Real-Time MI-BCI Signal Processing Using NVIDIA Jetson Orin Nano

Authors

  • Mahesh Khadtare, Dr. Gajanan Kharate, Dr. Dnyaneshwar Ahire

Keywords:

EEG Brain-Computer Interface (BCI) Attention Mechanism Multimodal Data Fusion Transformer

Abstract

This paper presents a real-time Motor Imagery-Based Brain-ComputerInterface (MI-BCI) system designed to operate efficiently on the NVIDIAJetson Orin Nano, a compact yet powerful edge computing platform. Thesystem addresses key challenges in EEG-based BCI development, including

References

S. Zhang, Y. Chen, and B. Yang, "Real-time Motor Imagery Classification with Embedded Deep Learning on NVIDIA Jetson Platform," IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 30, no. 4, pp. 891-900, 2022.

R. Kumar, A. Singh, and M. Lee, "Optimizing Deep Learning Models for Edge Computing: Implementation on Jetson Platform," IEEE Internetof Things Journal, vol. 9, no. 12, pp. 9876-9888, 2022

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Published

2024-05-22

How to Cite

Mahesh Khadtare, Dr. Gajanan Kharate, Dr. Dnyaneshwar Ahire. (2024). Real-Time MI-BCI Signal Processing Using NVIDIA Jetson Orin Nano . Journal of Computational Analysis and Applications (JoCAAA), 33(05), 2235–2244. Retrieved from https://www.eudoxuspress.com/index.php/pub/article/view/3280

Issue

Vol. 33 No. 05 (2024): JOCAAA

Section

Articles

License

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.