Real Time Brain Signals Viewer
DOI:
https://doi.org/10.61961/injei.v2i1.16Keywords:
brain signals, 2d visualization, real time signals, emotiv-epoc, unityAbstract
This paper addresses the successful integration of Emotiv EPOC and Unity for real-time visualization of brain signals, representing a significant advance in understanding and interacting with brain activity. Real-time visualization of brain signals offers fundamental opportunities in neuroscience, brain-computer interface, and cognitive therapy. Through this study, a solid methodology was established to acquire, process, and graphically represent brain signals in 2D, allowing for an immersive and research-based experience. The results, products generated and implications in areas such as BCI and cognitive therapy are presented. Additionally, future exploration of integration with virtual reality and clinical validation is proposed to advance the understanding and application of real-time brain activity. This research lays the foundation for low-cost research and applications, promoting a deeper understanding of the human mind and its interaction with technology.
Downloads
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. DOI: https://doi.org/10.1155/2015/514361
Ramadan, R. A., & Vasilakos, A. V. (2017). Brain computer interface: control signals review. Neurocomputing, 223, 26-44. DOI: https://doi.org/10.1016/j.neucom.2016.10.024
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. DOI: https://doi.org/10.1007/978-3-319-92013-9_16
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. DOI: https://doi.org/10.1007/978-3-030-30581-9_3
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. DOI: https://doi.org/10.1007/978-94-007-4792-0_24
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. DOI: https://doi.org/10.1109/GWS.2018.8686639
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. DOI: https://doi.org/10.1016/j.bbe.2018.02.007
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. DOI: https://doi.org/10.1109/PERVASIVE.2015.7087109
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. DOI: https://doi.org/10.17706/jsw.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. DOI: https://doi.org/10.1109/CarpathianCC.2014.6843587
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. DOI: https://doi.org/10.1109/CIS-RAM47153.2019.9095807
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. DOI: https://doi.org/10.1109/CCWC54503.2022.9720815
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. DOI: https://doi.org/10.1109/RTSI55261.2022.9905223
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Gregory Celis, Washington X. Quevedo
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License 4.0 that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.