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Analysis of a Parallel and Distributed BPSO Algorithm for EEG Classification: Impact on Energy, Time and Accuracy

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Bioinformatics and Biomedical Engineering (IWBBIO 2024)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 14848))

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Abstract

Today, the amount of data generated each year is growing exponentially, directly affecting the time required for its analysis. This problem worsens with high-dimensional datasets, such as those used in electroencephalography, so a good feature selection method and techniques that improve algorithms’ efficiency are increasingly relevant. Consequently, computing time and energy consumption are reduced, which could be used to explore more solutions to the problem. However, it is also necessary to adapt the applications to take advantage of the hardware offered by high-performance computing systems. Therefore, in this work, a parallel and distributed binary particle swarm optimization algorithm has been implemented, used as a feature selection method, and applied to two real electroencephalography datasets: the University of Essex dataset and the well-known BCI Competition IV 2a dataset. The proposed method has been analyzed in a multi-node computing cluster, not only in terms of classification accuracy, but also from the energy-time point of view to study its impact depending on different experimental conditions and datasets used.

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References

  1. Akbari, H., Ghofrani, S., Zakalvand, P., Tariq Sadiq, M.: Schizophrenia recognition based on the phase space dynamic of EEG signals and graphical features. Biomed. Sig. Process. Control 69, e102917 (2021). https://doi.org/10.1016/j.bspc.2021.102917

    Article  Google Scholar 

  2. Asensio-Cubero, J., Gan, J.Q., Palaniappan, R.: Multiresolution analysis over simple graphs for brain computer interfaces. J. Neural Eng. 10(4), 21–26 (2013). https://doi.org/10.1088/1741-2560/10/4/046014

    Article  Google Scholar 

  3. Brunner, C., Leeb, R., Müller-Putz, G., Schlögl, A., Pfurtscheller, G.: BCI competition IV 2a (2008). https://www.bbci.de/competition/iv/desc_2a.pdf. Accessed 19 Feb 2024

  4. Choubey, H., Pandey, A.: A combination of statistical parameters for the detection of epilepsy and EEG classification using ANN and KNN classifier. SIViP 15(3), 475–483 (2021). https://doi.org/10.1007/s11760-020-01767-4

    Article  Google Scholar 

  5. Dali, N., Bouamama, S.: GPU-PSO: parallel particle swarm optimization approaches on graphical processing unit for constraint reasoning: case of max-CSPs. Procedia Comput. Sci. 60, 1070–1080 (2015). https://doi.org/10.1016/j.procs.2015.08.152

    Article  Google Scholar 

  6. Escobar, J.J., Ortega, J., Díaz, A.F., González, J., Damas, M.: Time-energy analysis of multi-level parallelism in heterogeneous clusters: the case of EEG classification in BCI tasks. J. Supercomput. 75(7), 3397–3425 (2019). https://doi.org/10.1007/s11227-019-02908-4

    Article  Google Scholar 

  7. Escobar, J.J., Ortega, J., Damas, M., Savran Kızıltepe, R., Gan, J.Q.: Energy-time analysis of convolutional neural networks distributed on heterogeneous clusters for EEG classification. In: Rojas, I., Joya, G., Catala, A. (eds.) IWANN 2019. LNCS, vol. 11507, pp. 895–907. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-20518-8_74

    Chapter  Google Scholar 

  8. Escobar, J.J., Rodríguez, F., Prieto, B., Kimovski, D., Ortiz, A., Damas, M.: A distributed and energy-efficient KNN for EEG classification in heterogeneous clusters with dynamic money-saving policy. Computing 105(11), 2487–2510 (2023). https://doi.org/10.1007/s00607-023-01193-7

    Article  Google Scholar 

  9. Falcon, R., Almeida, M., Nayak, A.: A binary particle swarm optimization approach to fault diagnosis in parallel and distributed systems. In: IEEE Congress on Evolutionary Computation. CEC’2010, pp. 1–8. IEEE, Barcelona, Spain (2010). https://doi.org/10.1109/CEC.2010.5586002

  10. Gómez-Rubio, Á., et al.: Applying parallel and distributed models on bio-inspired algorithms via a clustering method. Mathematics 10(2), 274 (2022). https://doi.org/10.3390/math10020274

    Article  Google Scholar 

  11. Hersche, M., Rellstab, T., Schiavone, P.D., Cavigelli, L., Benini, L., Rahimi, A.: Fast and accurate multiclass inference for MI-BCIs using large multiscale temporal and spectral features. In: 26th European Signal Processing Conference. EUSIPCO’2018, pp. 1690–1694. IEEE, Rome, Italy (2018). https://doi.org/10.23919/EUSIPCO.2018.8553378

  12. Idowu, O.P., Fang, P., Li, G.: Bio-inspired algorithms for optimal feature selection in motor imagery-based brain-computer interface. In: 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society. EMBC’2020, pp. 519–522. IEEE, Montreal, QC, Canada (2020). https://doi.org/10.1109/EMBC44109.2020.9176244

  13. Jafarifarmand, A., Badamchizadeh, M.A., Khanmohammadi, S., Nazari, M.A., Tazehkand, B.M.: A new self-regulated neuro-fuzzy framework for classification of EEG signals in motor imagery BCI. IEEE Trans. Fuzzy Syst. 26(3), 1485–1497 (2018). https://doi.org/10.1109/TFUZZ.2017.2728521

    Article  Google Scholar 

  14. Khan, J., Bhatti, M.H., Khan, U.G., Iqbal, R.: Multiclass EEG motor-imagery classification with sub-band common spatial patterns. J. Wirel. Commun. Netw. 174 (2019). https://doi.org/10.1186/s13638-019-1497-y

  15. Kimovski, D., Ortega, J., Ortiz, A., Baños, R.: Leveraging cooperation for parallel multi-objective feature selection in high-dimensional EEG data. Concurr. Comput. Pract. Exp. 27(18), 5476–5499 (2015). https://doi.org/10.1002/cpe.3594

    Article  Google Scholar 

  16. Malan, N.S., Sharma, S.: Time window and frequency band optimization using regularized neighbourhood component analysis for multi-view motor imagery EEG classification. Biomed. Sig. Process. Control 67, e102550 (2021). https://doi.org/10.1016/j.bspc.2021.102550

  17. Molla, M.K.I., Saha, S.K., Yasmin, S., Islam, M.R., Shin, J.: Trial regeneration with subband signals for motor imagery classification in BCI paradigm. IEEE Access 9, 7632–7642 (2021). https://doi.org/10.1109/ACCESS.2021.3049191

    Article  Google Scholar 

  18. Nakisa, B., Rastgoo, M.N., Tjondronegoro, D., Chandran, V.: Evolutionary computation algorithms for feature selection of EEG-based emotion recognition using mobile sensors. Expert Syst. Appl. 93, 143–155 (2018). https://doi.org/10.1016/j.eswa.2017.09.062

  19. Nash, P.: The Assessment and Management of Psychological Aspects of Reading and Language Impairments, pp. 278–301. Whurr (2006)

    Google Scholar 

  20. Ortega, J., Asensio-Cubero, J., Gan, J.Q., Ortiz, A.: Classification of motor imagery tasks for BCI with multiresolution analysis and multiobjective feature selection. Biomed. Eng. Online 15(1), 149–164 (2016). https://doi.org/10.1186/s12938-016-0178-x

    Article  Google Scholar 

  21. Pennington, B.F.: The genetics of dyslexia. J. Child Psychol. Psychiatry 31(2), 193–201 (1990). https://doi.org/10.1111/j.1469-7610.1990.tb01561.x

    Article  Google Scholar 

  22. Piotrowski, A.P., Napiorkowski, J.J., Piotrowska, A.E.: Population size in particle swarm optimization. Swarm Evol. Comput. 58, 100718 (2020). https://doi.org/10.1016/j.swevo.2020.100718

  23. Sabancı, K., Koklu, M.: The classification of eye state by using kNN and MLP classification models according to the EEG signals. Int. J. Intell. Syst. Applic. Eng. 3(4), 127–130 (2015). https://doi.org/10.18201/ijisae.75836

  24. Savran Kızıltepe, R., Gan, J.Q., Escobar, J.J.: Integration of feature and decision fusion with deep learning architectures for video classification. IEEE Access 12, 19432–19446 (2024). https://doi.org/10.1109/ACCESS.2024.3360929

    Article  Google Scholar 

  25. Savran Kızıltepe, R., et al.: An annotated video dataset for computing video memorability. Data Brief. 39, e107671 (2021). https://doi.org/10.1016/j.dib.2021.107671

  26. Schroder, M., Bogdan, M., Hinterberger, T., Birbaumer, N.: Automated EEG feature selection for brain computer interfaces. In: 1st International IEEE EMBS Conference on Neural Engineering. NER’2003, pp. 626–629. IEEE, Caoru, Italy (2003). https://doi.org/10.1109/CNE.2003.1196906

  27. Venter, G., Sobieszczanski-Sobieski, J.: Parallel particle swarm optimization algorithm accelerated by asynchronous evaluations. J. Aerosp. Comput. Inf. Commun. 3(3), 123–137 (2006). https://doi.org/10.2514/1.17873

    Article  Google Scholar 

  28. Wang, J., Feng, Z., Ren, X., Lu, N., Luo, J., Sun, L.: Feature subset and time segment selection for the classification of EEG data based motor imagery. Biomed. Sig. Process. Control 61, e102026 (2020). https://doi.org/10.1016/j.bspc.2020.102026

    Article  Google Scholar 

  29. Zainuddin, A.Z.A., Mansor, W., Khuan, L.Y., Mahmoodin, Z.: Classification of EEG signal from capable dyslexic and normal children using KNN. Adv. Sci. Lett. 24(2), 1402–1405 (2018). https://doi.org/10.1166/asl.2018.10758

    Article  Google Scholar 

  30. Zhang, R., Zong, Q., Dou, L., Zhao, X.: A novel hybrid deep learning scheme for four-class motor imagery classification. J. Neural Eng. 16(9), e066004 (2019). https://doi.org/10.1088/1741-2552/ab3471

    Article  Google Scholar 

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Acknowledgements

This research is part of the PID2022-137461NB-C32 and PID2020-119478GB-I00 projects, funded by the MICIU/AEI/10.13039/501100011033 and by ESF+ (“NextGenerationEU/PRTR”). Also, from grant PPJIA2023-025, funded by the University of Granada. The work is also part of the program of mobility stays for professors and researchers in foreign higher education and research centers, sponsored by the Spanish Ministry of Universities under grant CAS22/00332.

Author information

Authors and Affiliations

  1. Department of Software Engineering, CITIC, University of Granada, Granada, Spain

    Juan José Escobar & Diego García-Gil

  2. Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), University of Granada, Granada, Spain

    Diego García-Gil

  3. Department of Computer Engineering, Automation, and Robotics, CITIC, University of Granada, Granada, Spain

    Jesús López-Rodríguez & Beatriz Prieto

  4. Department of Computer Science and Artificial Intelligence, CITIC, University of Granada, Granada, Spain

    Roberto Morcillo-Jiménez

  5. Department of Communications Engineering, University of Málaga, Málaga, Spain

    Andrés Ortiz

  6. Institute of Information Technology, University of Klagenfurt, Klagenfurt, Austria

    Dragi Kimovski

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  1. Juan José Escobar
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  3. Diego García-Gil
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Corresponding author

Correspondence to Juan José Escobar .

Editor information

Editors and Affiliations

  1. Department of Computer Architecture, CITIC-UGR - University of Granada, Granada, Granada, Spain

    Ignacio Rojas

  2. Department of Computer Architecture, CITIC-UGR - University of Granada, Granada, Granada, Spain

    Francisco Ortuño

  3. Department of Computer Architecture, CITIC-UGR - University of Granada, Granada, Granada, Spain

    Fernando Rojas

  4. Department of Computer Architecture, CITIC-UGR - University of Granada, Granada, Granada, Spain

    Luis Javier Herrera

  5. Department of Applied Mathematics, University of Granada, Granada, Granada, Spain

    Olga Valenzuela

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Escobar, J.J. et al. (2024). Analysis of a Parallel and Distributed BPSO Algorithm for EEG Classification: Impact on Energy, Time and Accuracy. In: Rojas, I., Ortuño, F., Rojas, F., Herrera, L.J., Valenzuela, O. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2024. Lecture Notes in Computer Science(), vol 14848. Springer, Cham. https://doi.org/10.1007/978-3-031-64629-4_6

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  • DOI: https://doi.org/10.1007/978-3-031-64629-4_6

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  • Print ISBN: 978-3-031-64628-7

  • Online ISBN: 978-3-031-64629-4

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