Multi-Tiered CNN Model for Motor Imagery Analysis: Enhancing UAV Control in Smart City Infrastructure for Industry 5.0

Authors

  • Z.T. Al-Qaysi Department of Computer Science, Computer Science and Mathematics College, Tikrit University, Tikrit, Iraq.
  • Mahmood M. Salih Department of Computer Science, Computer Science and Mathematics College, Tikrit University, Tikrit, Iraq.
  • Moceheb Lazam Shuwandy Department of Computer Science, Computer Science and Mathematics College, Tikrit University, Tikrit, Iraq.
  • M.A. Ahmed Department of Computer Science, Computer Science and Mathematics College, Tikrit University, Tikrit, Iraq.
  • Yazan S.M. Altarazi Aerodynamic, Heat Transfer & Propulsion Group, Department of Aerospace Engineering, University Putra Malaysia, Malaysia.

DOI:

https://doi.org/10.58496/ADSA/2023/007

Keywords:

Brain-Computer Interface, Deep Learning, Motor Imagery, Transfer Learning, VGG-19, Neural Network Classifier, UAV, Short-time Fourier transform

Abstract

The concept of brain-controlled UAVs, pioneered by researchers at the University of Minnesota, initiated a series of investigations. These early efforts laid the foundation for more advanced prototypes of brain-controlled UAVs. However, BCI signals are inherently complex due to their nonstationary and high-dimensionality nature. Therefore, it is crucial to carefully consider both feature extraction and the classification process. This study introduces a novel approach, combining a pretrained CNN with a classical neural network classifier and STFT spectrum, into a Multi-Tiered CNN model (MTCNN). The MTCNN model is applied to decode two-class Motor Imagery (MI) signals, enabling the control of UAV up/down movement. The experimental phase of this study involved four key experiments. The first assessed the MTCNN model's performance using a substantial dataset, resulting in an impressive classification accuracy of 99.1%. The second and third experiments evaluated the model on two different datasets for the same subjects, successfully addressing challenges associated with inter-subject and intra-subject variability. The MTCNN model achieved a remarkable classification accuracy of 99.7% on both datasets. In a fourth experiment, the model was validated on an additional dataset, achieving classification accuracies of 100% and 99.6%. Remarkably, the MTCNN model surpassed the accuracy of existing literature on two BCI competition datasets. In conclusion, the MTCNN model demonstrates its potential to decode MI signals associated with left- and right-hand movements, offering promising applications in the field of brain-controlled UAVs, particularly in controlling up/down movements. Furthermore, the MTCNN model holds the potential to contribute significantly to the BCI-MI community by facilitating the integration of this model into MI-based UAV control systems.

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Published

2023-09-20

How to Cite

Z.T. Al-Qaysi, Mahmood M. Salih, Moceheb Lazam Shuwandy, M.A. Ahmed, & Yazan S.M. Altarazi. (2023). Multi-Tiered CNN Model for Motor Imagery Analysis: Enhancing UAV Control in Smart City Infrastructure for Industry 5.0 . Applied Data Science and Analysis, 2023, 88–101. https://doi.org/10.58496/ADSA/2023/007
CITATION
DOI: 10.58496/ADSA/2023/007
Published: 2023-09-20

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Section

Articles