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Article

Classification of Individual Finger Movements from Right Hand Using fNIRS Signals

1
Department of Mechanical, Electronics and Chemical Engineering, OsloMet-Oslo Metropolitan University, 0167 Oslo, Norway
2
Department of Informatics, University of Oslo, 0315 Oslo, Norway
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Department of Computer Science, OsloMet-Oslo Metropolitan University, 0167 Oslo, Norway
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Department of Neurosurgery, Oslo University Hospital, 0450 Oslo, Norway
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Department of Computer Science, Norwegian University of Science and Technology, 7491 Trondheim, Norway
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Software and Service Innovation, SINTEF Digital, 0373 Oslo, Norway
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School of Mechanical Engineering, Pusan National University, Busan 46241, Korea
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Department of Biomedical Engineering, Michigan Technological University, Houghton, MI 49931, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Tara Julia Hamilton
Sensors 2021, 21(23), 7943; https://doi.org/10.3390/s21237943
Received: 8 November 2021 / Revised: 25 November 2021 / Accepted: 26 November 2021 / Published: 28 November 2021
(This article belongs to the Special Issue Signal Processing for Brain–Computer Interfaces)
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 (XGBoost). 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. View Full-Text
Keywords: functional near-infrared spectroscopy (fNIRS); finger-tapping; classification; motor cortex; machine learning functional near-infrared spectroscopy (fNIRS); finger-tapping; classification; motor cortex; machine learning
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MDPI and ACS Style

Khan, H.; Noori, F.M.; Yazidi, A.; Uddin, M.Z.; Khan, M.N.A.; Mirtaheri, P. Classification of Individual Finger Movements from Right Hand Using fNIRS Signals. Sensors 2021, 21, 7943. https://doi.org/10.3390/s21237943

AMA Style

Khan H, Noori FM, Yazidi A, Uddin MZ, Khan MNA, Mirtaheri P. Classification of Individual Finger Movements from Right Hand Using fNIRS Signals. Sensors. 2021; 21(23):7943. https://doi.org/10.3390/s21237943

Chicago/Turabian Style

Khan, Haroon, Farzan M. Noori, Anis Yazidi, Md Zia Uddin, M. N. Afzal Khan, and Peyman Mirtaheri. 2021. "Classification of Individual Finger Movements from Right Hand Using fNIRS Signals" Sensors 21, no. 23: 7943. https://doi.org/10.3390/s21237943

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