Functional 3D model of the brain from 2D ECG signals

Authors

  • Washington X. Quevedo Inmersoft
  • Gregory Celis

DOI:

https://doi.org/10.61961/injei.v2i1.17

Keywords:

2d visualization, real time signals, emotiv-epoc, cognitive study, 3d brain signals

Abstract

This article shows the investigation of the use of the signals obtained from the ECG, in order to express them in a 3D model of the brain. Starting from obtaining raw data of the values ​​of the waves obtained in real time, and by assigning these values ​​in the order of milliwatts to and from the brain areas where the physical electrodes of the Emotiv Epoc sensor are located, they can be illuminated. and animate the active areas of the patient's brain while he is exposed to an experiment to test his ability to identify objects, solve mazes, and keep his mind at rest. The visualization in the 3D model consists in the first instance of the illumination of the areas where the electrical impulses originate, to culminate with the inferred animation of the origin and destination of the electrical impulses considering the didactic approach in this first stage of the research. This approach aims to be the basis of in-depth research to obtain animations and visualization of brain data in real time and using virtual reality and augmented reality technology for greater immersion in the visualization of non-numerical data in 3D.

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References

Olivares, E. I., Iglesias, J., Saavedra, C., Trujillo-Barreto, N. J., & Valdés-Sosa, M. (2015). Brain signals of face processing as revealed by event-related potentials. Behavioural neurology, 2015.

Ramadan, R. A., & Vasilakos, A. V. (2017). Brain computer interface: control signals review. Neurocomputing, 223, 26-44.

Supriya, Siuly, Wang, H., & Zhang, Y. (2018). An efficient framework for the analysis of big brain signals data. In Databases Theory and Applications: 29th Australasian Database Conference, ADC 2018, Gold Coast, QLD, Australia, May 24-27, 2018, Proceedings 29 (pp. 199-207). Springer International Publishing.

Strmiska, M., Koudelková, Z., & Žabčíková, M. (2018). Measuring brain signals using emotiv devices. WSEAS Transactions on Systems and Control.

Paszkiel, S. (2020). Analysis and classification of EEG signals for brain-computer interfaces (pp. 11-17). Cham: Springer International Publishing.

Elgendi, M., Rebsamen, B., Cichocki, A., Vialatte, F., & Dauwels, J. (2013). Real-time wireless sonification of brain signals. In Advances in Cognitive Neurodynamics (III) Proceedings of the Third International Conference on Cognitive Neurodynamics-2011 (pp. 175-181). Springer Netherlands.

Srimaharaj, W., Chaising, S., Temdee, P., Chaisricharoen, R., & Sittiprapaporn, P. (2018, November). Brain cognitive performance identification for student learning in classroom. In 2018 Global Wireless Summit (GWS) (pp. 102-106). IEEE.

Karimui, R. Y., Azadi, S., & Keshavarzi, P. (2018). The ADHD effect on the actions obtained from the EEG signals. Biocybernetics and Biomedical Engineering, 38(2), 425-437.

Shende, P. M., & Jabade, V. S. (2015, January). Literature review of brain computer interface (BCI) using Electroencephalogram signal. In 2015 International Conference on Pervasive Computing (ICPC) (pp. 1-5). IEEE.

Dimitrov, G. P., Panayotova, G. S., Kovatcheva, E., Borissova, D., & Petrov, P. (2018). One Approach for Identification of Brain Signals for Smart Devices Control. J. Softw., 13(7), 407-413.

Holewa, K., & Nawrocka, A. (2014, May). Emotiv EPOC neuroheadset in brain-computer interface. In Proceedings of the 2014 15th International Carpathian Control Conference (ICCC) (pp. 149-152). IEEE.

Malete, T. N., Moruti, K., Thapelo, T. S., & Jamisola, R. S. (2019, November). Eeg-based control of a 3d game using 14-channel emotiv epoc+. In 2019 IEEE International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM) (pp. 463-468). IEEE.

Ketola, E., Lloyd, C., Shuhart, D., Schmidt, J., Morenz, R., Khondker, A., & Imtiaz, M. (2022, January). Lessons Learned from the Initial Development of a Brain Controlled Assistive Device. In 2022 IEEE 12th Annual Computing and Communication Workshop and Conference (CCWC) (pp. 0580-0585). IEEE.

Crespi, E., Cerioli, D. E., Gentili, A., Carloni, F., & Santambrogio, M. D. (2022, August). BrainTrack: A Replicable and Accessible Methodology for Customized Brain-Machine Interface Applications. In 2022 IEEE 7th Forum on Research and Technologies for Society and Industry Innovation (RTSI) (pp. 129-135). IEEE.

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Published

2024-05-15

How to Cite

Quevedo, W. X., & Celis, G. (2024). Functional 3D model of the brain from 2D ECG signals. International Journal of Engineering Insights, 2(1), 30–35. https://doi.org/10.61961/injei.v2i1.17

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