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Classification of Individual Finger Movements from Right Hand Using fNIRS Signal

Navngivelse 4.0 Internasjonal © 2021 by the authors ;ISSN: 1424-8220 ;EISSN: 1424-8220 ;DOI: 10.3390/s21237943

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  • Title:
    Classification of Individual Finger Movements from Right Hand Using fNIRS Signal
  • Author: Khan, Haroon ; Noori, Farzan Majeed ; Yazidi, Anis ; Uddin, Md Zia ; Khan, M.N Afzal ; Mirtaheri, Peyman
  • Subjects: Functional near-infrared spectroscopy
  • Description: Functional near-infrared spectroscopy (fNIRS) is a comparatively new noninvasive, portable, and easy-to-use brain imaging modality. However, complicated dexterous tasks such as individual finger-tapping, particularly using one hand, have been not investigated using fNIRS technology. Twenty-four healthy volunteers participated in the individual finger-tapping experiment. Data were acquired from the motor cortex using sixteen sources and sixteen detectors. In this preliminary study, we applied standard fNIRS data processing pipeline, i.e. optical densities conversation, signal processing, feature extraction, and classification algorithm implementation. Physiological and non-physiological noise is removed using 4th order band-pass Butter-worth and 3rd order Savitzky–Golay filters. Eight spatial statistical features were selected: signal-mean, peak, minimum, Skewness, Kurtosis, variance, median, and peak-to-peak form data of oxygenated haemoglobin changes. Sophisticated machine learning algorithms were applied, such as support vector machine (SVM), random forests (RF), decision trees (DT), AdaBoost, quadratic discriminant analysis (QDA), Artificial neural networks (ANN), k-nearest neighbors (kNN), and extreme gradient boosting (XG-Boost). The average classification accuracies achieved were 0.75 ± 0.04, 0.75 ± 0.05, and 0.77 ± 0.06 using k-nearest neighbors (kNN), Random forest (RF) and XGBoost, respectively. KNN, RF and XGBoost classifiers performed exceptionally well on such a high-class problem. The results need to be further investigated. In the future, a more in-depth analysis of the signal in both temporal and spatial domains will be conducted to investigate the underlying facts. The accuracies achieved are promising results and could open up a new research direction leading to enrichment of control commands generation for fNIRS-based brain-computer interface applications. This research was funded and supported by the department of MEK, OsloMet-Oslo Metropolitan University and the Norwegian Research Council under a project titled ’Patient-Centric Engineering in Rehabilitation (PACER)’ grant number 273599. publishedVersion
  • Publisher: MDPI
  • Creation Date: 2021-11
  • Language: English
  • Identifier: ISSN: 1424-8220
    EISSN: 1424-8220
    DOI: 10.3390/s21237943
  • Source: Brage Consortium Repository
    Geneva Foundation Free Medical Journals at publisher websites
    PubMed Central
    ROAD: Directory of Open Access Scholarly Resources
    ProQuest Central
    DOAJ Directory of Open Access Journals
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