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Use of Multi-agent System to Classify Control EEG Signals: A Preliminary Study

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Agents and Multi-Agent Systems: Technologies and Applications 2022

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 306))

  • 220 Accesses

Abstract

  The potential use of EEG data along with a Multi-Agent System can offer great benefits to the medical and technological area, and this conjunction is used to provide electronic device control through a BCI that can contribute to elderly people or with motor disabilities as well. This work-in-progress paper focuses primarily on the feature discrimination of an EEG dataset that follows the motor-imaginary paradigm, by applying classification techniques and comparing the accuracy between them will allow us to select the best technique to identify between four different classes, finally those trained datasets will serve as supervised learning data, as reference for real-time EEG signal acquisition, and to program commands.

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References

  1. Discapacidad, I.: Estadísticas a propósito del día internacional de las personas con discapacidad (Datos Nacionales). Comunicado de Prensa Num. 713/21. (pp. 1–5). 3 de diciembre (2021)

    Google Scholar 

  2. Sun, L., Feng, Z.R.: Classification of imagery motor EEG data with wavelet denoising and features selection. In: 2016 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR), pp. 184–188 (2016). https://doi.org/10.1109/ICWAPR.2016.7731641

  3. Kim, H. S, Chang, M.H., Lee, H.J., Park, K.S.: A comparison of classification performance among the various combinations of motor imagery tasks for brain-computer interface. In: 2013 6th International IEEE/EMBS Conference on Neural Engineering (NER), pp. 435–438. IEEE (2013). https://doi.org/10.1109/NER.2013.6695965

  4. Pinheiro, O.R., Alves, L.R., Romero, M.F.M., de Souza, J.R.: Wheelchair simulator game for training people with severe disabilities. In: 2016 1st International Conference on Technology and Innovation in Sports, Health and Wellbeing (TISHW), pp. 1–8. IEEE (2016). https://doi.org/10.1109/TISHW.2016.7847792

  5. Rodríguez Bermúdez, G., García Laencina, P.J., Brizion, D., Roca Dorda, J.: Adquisición, procesamiento y clasificación de señales EEG para diseño de sistemas BCI basados en imaginación de movimiento (2013). https://doi.org/10317/3295

  6. Isa, N.M., Amir, A., Ilyas, M.Z., Razalli, M.S.: Motor imagery classification in Brain computer interface (BCI) based on EEG signal by using machine learning technique. Bull. Electr. Eng. Inform. 8(1), 269–275. (2019). https://doi.org/10.11591/eei.v8i1.1402

  7. Angrisani, L., Arpaia, P., Casinelli, D.: Instrumentation and measurements for non-invasive EEG-based brain-computer interface. In: 2017 IEEE International Workshop on Measurement and Networking (MN), pp. 1–5. IEEE (2017). https://doi.org/10.1109/IWMN.2017.8078383

  8. Wang, Y., Wang, R., Gao, X., Hong, B., Gao, S.: A practical VEP-based brain-computer interface. IEEE Trans. Neural Syst. Rehabil. Eng. 234–240 (2006). https://doi.org/10.1109/TNSRE.2006.875576

  9. Melnik, A., Legkov, P., Izdebski, K., Körcher, S.M., Hairston, W.D., Ferris, D.P., König, P.: Systems, subjects, sessions: to what extent do these factors influence EEG data? Front. Hum. Neurosci. 11, 150 (2017)

    Article  Google Scholar 

  10. Goldberger, A., Amaral, L., Glass, L., Hausdorff, J., Ivanov, P.C., Mark, R., Stanley, H.E.: PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation 101(23), e215–e220 (2000). https://doi.org/10.13026/C28G6P

  11. 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. 1034–1043 (2004). https://doi.org/10.1109/tbme.2004.827072

  12. Iversen J.R., Makeig S.: MEG/EEG data analysis using EEGLAB. Magnetoencephalography: From Signals to Dynamic Cortical Networks, pp. 391–406. Springer, Berlin (2014). https://doi.org/10.1007/978-3-642-33045-2_8

  13. MacIntyre, T.E., Madan, C.R., Moran, A.P., Collet, C., Guillot, A.: Motor imagery, performance and motor rehabilitation. Prog. Brain Res. 141–159 (2018). https://doi.org/10.1016/bs.pbr.2018.09.010

  14. Georgoulas, G., Georgopoulos, V.C., Stylios, C.D.: Speech sound classification and detection of articulation disorders with support vector machines and wavelets. In: 2006 International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 2199–2202. IEEE (2006). https://doi.org/10.1109/IEMBS.2006.259499

  15. Subasi, A., Gursoy, M.I.: EEG signal classification using PCA, ICA, LDA and support vector machines. Expert. Syst. Appl. 8659–8666 (2010). https://doi.org/10.1016/j.eswa.2010.06.065

  16. Medina Salgado, B., Alvarez López, R.: Caracterización de Señales EEG mediante Wavelet Packet y Entropía Difusa para tareas de imaginación motora. Ingeniería, pp. 226–238 (2017). https://doi.org/10.14483/udistrital.jour.reving.2017.2.a04

  17. Chaisaen, R., et al.: Decoding EEG rhythms during action observation, motor imagery, and execution for standing and sitting. IEEE Sens. J. 20(22), 13776–13786 (2020). https://doi.org/10.1109/JSEN.2020.3005968

  18. Guler, I., Ubeyli, E.D.: Multiclass support vector machines for EEG-signals classification. IEEE Trans. Inf. Technol. Biomed. 11, 117–126 (2007). https://doi.org/10.1109/TITB.2006.879600

  19. Sharmila, A., Geethanjali, P.: DWT based detection of epileptic seizure from EEG signals using Naive Bayes and k-NN classifiers. In: IEEE Access, vol. 4, pp. 7716–7727 (2016). https://doi.org/10.1109/ACCESS.2016.2585661

  20. Murugappan, M., Ramachandran, N., Sazali, Y.: Classification of human emotion from EEG using discrete wavelet transform. J. Biomed. Sci. Eng. 390 (2010). https://doi.org/10.4236/jbise.2010.34054

  21. Jahankhani, P., Kodogiannis, V., Revett, K.: EEG signal classification using wavelet feature extraction and neural networks. In: IEEE John Vincent Atanasoff 2006 International Symposium on Modern Computing, pp. 120–124 (2006). https://doi.org/10.1109/JVA.2006.17

  22. 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. Appl. Eng. 3(4), 127–130 (2015). https://doi.org/10.18201/ijisae.75836

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Acknowledgements

To CONACYT, for their support during the study of the master’s degree. To PhD. Rosario Baltazar and the research committee for their contribution to this preliminary study.

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Authors and Affiliations

  1. Instituto Tecnológico de León, Av.Tecnológico s/n 37290, León, GTO, Mexico

    Francisco Sierra, Rosario Baltazar, Miguel-Ángel Casillas, Claudia Díaz & Martha-Alicia Rocha

  2. Instituto Tecnológico de Matamoros, Carretera Lauro Villar Kilometro 6.5, Tecnológico, 87490, Heroica Matamoros, Tamps, Mexico

    Anabel Pineda

Authors
  1. Francisco Sierra
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  2. Rosario Baltazar
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  3. Anabel Pineda
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  4. Miguel-Ángel Casillas
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  5. Claudia Díaz
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  6. Martha-Alicia Rocha
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Corresponding author

Correspondence to Rosario Baltazar .

Editor information

Editors and Affiliations

  1. University of Zagreb, Zagreb, Croatia

    Gordan Jezic

  2. The Heriot-Watt University, Scotland, UK

    Yun-Heh Jessica Chen-Burger

  3. University of Zagreb, Zagreb, Croatia

    Mario Kusek

  4. Silesian University in Opava, Opava, Czech Republic

    Roman Šperka

  5. KES International Research, Shoreham-by-sea, UK

    Robert J. Howlett

  6. KES International, Selby, UK

    Lakhmi C. Jain

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© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Sierra, F., Baltazar, R., Pineda, A., Casillas, MÁ., Díaz, C., Rocha, MA. (2022). Use of Multi-agent System to Classify Control EEG Signals: A Preliminary Study. In: Jezic, G., Chen-Burger, YH.J., Kusek, M., Šperka, R., Howlett, R.J., Jain, L.C. (eds) Agents and Multi-Agent Systems: Technologies and Applications 2022. Smart Innovation, Systems and Technologies, vol 306. Springer, Singapore. https://doi.org/10.1007/978-981-19-3359-2_19

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