EEG Channel Selection Based User Identification via Improved Flower Pollination Algorithm
<p>Proposed EEG-based user identification system using FPA<math display="inline"><semantics> <mi>β</mi> </semantics></math>-hc.</p> "> Figure 2
<p>Distribution of the electrodes used in the study.</p> "> Figure 3
<p>EEG feature representation.</p> "> Figure 4
<p>EEG channel selection using the proposed approach (FPA<math display="inline"><semantics> <mi>β</mi> </semantics></math>-hc).</p> "> Figure 5
<p>Convergence rate and the frequency of channel selection for FPA<math display="inline"><semantics> <mi>β</mi> </semantics></math>-hc and FPA.</p> "> Figure 6
<p>Performance results of the proposed approach over different feature extraction methods.</p> "> Figure 7
<p>Comparison of the proposed approach with state-of-art methods.</p> ">
Abstract
:1. Introduction
- To evaluate the proposed FPA-hc for EEG-based user identification. Such a hybrid approach aims to improve local pollination in FPA to avoid being stuck in local minima.
- To perform an extensive study to select the most suitable classifier to guide the optimization process using FPA-hc. Our experiments showed that Support Vector Machines with Radial Basis Function (SVM-RBF) obtained the most effective results, thus being the preferred approach in this work.
2. Proposed Method
2.1. EEG Signal Acquisition
2.2. Pre-Processing
2.3. Feature Extraction
2.4. Objective Function
Algorithm 1 Hybridizing Flower Pollination Algorithm with -hill climbing for EEG Channels Selection. |
|
2.5. Experimental Setup
3. Results
3.1. EEG Classification Using Standard Machine Learning Approaches
3.2. Comparison against Standard FPA, -hc, and FPA-hc
3.3. Comparison with State-of-the-Art
4. Discussions
5. Conclusions and Future Works
- The proposed algorithm was tested by splitting EEG datasets into three subgroups, i.e., training, validating, and test sets. This approach may lead to overfitting the results. We recommended trying the FPA-hc-SVM using k-fold-cross-validation approach instead.
- The FPA-hc-SVM technique was tested using features and auto-regressive models only. Future work may recommend testing the proposed method over different features. In addition, we recommend investigating the usage of a multi-objective approach.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
BFPA | Binary Flower Pollination Algorithm |
-hc | Hill Climbing |
EEG | Electroencephalogram |
ECoG-BCI | Electrocardiography Brain-Computer Interface |
FPA | Flower Pollination Algorithm |
RBF | Radial Basis Function |
SVM | Support Vector Machines |
GA | Genetic Algorithm |
GWO | Grey Wolf Optimizer |
MRI | Magnetic Resonance Imaging |
EA | Evolutionary-based Algorithms |
TAs | Trajectory-based Algorithms |
SI | Swarm Intelligence |
References
- Rodrigues, D.; Silva, G.F.; Papa, J.P.; Marana, A.N.; Yang, X.S. EEG-based person identification through binary flower pollination algorithm. Expert Syst. Appl. 2016, 62, 81–90. [Google Scholar] [CrossRef] [Green Version]
- Alyasseri, Z.A.A.; Khader, A.T.; Al-Betar, M.A.; Alomari, O.A. Person identification using EEG channel selection with hybrid flower pollination algorithm. Pattern Recognit. 2020, 105, 107393. [Google Scholar] [CrossRef]
- Al-Qazzaz, N.K.; Alyasseri, Z.A.A.; Abdulkareem, K.H.; Ali, N.S.; Al-Mhiqani, M.N.; Guger, C. EEG feature fusion for motor imagery: A new robust framework towards stroke patients rehabilitation. Comput. Biol. Med. 2021, 137, 104799. [Google Scholar] [CrossRef] [PubMed]
- Alyasseri, Z.A.A.; Khader, A.T.; Al-Betar, M.A. Electroencephalogram signals denoising using various mother wavelet functions: A comparative analysis. In Proceedings of the International Conference on Imaging, Signal Processing and Communication, Penang, Malaysia, 26–28 July 2017; pp. 100–105. [Google Scholar]
- Souza, L.; Oliveira, L.; Pamplona, M.; Papa, J. How Far Did We Get in Face Spoofing Detection? Eng. Appl. Artif. Intell. 2018, 72, 368–381. [Google Scholar] [CrossRef]
- Alyasseri, Z.A.A.; Abasi, A.K.; Al-Betar, M.A.; Makhadmeh, S.N.; Papa, J.P.; Abdullah, S.; Khader, A.T. EEG-Based Person Identification Using Multi-Verse Optimizer as Unsupervised Clustering Techniques. In Evolutionary Data Clustering: Algorithms and Applications; Springer: Singapore, 2021; p. 89. [Google Scholar]
- Marcel, S.; Nixon, M.S.; Li, S.Z. Handbook of Biometric Anti-Spoofing; Springer: London, UK, 2014. [Google Scholar]
- Campisi, P.; La Rocca, D. Brain waves for automatic biometric-based user recognition. IEEE Trans. Inf. Forensics Secur. 2014, 9, 782–800. [Google Scholar] [CrossRef]
- Ramadan, R.A.; Vasilakos, A.V. Brain computer interface: Control signals review. Neurocomputing 2017, 223, 26–44. [Google Scholar] [CrossRef]
- Jayarathne, I.; Cohen, M.; Amarakeerthi, S. BrainID: Development of an EEG-based biometric authentication system. In Proceedings of the 2016 IEEE 7th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON), Vancouver, BC, Canada, 13–15 October 2016; pp. 1–6. [Google Scholar]
- Kaur, B.; Singh, D. Neuro signals: A future biomertic approach towards user identification. In Proceedings of the IEEE 2017 7th International Conference on Cloud Computing, Data Science & Engineering-Confluence, Noida, India, 12–13 January 2017; pp. 112–117. [Google Scholar]
- Alyasseri, Z.A.A.; Khader, A.T.; Al-Betar, M.A.; Yang, X.S.; Mohammed, M.A.; Abdulkareem, K.H.; Kadry, S.; Razzak, I. Multi-objective flower pollination algorithm: A new technique for EEG signal denoising. Neural Comput. Appl. 2022, 1–20. [Google Scholar] [CrossRef]
- Alyasseri, Z.A.A.; Khader, A.T.; Al-Betar, M.A.; Papa, J.P.; Alomari, O.A. EEG Feature Extraction for Person Identification using Wavelet Decomposition and Multi-Objective Flower Pollination Algorithm. IEEE Access 2018, 6, 76007–76024. [Google Scholar] [CrossRef]
- Abdi Alkareem Alyasseri, Z.; Alomari, O.A.; Al-Betar, M.A.; Awadallah, M.A.; Hameed Abdulkareem, K.; Abed Mohammed, M.; Kadry, S.; Rajinikanth, V.; Rho, S. EEG Channel Selection Using Multiobjective Cuckoo Search for Person Identification as Protection System in Healthcare Applications. Comput. Intell. Neurosci. 2022, 2022, 5974634. [Google Scholar] [CrossRef] [PubMed]
- Alyasseri, Z.A.A.; Alomari, O.A.; Makhadmeh, S.N.; Mirjalili, S.; Al-Betar, M.A.; Abdullah, S.; Ali, N.S.; Papa, J.P.; Rodrigues, D.; Abasi, A.K. EEG Channel Selection for Person Identification Using Binary Grey Wolf Optimizer. IEEE Access 2022, 10, 10500–10513. [Google Scholar] [CrossRef]
- Fraschini, M.; Didaci, L.; Marcialis, G.L. EEG-based personal identification: Comparison of different functional connectivity metrics. bioRxiv 2018, 254557. [Google Scholar] [CrossRef] [Green Version]
- Gaur, P.; McCreadie, K.; Pachori, R.B.; Wang, H.; Prasad, G. An automatic subject specific channel selection method for enhancing motor imagery classification in EEG-BCI using correlation. Biomed. Signal Process. Control. 2021, 68, 102574. [Google Scholar] [CrossRef]
- Idowu, O.P.; Adelopo, O.; Ilesanmi, A.E.; Li, X.; Samuel, O.W.; Fang, P.; Li, G. Neuro-evolutionary approach for optimal selection of EEG channels in motor imagery based BCI application. Biomed. Signal Process. Control. 2021, 68, 102621. [Google Scholar] [CrossRef]
- Alyasseri, Z.A.A.; Khader, A.T.; Al-Betar, M.A.; Papa, J.P.; Alomari, O.A.; Makhadmeh, S.N. Classification of EEG mental tasks using Multi-Objective Flower Pollination Algorithm for Person Identification. Int. J. Integr. Eng. 2018, 10. Available online: https://publisher.uthm.edu.my/ojs/index.php/ijie/article/view/3478 (accessed on 21 January 2022). [CrossRef]
- Alyasseri, Z.A.A.; Khader, A.T.; Al-Betar, M.A.; Papa, J.P.; Osama, A.A.; Makhadme, S.N. An efficient optimization technique of EEG decomposition for user authentication system. In Proceedings of the 2nd International Conference on BioSignal Analysis, Processing and Systems (ICBAPS), Kuching, Malaysia, 24–26 July 2018; pp. 25–31. [Google Scholar]
- Alyasseri, Z.A.A.; Khader, A.T.; Al-Betar, M.A.; Papa, J.P.; ahmad Alomari, O. EEG-based person authentication using multi-objective flower pollination algorithm. In Proceedings of the 2018 IEEE Congress on Evolutionary Computation (CEC), Rio de Janeiro, Brazil, 8–13 July 2018; pp. 1–8. [Google Scholar]
- Yang, X.S. Flower pollination algorithm for global optimization. In International Conference on Unconventional Computing and Natural Computation; Springer: Berlin/Heidelberg, Germany, 2012; pp. 240–249. [Google Scholar]
- Alyasseri, Z.A.A.; Al-Betar, M.A.; Awadallah, M.A.; Makhadmeh, S.N.; Abasi, A.K.; Doush, I.A.; Alomari, O.A. A Hybrid Flower Pollination with β-Hill Climbing Algorithm for Global Optimization. J. King Saud Univ.-Comput. Inf. Sci. 2021, in press. [Google Scholar] [CrossRef]
- Al-Betar, M.A. β-Hill climbing: An exploratory local search. Neural Comput. Appl. 2017, 28, 153–168. [Google Scholar] [CrossRef]
- Schalk, G.; McFarland, D.J.; Hinterberger, T.; Birbaumer, N.; Wolpaw, J.R. BCI2000: A general-purpose brain-computer interface (BCI) system. IEEE Trans. Biomed. Eng. 2004, 51, 1034–1043. [Google Scholar] [CrossRef]
- Albasri, A.; Abdali-Mohammadi, F.; Fathi, A. EEG electrode selection for person identification thru a genetic-algorithm method. J. Med. Syst. 2019, 43, 1–12. [Google Scholar] [CrossRef] [PubMed]
- Karácsony, T.; Hansen, J.P.; Iversen, H.K.; Puthusserypady, S. Brain computer interface for neuro-rehabilitation with deep learning classification and virtual reality feedback. In Proceedings of the 10th Augmented Human International Conference 2019, Reims, France, 11–12 March 2019; pp. 1–8. [Google Scholar]
- Sun, Y.; Lo, F.P.W.; Lo, B. EEG-based user identification system using 1D-convolutional long short-term memory neural networks. Expert Syst. Appl. 2019, 125, 259–267. [Google Scholar] [CrossRef]
Work | Approach | Case Study | Channels | Selected | Accuracy |
---|---|---|---|---|---|
Rodrigues et al. [1] | Binary Flower Pollination with OPF | Person Identification | 64 | 45 | 86% |
Fraschini et al. [16] | Different connectivity metrics | Person Identification | 64 | N/a | N/a |
Gaur et al. [17] | Person correlation coefficient | Motor Imagery | 118 | 36.58 | 78.08% |
Kaur et al. [11] | Principal Component Analysis | Person Identification | 64 | 64 | 97.73% |
Idowu et al. [18] | Modified Particle Swarm Optimization | Motor Imagery | 64 | 30.4 | 91.89% |
Jayarathne et al. [10] | Common Spatial Patterns | Person Identification | 14 | 14 | 96.97% |
Algorithm | Parameters |
---|---|
FPA | p = 0.8, = 64, D = 20, and = 100 |
-hc | = 0.5, = 64, D = 1, and = 100 |
Classifier | Parameters |
---|---|
LDA | Preset = Linear, covariance structure = Full |
LinearSVM | C = 1.00 × 10, = 0.01, Kernel = Linear, Standardize data: True |
KNN | Kernel = Fine, Distance weight: Equal, Distance: Euclidean, Standardize data: True |
ANN | Hidden layer = 32, Learning Rate = 0.3, binary splits = True |
Naivebayes | C = 0.691, = 0.95, binary splits = True |
J48 | confidence factor = 0.25, binary splits = False, seed = 1 |
OPF | – – |
RBF-SVM | C = 1.00 × 10, = 0.01, Kernel = RBF |
Dataset | Measure | FPA-hc-SVM-RBF | FPA-hc-LSVM | FPA-hc-LDA | FPA-hc-ANN | FPA-hc-NB | -hc-OPF | FPA-hc-J48 | FPA-hc-KNN |
---|---|---|---|---|---|---|---|---|---|
Acc | 94.5619 | 52.66 | 90.13 | 35.46 | 85.20 | 79.73 | 80.13 | 83.06 | |
No. Ch | 34 | 40 | 40 | 43 | 39 | 39 | 43 | 43 | |
AR | Sen | 0.9476 | 0.5266 | 0.9013 | 0.3546 | 0.852 | 79.73 | 0.8013 | 0.8306 |
Spe | 0.9943 | 0.5704 | 0.8776 | 0.2827 | 0.8772 | 81.55 | 0.8351 | 0.8719 | |
F-Score | 0.9473 | 0.5038 | 0.8790 | 0.2765 | 0.8469 | 78.61 | 0.7880 | 0.8223 | |
Acc | 97.9619 | 50.66 | 93.46 | 20.40 | 76.66 | 78.53 | 69.73 | 80.13 | |
No. Ch | 36 | 39 | 36 | 39 | 37 | 37 | 42 | 45 | |
AR | Sen | 0.9796 | 0.5066 | 0.9346 | 0.2040 | 0.7666 | 0.7853 | 69.73 | 80.13 |
Spe | 0.9943 | 0.5152 | 0.9430 | 0.1744 | 0.8199 | 0.8281 | 0.7068 | 83.83 | |
F-Score | 0.983 | 0.4661 | 0.9304 | 0.1494 | 0.7574 | 0.7809 | 0.6620 | 79.12 | |
Acc | 100 | 50.66 | 85.6 | 20.40 | 83.40 | 78.66 | 76.00 | 81.46 | |
No. Ch | 35 | 36 | 36 | 39 | 39 | 41 | 42 | 47 | |
AR | Sen | 1 | 0.5066 | 0.856 | 0.2040 | 0.8346 | 0.7866 | 0.7600 | 0.8146 |
Spe | 1 | 0.5152 | 0.8822 | 0.1744 | 0.8605 | 0.8214 | 0.8218 | 0.8511 | |
F-Score | 1 | 0.4661 | 0.8505 | 0.1494 | 0.8263 | 0.7775 | 0.7610 | 0.8110 |
FPA-hc-LSVM | FPA-hc-LDA | FPA-hc-ANN | FPA-hc-NB | -hc-OPF | FPA-hc-J48 | FPA-hc-KNN | ||
---|---|---|---|---|---|---|---|---|
AR | Mean | 0.5266 | 0.9013 | 0.3546 | 0.852 | 0.8306 | 0.8013 | 0.8306 |
STD | 0.0452 | 0.0520 | 0.0652 | 0.0232 | 0.0672 | 0.0774 | 0.0672 | |
t-value | 44.00 | 3.9338 | 43.6743 | 17.6180 | 8.1506 | 8.9398 | 8.1506 | |
p-value | 0.00001 | 0.000134 | 0.00001 | 0.00001 | 0.00001 | 0.00001 | 0.00001 | |
AR | Mean | 0.5066 | 0.9346 | 0.204 | 0.7666 | 0.7853 | 0.6973 | 0.8013 |
STD | 0.0421 | 0.0485 | 0.0483 | 0.0249 | 0.0566 | 0.0711 | 0.0599 | |
t-value | 41 | 2.09 | 62.64 | 22.46 | 12.95 | 16.32 | 11.27 | |
p-value | 0.00001 | 0.020736 | 0.00001 | 0.00001 | 0.00001 | 0.00001 | 0.00001 | |
AR | Mean | 0.5066 | 0.8560 | 0.2040 | 0.8346 | 0.7866 | 0.7600 | 0.8146 |
STD | 0.0421 | 0.0579 | 0.0483 | 0.0436 | 0.0461 | 0.0498 | 0.0389 | |
t-value | 57.32 | 12.18 | 80.62 | 18.53 | 22.62 | 23.56 | 23.3 | |
p-value | 0.00001 | 0.00001 | 0.00001 | 0.00001 | 0.00001 | 0.00001 | 0.00001 |
Dataset | Measure | FPA-SVM-RBF | FPA-hc-RBF-SVM | -hc-RBFSVM |
---|---|---|---|---|
Acc | 93.3523 | 94.5619 | 93.2 | |
No. Ch | 37 | 34 | 31 | |
AR | Sen | 0.9395 | 0.9476 | 0.928 |
Spe | 0.9935 | 0.9943 | 0.9963 | |
F-Score | 0.93 | 0.9473 | 0.929 | |
Acc | 97 | 97.9619 | 94.2667 | |
No. Ch | 40 | 36 | 30 | |
AR | Sen | 0.9795 | 0.9796 | 0.9422 |
Spe | 0.9935 | 0.9943 | 0.9936 | |
F-Score | 0.97 | 0.983 | 0.9412 | |
Acc | 99.523 | 100 | 89.6 | |
No. Ch | 38 | 35 | 33 | |
AR | Sen | 0.995 | 1 | 0.8899 |
Spe | 0.9935 | 1 | 0.9884 | |
F-Score | 0.995 | 1 | 0.8811 | |
EEGAcc | 78.1714 | 79.48 | 77.2 | |
No. Ch | 33 | 39 | 33 | |
WT | Sen | 0.7817 | 0.7949 | 0.772 |
Spe | 0.9757 | 0.9772 | 0.9747 | |
F-Score | 0.7727 | 0.7854 | 0.7632 |
Dataset | p-Value | w-Value | z-Value | T-Sig | FPAhc |
---|---|---|---|---|---|
AR | 0.05 | 0 | −8.329 | 0.00058 | ++ |
AR | 0.05 | 72.5 | −0.1894 | 0.008493 | ++ |
AR | 0.05 | 12.5 | −2.3062 | 0.002088 | ++ |
WT | 0.05 | 0 | −0.14 | 0.00334 | ++ |
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Alyasseri, Z.A.A.; Alomari, O.A.; Papa, J.P.; Al-Betar, M.A.; Abdulkareem, K.H.; Mohammed, M.A.; Kadry, S.; Thinnukool, O.; Khuwuthyakorn, P. EEG Channel Selection Based User Identification via Improved Flower Pollination Algorithm. Sensors 2022, 22, 2092. https://doi.org/10.3390/s22062092
Alyasseri ZAA, Alomari OA, Papa JP, Al-Betar MA, Abdulkareem KH, Mohammed MA, Kadry S, Thinnukool O, Khuwuthyakorn P. EEG Channel Selection Based User Identification via Improved Flower Pollination Algorithm. Sensors. 2022; 22(6):2092. https://doi.org/10.3390/s22062092
Chicago/Turabian StyleAlyasseri, Zaid Abdi Alkareem, Osama Ahmad Alomari, João P. Papa, Mohammed Azmi Al-Betar, Karrar Hameed Abdulkareem, Mazin Abed Mohammed, Seifedine Kadry, Orawit Thinnukool, and Pattaraporn Khuwuthyakorn. 2022. "EEG Channel Selection Based User Identification via Improved Flower Pollination Algorithm" Sensors 22, no. 6: 2092. https://doi.org/10.3390/s22062092