EEG Channel Selection for Stroke Patient Rehabilitation Using BAT Optimizer
Abstract
:1. Introduction
2. Related Works
3. BAT Algorithm
3.1. Inspiration of BAT
3.2. Procedure of BAT
- Step 1: BAT algorithm parameter initialization. In this step, the BAT parameters are initialized with initial values. These parameters are the number of microbats (solutions) (N), maximum number of iterations (), max () and min () frequency range, velocity vector (v), loudness rate (A), pulse rate (r), initial pulse rate (), two parameters ( and ) with constant values in the range [0, 1], and bandwidth range () in the range [−1, 1].
- Step 2: BAT population memory initialization. The BAT solutions are randomly generated in this step using Equation (1), considering the BAT algorithm and particular optimization problem parameters and constraints.
- Step 3: Bat population intensification. Now, all microbats x fly and change their position considering a velocity v defined by a frequency f that is generated randomly, as shown in Equations (3) and (4). Accordingly, the bat positions are updated using Equation (5).It is notable that the microbats update their locations to be closer to the global best (). Thus, the new position of the microbats intensifies the position in a direction toward .
- Step 4: Bat population diversification.In this step, the microbats’ positions are updated based on random parameters to attempt to find better global solutions. A new solution is selected from the BM based on a selection method and assigned to . Subsequently, the current solution is updated using as follows:
- Step 5: BM update. The position of the microbats in the BM will be updated in this step if the new location is fitter than the old one and . Moreover, will be updated if the new BM contains a solution with a better fitness value. Subsequently, and values will be updated in accordance with Equations (8) and (9).
- Step 6: Stop criterion. Steps 3, 4, and 5 will be repeated until the algorithm reaches the stop criterion.
4. Proposed Method
4.1. Phase I: EEG Signal Acquisition
4.2. Phase II: Preprocessing
4.3. Phase III: EEG Feature Extraction
4.4. Phase IV: EEG Channel Selection Using BAT Optimizer
- Step 1: BAT algorithm parameter initialization. In this step, the BAT parameters are initialized with initial values. These parameters are the number of microbats (solutions) (N), maximum number of iterations (), max () and min () frequency range, velocity vector (v), loudness rate (A), pulse rate (r), initial pulse rate (), two parameters ( and ) with constant values in the range [0, 1], and bandwidth range () in the range [−1, 1], as shown in Table 1.
- Step 2: BAT population memory initialization. The BAT solutions are randomly generated in this step using Equation (11), considering the BAT algorithm and particular optimization problem parameters and constraints.
- Step 3: BAT population intensification. Now, all microbats x fly and change their position considering a velocity v defined by a frequency f that is generated randomly, as shown in Equations (13) and (14). Accordingly, the bat positions are updated using Equation (15).It is notable that the microbats update their locations to be closer to the global best (). Thus, the new position of the microbats intensifies the position in a direction towards .
- Step 4: BAT population diversification.In this step, the microbats’ positions are updated based on random parameters to attempt to find better global solutions. A new solution is selected from the BM based on a selection method and assigned to . Subsequently, the current solution is updated using , as follows:
- Step 5: BM update. The position of the microbats in the BM will be updated in this step if the new location is fitter than the old one and . Moreover, will be updated if the new BM contains a solution with a better fitness value. Subsequently, and values will be updated in accordance with Equations (18) and (19).
- Step 6: stop criterion. Steps 3, 4, and 5 will be repeated until the algorithm reach the stop criterion.
4.5. Phase V: Classification
5. Experiments and Results
5.1. Time Domain Results
5.2. Entropy Domain Results
5.3. Frequency Domain Results
5.4. Statistical Analysis Results
5.5. Results Discussion
6. Conclusions and Future Work
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Algorithm | Parameters | Population Size | Runs |
---|---|---|---|
CS | = 1.5, | 20 | 30 |
PSO | 20 | 30 | |
BAT | A = 0.5, r = 0.5 | 20 | 30 |
FFA | = 0.5, = 1, = 0.2 | 20 | 30 |
GWO | - | 20 | 30 |
Feature/Algorithm | PSO | GWO | FFA | CS | BAT | ||
---|---|---|---|---|---|---|---|
Best | Accuracy | 97.9 | 90.7 | 66.7 | 54.9 | 99 | |
No. channels | 8 | 3 | 1 | 1 | 6 | ||
Worst | Accuracy | 81.5 | 67.4 | 45.1 | 43.2 | 92.8 | |
No. channels | 12 | 6 | 3 | 2 | 13 | ||
Mean | Accuracy | 94.4 | 76.2 | 56.7 | 50.6 | 97.5 | |
No. channels | 9 | 4 | 2 | 2 | 10 | ||
Best | Accuracy | 97.1 | 88.4 | 68.8 | 55.1 | 97.7 | |
No. channels | 5 | 3 | 1 | 1 | 4 | ||
HjAc | Worst | Accuracy | 85.3 | 70.4 | 41.7 | 42.1 | 77.9 |
No. channels | 9 | 5 | 3 | 2 | 14 | ||
Mean | Accuracy | 94.4 | 82.1 | 60.7 | 48.7 | 90.8 | |
No. channels | 8 | 4 | 3 | 2 | 8 | ||
Best | Accuracy | 91.2 | 78.9 | 58.8 | 48.7 | 93.8 | |
No. channels | 7 | 3 | 2 | 1 | 5 | ||
HjComp | Worst | Accuracy | 84.7 | 58.1 | 47.4 | 40.9 | 74.7 |
No. channels | 15 | 6 | 3 | 2 | 13 | ||
Mean | Accuracy | 88.6 | 69.9 | 53.9 | 44.2 | 88.2 | |
No. channels | 9 | 4 | 3 | 1 | 9 | ||
Best | Accuracy | 96.5 | 89.3 | 67.9 | 53.5 | 97.9 | |
No. channels | 6 | 2 | 2 | 1 | 5 | ||
HjMo | Worst | Accuracy | 86.4 | 51.8 | 53.9 | 43.8 | 88.6 |
No. channels | 11 | 6 | 3 | 2 | 12 | ||
Mean | Accuracy | 92.3 | 80.6 | 61.2 | 47.9 | 95.4 | |
No. channels | 9 | 4 | 3 | 1 | 9 | ||
Best | Accuracy | 72.8 | 57.9 | 47.2 | 44.1 | 70.1 | |
No. channels | 4 | 2 | 1 | 1 | 5 | ||
Worst | Accuracy | 56.6 | 44.1 | 41.5 | 41.3 | 53.9 | |
No. channels | 10 | 6 | 3 | 2 | 11 | ||
Mean | Accuracy | 65.2 | 51.7 | 44.7 | 42.6 | 64.9 | |
No. channels | 8 | 4 | 3 | 2 | 9 | ||
Best | Accuracy | 71.2 | 61 | 48.4 | 43.4 | 73.2 | |
No. channels | 7 | 3 | 2 | 1 | 7 | ||
Worst | Accuracy | 56.5 | 48.2 | 43.3 | 40.3 | 63.1 | |
No. channels | 12 | 6 | 3 | 2 | 12 | ||
Mean | Accuracy | 65.6 | 54.9 | 45.4 | 42 | 68.8 | |
No. channels | 9 | 5 | 3 | 2 | 10 | ||
Best | Accuracy | 78.7 | 63.7 | 50 | 44.4 | 79.2 | |
No. channels | 4 | 1 | 2 | 1 | 3 | ||
Worst | Accuracy | 58 | 41 | 43.2 | 40.4 | 48.2 | |
No. channels | 11 | 6 | 3 | 2 | 13 | ||
Mean | Accuracy | 69.6 | 53.3 | 45.9 | 42.3 | 72.5 | |
No. channels | 7 | 4 | 2 | 2 | 9 |
Feature/Algorithm | PSO | GWO | FFA | CS | BAT | ||
---|---|---|---|---|---|---|---|
Best | Accuracy | 96.6 | 82.7 | 61.3 | 52 | 96.5 | |
No. channels | 3 | 3 | 1 | 1 | 7 | ||
Conv | Worst | Accuracy | 76.4 | 64.7 | 45.5 | 42.5 | 87.1 |
No. channels | 11 | 6 | 3 | 2 | 14 | ||
Mean | Accuracy | 90.2 | 73.6 | 54.9 | 45.8 | 93.2 | |
No. channels | 8 | 5 | 2 | 1 | 11 | ||
Best | Accuracy | 93.6 | 81.6 | 60.6 | 49.1 | 95.5 | |
No. channels | 2 | 4 | 2 | 1 | 8 | ||
Worst | Accuracy | 82.2 | 68.7 | 50.5 | 41.8 | 88.9 | |
No. channels | 10 | 6 | 3 | 2 | 12 | ||
Mean | Accuracy | 88.6 | 74.4 | 55.7 | 45.5 | 92.1 | |
No. channels | 7 | 5 | 3 | 1 | 10 | ||
Best | Accuracy | 94.7 | 78.6 | 59.3 | 49.8 | 95.6 | |
No. channels | 5 | 3 | 2 | 1 | 7 | ||
n | Worst | Accuracy | 75.8 | 56.1 | 47.9 | 44.3 | 84.9 |
No. channels | 10 | 6 | 3 | 2 | 12 | ||
Mean | Accuracy | 85.3 | 69.8 | 52.3 | 46.6 | 90.8 | |
No. channels | 8 | 5 | 2 | 2 | 10 | ||
Best | Accuracy | 85.2 | 73.6 | 54.3 | 46.8 | 88.1 | |
No. channels | 7 | 3 | 2 | 1 | 7 | ||
mu | Worst | Accuracy | 70.8 | 55.9 | 45.1 | 40.5 | 75.5 |
No. channels | 12 | 6 | 3 | 2 | 12 | ||
Mean | Accuracy | 80.7 | 66.1 | 49.4 | 44.8 | 84 | |
No. channels | 9 | 5 | 2 | 2 | 9 | ||
Best | Accuracy | 84.3 | 67.7 | 51.9 | 46.6 | 87.2 | |
No. channels | 4 | 2 | 2 | 1 | 8 | ||
mu | Worst | Accuracy | 63.7 | 47 | 44.9 | 41.2 | 75.3 |
No. channels | 12 | 6 | 3 | 2 | 13 | ||
Mean | Accuracy | 77.3 | 56.5 | 47.7 | 43 | 80.3 | |
No. channels | 8 | 4 | 2 | 1 | 10 | ||
Best | Accuracy | 87.9 | 71.5 | 52.9 | 51.9 | 91.4 | |
No. channels | 6 | 3 | 1 | 1 | 7 | ||
Worst | Accuracy | 75.4 | 53.2 | 42.6 | 40.4 | 75.9 | |
No. channels | 10 | 6 | 3 | 3 | 14 | ||
Mean | Accuracy | 81.4 | 63.7 | 48.2 | 44.3 | 83.6 | |
No. channels | 8 | 5 | 2 | 2 | 10 | ||
Best | Accuracy | 83.9 | 72.5 | 52.3 | 46.6 | 84.6 | |
No. channels | 7 | 3 | 1 | 1 | 4 | ||
Worst | Accuracy | 65.9 | 54.4 | 41.2 | 40.2 | 61.3 | |
No. channels | 12 | 7 | 3 | 2 | 13 | ||
Mean | Accuracy | 78.7 | 64.5 | 47.2 | 42.8 | 79.8 | |
No. channels | 10 | 5 | 2 | 1 | 10 |
Feature/Algorithm | PSO | GWO | FFA | CS | BAT | ||
---|---|---|---|---|---|---|---|
Best | Accuracy | 96.7 | 87.3 | 64.1 | 52.5 | 96.3 | |
No. channels | 5 | 3 | 2 | 1 | 7 | ||
Worst | Accuracy | 65.4 | 66 | 50.2 | 41.9 | 90.3 | |
No. channels | 10 | 6 | 3 | 2 | 12 | ||
Mean | Accuracy | 91.2 | 76.4 | 54 | 50 | 93.8 | |
No. channels | 8 | 4 | 2 | 2 | 10 | ||
Best | Accuracy | 82.7 | 69.2 | 53.6 | 46.6 | 83.4 | |
No. channels | 6 | 3 | 2 | 1 | 6 | ||
Worst | Accuracy | 69.5 | 55.5 | 41 | 20 | 68.7 | |
No. channels | 10 | 6 | 3 | 2 | 14 | ||
Mean | Accuracy | 76.3 | 60.6 | 46.1 | 36.5 | 78.4 | |
No. channels | 9 | 4 | 2 | 2 | 10 |
Feature/Algorithm | PSO | GWO | FFA | CS | BAT | ||
---|---|---|---|---|---|---|---|
Accuracy | 97.9 | 90.7 | 66.7 | 54.9 | 99 | ||
Rank | 2 | 3 | 4 | 5 | 1 | ||
HjAc | Accuracy | 97.1 | 88.4 | 68.8 | 55.1 | 97.7 | |
Rank | 2 | 3 | 4 | 5 | 1 | ||
HjComp | Accuracy | 91.2 | 78.9 | 58.8 | 48.7 | 93.8 | |
Rank | 2 | 3 | 4 | 5 | 1 | ||
TDF | HjMo | Accuracy | 96.5 | 89.3 | 67.9 | 53.5 | 97.9 |
Rank | 2 | 3 | 4 | 5 | 1 | ||
Accuracy | 72.8 | 57.9 | 47.2 | 44.1 | 70.1 | ||
Rank | 1 | 3 | 4 | 5 | 2 | ||
Accuracy | 71.2 | 61 | 48.4 | 43.4 | 73.2 | ||
Rank | 2 | 3 | 4 | 5 | 1 | ||
Accuracy | 78.7 | 63.7 | 50 | 44.4 | 79.2 | ||
Rank | 2 | 3 | 4 | 5 | 1 | ||
Summation of Ranks | 13 | 21 | 28 | 35 | 8 | ||
Accuracy | 96.7 | 87.3 | 64.1 | 52.5 | 96.3 | ||
Rank | 1 | 3 | 4 | 5 | 2 | ||
FDF | Accuracy | 82.7 | 69.2 | 53.6 | 46.6 | 83.4 | |
Rank | 2 | 3 | 4 | 5 | 1 | ||
Summation of Ranks | 3 | 6 | 8 | 10 | 3 | ||
Conv | Accuracy | 96.6 | 82.7 | 61.3 | 52 | 96.5 | |
Rank | 1 | 3 | 4 | 5 | 2 | ||
Accuracy | 93.6 | 81.6 | 60.6 | 49.1 | 95.5 | ||
Rank | 2 | 3 | 4 | 5 | 1 | ||
n | Accuracy | 94.7 | 78.6 | 59.3 | 49.8 | 95.6 | |
Rank | 2 | 3 | 4 | 5 | 1 | ||
EDF | mu | Accuracy | 85.2 | 73.6 | 54.3 | 46.8 | 88.1 |
Rank | 2 | 3 | 4 | 5 | 1 | ||
mu | Accuracy | 84.3 | 67.7 | 51.9 | 46.6 | 87.2 | |
Rank | 2 | 3 | 4 | 5 | 1 | ||
Accuracy | 87.9 | 71.5 | 52.9 | 51.9 | 91.4 | ||
Rank | 2 | 3 | 4 | 5 | 1 | ||
Accuracy | 83.9 | 72.5 | 52.3 | 46.6 | 84.6 | ||
Rank | 2 | 3 | 4 | 5 | 1 | ||
Summation of Ranks | 13 | 21 | 28 | 35 | 8 |
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© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
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Al-Betar, M.A.; Alyasseri, Z.A.A.; Al-Qazzaz, N.K.; Makhadmeh, S.N.; Ali, N.S.; Guger, C. EEG Channel Selection for Stroke Patient Rehabilitation Using BAT Optimizer. Algorithms 2024, 17, 346. https://doi.org/10.3390/a17080346
Al-Betar MA, Alyasseri ZAA, Al-Qazzaz NK, Makhadmeh SN, Ali NS, Guger C. EEG Channel Selection for Stroke Patient Rehabilitation Using BAT Optimizer. Algorithms. 2024; 17(8):346. https://doi.org/10.3390/a17080346
Chicago/Turabian StyleAl-Betar, Mohammed Azmi, Zaid Abdi Alkareem Alyasseri, Noor Kamal Al-Qazzaz, Sharif Naser Makhadmeh, Nabeel Salih Ali, and Christoph Guger. 2024. "EEG Channel Selection for Stroke Patient Rehabilitation Using BAT Optimizer" Algorithms 17, no. 8: 346. https://doi.org/10.3390/a17080346