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Cognitive performance detection using entropy-based features and lead-specific approach

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Abstract

Detecting cognitive performance during mental arithmetic allows researchers to observe and identify the brain’s response to stimuli. Existing non-invasive methods for automated cognitive performance detection need improvements in terms of accuracy. In this work, a novel approach for cognitive performance has been proposed which uses short-duration electroencephalography (EEG) signal (4.094 s). Stationary wavelet transform (SWT) has been used to decompose the signal followed by extraction of entropy-based features and classification using selected attributes. To tackle the imbalanced data issue, adaptive synthetic sampling approach has been used. The proposed technique works in two modes: multi-lead approach (MLA), where EEG signal from multiple leads was used, and a novel lead-specific approach (LSA), where EEG signal from a single lead (F4) was used. A high accuracy of 94.00% in MLA and 93.70% in LSA reflects reliability of the proposed technique. The use of short-duration single-lead EEG signal makes this technique suitable for continuous monitoring system of cognitive performance during mental workload.

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Acknowledgements

This work is funded under TEQIP-III, CRS application ID: 1-5730990370.

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Authors and Affiliations

  1. School of Electronics Engineering, VIT-AP University, Amaravati, India

    Lakhan Dev Sharma

  2. Electronics & Communication Engineering, M.L.V. Textile & Engineering College, Bhilwara, India

    Ritesh Kumar Saraswat

  3. Electronics & Communication Engineering, Dr. B. R. Ambedkar National Institute of Technology, Jalandhar, India

    Ramesh Kumar Sunkaria

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  1. Lakhan Dev Sharma
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  2. Ritesh Kumar Saraswat
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Correspondence to Lakhan Dev Sharma.

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Sharma, L.D., Saraswat, R.K. & Sunkaria, R.K. Cognitive performance detection using entropy-based features and lead-specific approach. SIViP 15, 1821–1828 (2021). https://doi.org/10.1007/s11760-021-01927-0

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  • DOI: https://doi.org/10.1007/s11760-021-01927-0

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