifiers
3. Proposed Method
4. Description of the Experimental Tests
4.1. EEG Signal Acquisition
4.2. QSA Method Implementation
4.3. Classification
5. Results and Discussion
6. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
Abbreviations
EEG | Electroencephalography |
QSA | Quaternion-based signal analysis |
BCI | Brain computer interface |
DT | Decision tree |
KNN | K-nearest neighbor |
SVM | Support vector machine |
FFT | Fast Fourier transform |
PSD | Power spectral density |
DWT | Discrete wavelet transform |
CSP | Common spatial patterns |
QFT | Quaternion Fourier transform |
QPCA | Quaternion principle component analysis |
RBF | Radial basis function |
RT | Recognition rate |
ET | Error rate |
S | Sensivity |
Sp | Specificity |
A | Accuracy |
PP | Positive probability |
NP | Negative probability |
FA | False alarm |
QDA | Quadratic discriminant analysis |
LDA | Linear discriminant analysis |
MSE | Multi-scale entropy |
MMSE | Multivariate multi-scale entropy |
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Operation | Formulae |
---|---|
Addition | |
Multiplication | |
Scalar product | |
Conjugate | |
Norm | |
Inverse |
Algorithm 1 |
---|
|
End for
|
Statistical Features | Equation |
---|---|
Mean | |
Variance | |
Contrast | |
Homogeneity | |
Cluster Shade | = |
Cluster prominence | = |
Block | BCI Channel | |||
---|---|---|---|---|
1 | FC5 | FC6 | P7 | P8 |
2 | FC5 | FC6 | T7 | T8 |
3 | FC6 | FC5 | P7 | P8 |
4 | FC6 | FC5 | T7 | T8 |
5 | F3 | F4 | FC5 | FC6 |
6 | F3 | F4 | FC5 | FC6 |
7 | F4 | F3 | FC5 | FC6 |
8 | F4 | F3 | T7 | T8 |
9 | T7 | T8 | FC5 | FC6 |
10 | T7 | T8 | P7 | P8 |
dt | Classification Accuracy | ||||||||
---|---|---|---|---|---|---|---|---|---|
DT | KNN | SVM | |||||||
MAX | MEAN | MIN | MAX | MEAN | MIN | MAX | MEAN | MIN | |
1 | 0.9572 | 0.8490 | 0.7662 | 0.9440 | 0.8418 | 0.7857 | 0.7815 | 0.7748 | 0.0000 |
2 | 0.9551 | 0.8473 | 0.7633 | 0.9490 | 0.8439 | 0.7852 | 0.7843 | 0.7749 | 0.0000 |
3 | 0.9471 | 0.8476 | 0.7660 | 0.9474 | 0.8429 | 0.7851 | 0.7837 | 0.7754 | 0.7662 |
4 | 0.9507 | 0.8492 | 0.7686 | 0.9487 | 0.8434 | 0.7826 | 0.7828 | 0.7749 | 0.7633 |
5 | 0.9516 | 0.8478 | 0.7628 | 0.9468 | 0.8432 | 0.7836 | 0.7822 | 0.7750 | 0.7660 |
6 | 0.9468 | 0.8463 | 0.7658 | 0.9457 | 0.8412 | 0.7777 | 0.7809 | 0.7743 | 0.7686 |
7 | 0.9534 | 0.8474 | 0.7638 | 0.9484 | 0.8424 | 0.7876 | 0.7836 | 0.7749 | 0.7628 |
8 | 0.9495 | 0.8478 | 0.7760 | 0.9473 | 0.8418 | 0.7837 | 0.7820 | 0.7753 | 0.7658 |
9 | 0.9504 | 0.8471 | 0.7722 | 0.9499 | 0.8427 | 0.7849 | 0.7855 | 0.7748 | 0.7638 |
10 | 0.9568 | 0.8475 | 0.7649 | 0.9490 | 0.8433 | 0.7879 | 0.7828 | 0.7747 | 0.7753 |
Classifier | Signal Blocks | |||||||||
---|---|---|---|---|---|---|---|---|---|---|
1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | |
DT | 0.8644 | 0.8519 | 0.8635 | 0.8514 | 0.8520 | 0.8437 | 0.8516 | 0.8485 | 0.8509 | 0.8531 |
KNN | 0.8651 | 0.8101 | 0.8609 | 0.8418 | 0.8457 | 0.8396 | 0.8451 | 0.8431 | 0.8394 | 0.8490 |
SVM | 0.7781 | 0.7770 | 0.7790 | 0.7799 | 0.7780 | 0.7774 | 0.7784 | 0.7784 | 0.7784 | 0.7775 |
Classifier | Signal Blocks | |||||||||
---|---|---|---|---|---|---|---|---|---|---|
1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | |
DT | 0.8584 | 0.8472 | 0.8575 | 0.8467 | 0.8477 | 0.8398 | 0.8489 | 0.8417 | 0.8463 | 0.8478 |
KNN | 0.8599 | 0.8370 | 0.8553 | 0.8371 | 0.8408 | 0.8357 | 0.8432 | 0.8363 | 0.8357 | 0.8457 |
SVM | 0.7752 | 0.7752 | 0.7760 | 0.7753 | 0.7741 | 0.7741 | 0.7739 | 0.7739 | 0.7754 | 0.7746 |
Classifier | Subjects | |||||||||
---|---|---|---|---|---|---|---|---|---|---|
1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | |
DT | 0.9427 | 0.8425 | 0.8324 | 0.8291 | 0.8662 | 0.8672 | 0.8649 | 0.8098 | 0.7632 | 0.8574 |
KNN | 0.9471 | 0.8313 | 0.7946 | 0.8122 | 0.8534 | 0.8508 | 0.8642 | 0.7875 | 0.7735 | 0.8473 |
SVM | 0.7625 | 0.7702 | 0.7710 | 0.7797 | 0.7811 | 0.7687 | 0.7818 | 0.7788 | 0.7581 | 0.7831 |
Performance Measures | DT | KNN | SVM |
---|---|---|---|
RT | 0.8475 | 0.8362 | 0.7735 |
ET | 0.1525 | 0.1638 | 0.2265 |
S0 | 1 | 1 | 1 |
S1 | 0.6505 | 0.6344 | 0.8775 |
S2 | 0.6701 | 0.6349 | 0.0938 |
Sp0 | 0.6598 | 0.6343 | 0.4948 |
Sp1 | 0.9064 | 0.8964 | 0.7427 |
Sp2 | 0.8972 | 0.8926 | 0.9638 |
A0 | 0.9978 | 0.9979 | 0.9979 |
A1 | 0.6944 | 0.6548 | 0.6317 |
A2 | 0.6663 | 0.5002 | 0.1404 |
FA0 | 0.3402 | 0.3657 | 0.5052 |
FA1 | 0.0936 | 0.1036 | 0.2573 |
FA2 | 0.1028 | 0.1074 | 0.0362 |
PP0 | 4.1122 | 3.5360 | 1.9911 |
PP1 | 8.3456 | 6.6754 | 3.3885 |
PP2 | 8.6331 | 8.4926 | 0.9183 |
NP0 | 0 | 0 | 0 |
NP1 | 0.3830 | 0.4094 | 0.1524 |
NP2 | 0.3647 | 0.4107 | 0.9343 |
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Share and Cite
Batres-Mendoza, P.; Montoro-Sanjose, C.R.; Guerra-Hernandez, E.I.; Almanza-Ojeda, D.L.; Rostro-Gonzalez, H.; Romero-Troncoso, R.J.; Ibarra-Manzano, M.A. Quaternion-Based Signal Analysis for Motor Imagery Classification from Electroencephalographic Signals. Sensors 2016, 16, 336. https://doi.org/10.3390/s16030336
Batres-Mendoza P, Montoro-Sanjose CR, Guerra-Hernandez EI, Almanza-Ojeda DL, Rostro-Gonzalez H, Romero-Troncoso RJ, Ibarra-Manzano MA. Quaternion-Based Signal Analysis for Motor Imagery Classification from Electroencephalographic Signals. Sensors. 2016; 16(3):336. https://doi.org/10.3390/s16030336
Chicago/Turabian StyleBatres-Mendoza, Patricia, Carlos R. Montoro-Sanjose, Erick I. Guerra-Hernandez, Dora L. Almanza-Ojeda, Horacio Rostro-Gonzalez, Rene J. Romero-Troncoso, and Mario A. Ibarra-Manzano. 2016. "Quaternion-Based Signal Analysis for Motor Imagery Classification from Electroencephalographic Signals" Sensors 16, no. 3: 336. https://doi.org/10.3390/s16030336
APA StyleBatres-Mendoza, P., Montoro-Sanjose, C. R., Guerra-Hernandez, E. I., Almanza-Ojeda, D. L., Rostro-Gonzalez, H., Romero-Troncoso, R. J., & Ibarra-Manzano, M. A. (2016). Quaternion-Based Signal Analysis for Motor Imagery Classification from Electroencephalographic Signals. Sensors, 16(3), 336. https://doi.org/10.3390/s16030336