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Cross-correlated spectral entropy-based classification of EEG motor imagery signal for triggering lower limb exoskeleton

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Abstract

Brain–computer interface (BCI) and assistive robotics are the emerging topics for research in the Biomedical Engineering field. Several remarkable findings have already been observed in both the areas. But still, there are some research efforts required for improving the previous results. A robust real-time guideline for controlling an assistive robotics device through motor imagery electroencephalography (EEG) signal is still under investigation. The most challenging issue in BCI is to develop the appropriate feature extraction technique for controlling the assistive device with the EEG signals. Although several feature extraction algorithms are available, cross-correlation is famous for its noise removal capability. On the other hand, entropy-based methods are most suitable for non-stationary signal characterization. A new feature extraction method is investigated using the power of cross-correlation and spectral entropy in the present work. For classification of this feature set, machine learning algorithms viz. Linear discriminant analysis, quadratic discriminant analysis, and logistic regression are used and obtained average accuracy results of 98.67%, 98.41%, and 98.48%, respectively. The other objective of the present work is to control a lower limb exoskeleton (LLE) device with this classified signal. The LLE device has been designed using a computer-aided design model and assembled in a MATLAB Simscape simulation environment. The satisfactory motion of the LLE device has been observed after triggered through the classified signal.

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Author notes

  1. Ganesh Roy

    Present address: Department of Instrumentation Engineering, Central Institute of Technology, Kokrajhar, Assam, India

Authors and Affiliations

  1. Department of Aerospace Engineering and Applied Mechanics, Indian Institute of Engineering Science and Technology, Shibpur, Howrah, West Bengal, India

    Ganesh Roy

  2. Department of Computer Science and Engineering, Sikkim Manipal Institute of Technology (SMIT), Sikkim Manipal University (SMU), Gangtok, Sikkim, India

    Akash Kumar Bhoi

  3. School of Mechatronics and Robotics, Indian Institute of Engineering Science and Technology, Shibpur, Howrah, India

    Subir Das

  4. Department of Aerospace Engineering and Applied Mechanics, Indian Institute of Engineering Science and Technology, Shibpur, Howrah, India

    Subhasis Bhaumik

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  1. Ganesh Roy
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  2. Akash Kumar Bhoi
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  4. Subhasis Bhaumik
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Correspondence to Ganesh Roy.

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Roy, G., Bhoi, A.K., Das, S. et al. Cross-correlated spectral entropy-based classification of EEG motor imagery signal for triggering lower limb exoskeleton. SIViP 16, 1831–1839 (2022). https://doi.org/10.1007/s11760-022-02142-1

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  • DOI: https://doi.org/10.1007/s11760-022-02142-1

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