Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)Jutinico Alarcón, Andrés LeonardoOrjuela Cañón, Álvaro DavidReyes Guzmán, Edwin AlfredoTriana Guzmán, Nayid2023-05-182023-05-182022-12-07http://repositorio.uan.edu.co/handle/123456789/8023Brain-computer interface (BCI) systems based on electroencephalography (EEG) and motor imagination (MI), have shown promising advances for the motor rehabilitation of lower extremities. However, in the state of the art there has been little explored about the MR of the lower limb, especially little is known about MR for standing and sitting. By Therefore, this paper presents an EEG-based ICC system for MI interpretation of these types of movements. The purpose of this system is to restore some mobility to people with disorders severe neuromuscular disorders that cannot exert the force required to move the physical interface (mouse, keyboard, joystick, microphone, or other peripherals) that use standing devices to perform transition from sitting to bipedal positionLos sistemas de interfaz cerebro-computadora (ICC) basados en electroencefalografía (EEG) e imaginación motora (IM), han mostrado avances prometedores para la rehabilitación motriz de las extremidades inferiores. Sin embargo, en el estado del arte ha sido poco explorado sobre la IM del miembro inferior, especialmente se sabe poco acerca de la IM para la bipedestación y la sedestación. Por lo tanto, este trabajo presenta un sistema de ICC basado en EEG para la interpretación de la IM de estos tipos de movimientos. El propósito de este sistema es devolver cierta movilidad a personas con trastornos neuromusculares graves que no pueden imprimir la fuerza que se requiere para mover la interfaz física (ratón, teclado, joystick, micrófono, u otros periféricos) que usan dispositivos bipedestadores para realizar la transición de la posición sedente-bípedaspaAcceso abiertointerfaz cerebro-computadora (ICC)computadora (ICC), electroencefalografía (EEG),imaginación motora (IM), sentarse-pararse, procesamiento digital de señales, reconocimiento de patrones600Sistema y método para la interpretación de la imaginación motora de los movimientos de ponerse de pie y sentarse basado en interfaz cerebro computadoraTesis y disertaciones (Maestría y/o Doctorado)brain-computer interface (BCI), electroencephalography (EEGmotor imagery (MI), sit-stand, digital signal processing, pattern recognitioninfo:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Abdulkader, S. N., Atia, A., & Mostafa, M.-S. M. (2015). Brain computer interfacing: Applications and challenges. Egyptian Informatics Journal, 16(2), 213–230. https://doi.org/10.1016/j.eij.2015.06.002Abiri, R., Borhani, S., Sellers, E. W., Jiang, Y., & Zhao, X. (2019). A comprehensive review of EEG-based brain–computer interface paradigms. Journal of Neural Engineering, 16(1), 1–43. https://doi.org/10.1088/1741-2552/aaf12eAggarwal, S., & Chugh, N. (2019). Signal processing techniques for motor imagery brain computer interface: A review. Array, 1–2, 1–12. https://doi.org/10.1016/j.array.2019.100003Aggarwal, S., & Chugh, N. (2022). Review of Machine Learning Techniques for EEG Based Brain Computer Interface. Archives of Computational Methods in Engineering, 29(5), 3001–3020. https://doi.org/10.1007/s11831-021-09684-6Ahn, M., Lee, M., Choi, J., & Jun, S. (2014). A Review of Brain-Computer Interface Games and an Opinion Survey from Researchers, Developers and Users. Sensors, 14(8), 14601–14633. https://doi.org/10.3390/s140814601Ajiboye, A. B., Willett, F. R., Young, D. R., Memberg, W. D., Murphy, B. A., Miller, J. P., Walter, B. L., Sweet, J. A., Hoyen, H. A., Keith, M. W., Peckham, P. H., Simeral, J. D., Donoghue, J. P., Hochberg, L. R., & Kirsch, R. F. (2017). Restoration of reaching and grasping movements through brain-controlled muscle stimulation in a person with tetraplegia: a proof-of-concept demonstration. The Lancet, 389(10081), 1821–1830. https://doi.org/10.1016/S0140- 6736(17)30601-3Al-Fahoum, A. S., & Al-Fraihat, A. A. (2014). Methods of EEG Signal Features Extraction Using Linear Analysis in Frequency and Time-Frequency Domains. ISRN Neuroscience, 2014, 1–7. https://doi.org/10.1155/2014/730218Al-Saegh, A., Dawwd, S. A., & Abdul-Jabbar, J. M. (2021). Deep learning for motor imagery EEGbased classification: A review. Biomedical Signal Processing and Control, 63, 1–21. https://doi.org/10.1016/j.bspc.2020.102172Allison, B. Z., & Neuper, C. (2010). Could Anyone Use a BCI? In D. S. Tan & A. Nijholt (Eds.), Brain-Computer Interfaces: Applying our Minds to Human-Computer Interaction (1st ed., pp. 35–54). Springer, London. https://doi.org/10.1007/978-1-84996-272-8_3Alyasseri, Z. A. A., Khadeer, A. T., Al-Betar, M. A., Abasi, A., Makhadmeh, S., & Ali, N. S. (2019). The Effects of EEG Feature Extraction Using Multi-Wavelet Decomposition for Mental Tasks Classification. Proceedings of the International Conference on Information and Communication Technology - ICICT ’19, 139–146. https://doi.org/10.1145/3321289.3321327instname:Universidad Antonio Nariñoreponame:Repositorio Institucional UANrepourl:https://repositorio.uan.edu.co/