AI Article Synopsis

  • * The study introduces a novel approach that combines ReliefF-based Deep Neural Networks (DNNs) and utilizes the MILimbEEG dataset of EEG signals to extract important statistical features during motor activities.
  • * The results show that the DNN-ReliefF model significantly outperforms traditional DNN models, achieving accuracy, precision, and recall rates above 97%, highlighting the enhancement in prosthetic control systems offered by this new method.

Article Abstract

In the rapidly advancing field of biomedical engineering, effective real-time control of artificial limbs is a pressing research concern. Addressing this, the current study introduces a pioneering method for augmenting task recognition in prosthetic control systems, combining a ReliefF-based Deep Neural Networks (DNNs) approach. This paper has leveraged the MILimbEEG dataset, a comprehensive rich source collection of EEG signals, to calculate statistical features of Arithmetic Mean (AM), Standard Deviation (SD), and Skewness (S) across various motor activities. Supreme Feature Selection (SFS), of the adopted time-domain features, was performed using the ReliefF algorithm. The highest scored DNN-ReliefF developed model demonstrated remarkable performance, achieving accuracy, precision, and recall rates of 97.4 %, 97.3 %, and 97.4 %, respectively. In contrast, a traditional DNN model yielded accuracy, precision, and recall rates of 50.8 %, 51.1 %, and 50.8 %, highlighting the significant improvements made possible by incorporating SFS. This stark contrast underscores the transformative potential of incorporating ReliefF, situating the DNN-ReliefF model as a robust platform for forthcoming advancements in real-time prosthetic control systems.

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Source
http://dx.doi.org/10.1016/j.compbiomed.2023.107894DOI Listing

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