{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T12:08:44Z","timestamp":1782475724094,"version":"3.54.5"},"reference-count":54,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2021,11,28]],"date-time":"2021-11-28T00:00:00Z","timestamp":1638057600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"The Research Council of Norway","award":["273599"],"award-info":[{"award-number":["273599"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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\u2013Golay 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\u00b10.04, 0.75\u00b10.05, and 0.77\u00b10.06 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.<\/jats:p>","DOI":"10.3390\/s21237943","type":"journal-article","created":{"date-parts":[[2021,12,1]],"date-time":"2021-12-01T01:45:02Z","timestamp":1638323102000},"page":"7943","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":34,"title":["Classification of Individual Finger Movements from Right Hand Using fNIRS Signals"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5563-8920","authenticated-orcid":false,"given":"Haroon","family":"Khan","sequence":"first","affiliation":[{"name":"Department of Mechanical, Electronics and Chemical Engineering, OsloMet-Oslo Metropolitan University, 0167 Oslo, Norway"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2256-3835","authenticated-orcid":false,"given":"Farzan M.","family":"Noori","sequence":"additional","affiliation":[{"name":"Department of Informatics, University of Oslo, 0315 Oslo, Norway"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anis","family":"Yazidi","sequence":"additional","affiliation":[{"name":"Department of Computer Science, OsloMet-Oslo Metropolitan University, 0167 Oslo, Norway"},{"name":"Department of Neurosurgery, Oslo University Hospital, 0450 Oslo, Norway"},{"name":"Department of Computer Science, Norwegian University of Science and Technology, 7491 Trondheim, Norway"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5215-1834","authenticated-orcid":false,"given":"Md Zia","family":"Uddin","sequence":"additional","affiliation":[{"name":"Software and Service Innovation, SINTEF Digital, 0373 Oslo, Norway"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2498-449X","authenticated-orcid":false,"given":"M. N. Afzal","family":"Khan","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Pusan National University, Busan 46241, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7664-5513","authenticated-orcid":false,"given":"Peyman","family":"Mirtaheri","sequence":"additional","affiliation":[{"name":"Department of Mechanical, Electronics and Chemical Engineering, OsloMet-Oslo Metropolitan University, 0167 Oslo, Norway"},{"name":"Department of Biomedical Engineering, Michigan Technological University, Houghton, MI 49931, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,11,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1109\/TNSRE.2005.847377","article-title":"Functional near-infrared neuroimaging","volume":"13","author":"Izzetoglu","year":"2005","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.neuroimage.2013.11.033","article-title":"Twenty years of functional near-infrared spectroscopy: Introduction for the special issue","volume":"85","author":"Boas","year":"2014","journal-title":"NeuroImage"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12984-018-0346-2","article-title":"fNIRS-based Neurorobotic Interface for gait rehabilitation","volume":"15","author":"Khan","year":"2018","journal-title":"J. Neuroeng. Rehabil."},{"key":"ref_4","first-page":"605","article-title":"Analysis of Human Gait using Hybrid EEG-fNIRS-based BCI System: A review","volume":"14","author":"Khan","year":"2020","journal-title":"Front. Hum. Neurosci."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"435","DOI":"10.1016\/S0166-2236(97)01132-6","article-title":"Non-invasive optical spectroscopy and imaging of human brain function","volume":"20","author":"Villringer","year":"1997","journal-title":"Trends Neurosci."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"467","DOI":"10.3389\/fnins.2015.00467","article-title":"Investigating human neurovascular coupling using functional neuroimaging: A critical review of dynamic models","volume":"9","author":"Huneau","year":"2015","journal-title":"Front. Neurosci."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"65","DOI":"10.3389\/fphys.2019.00065","article-title":"Measurement of neurovascular coupling in neonates","volume":"10","author":"Hendrikx","year":"2019","journal-title":"Front. Physiol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.ajp.2017.02.009","article-title":"Functional near infra-red spectroscopy (fNIRS) in schizophrenia: A review","volume":"27","author":"Kumar","year":"2017","journal-title":"Asian J. Psychiatry"},{"key":"ref_9","first-page":"3","article-title":"fNIRS-based brain-computer interfaces: A review","volume":"9","author":"Naseer","year":"2015","journal-title":"Front. Hum. Neurosci."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"5480760","DOI":"10.1155\/2016\/5480760","article-title":"Analysis of different classification techniques for two-class functional near-infrared spectroscopy-based brain-computer interface","volume":"2016","author":"Naseer","year":"2016","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1016\/j.jneumeth.2004.05.009","article-title":"Analysis of finger-tapping movement","volume":"141","author":"Harcos","year":"2005","journal-title":"J. Neurosci. Methods"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Liao, K., Xiao, R., Gonzalez, J., and Ding, L. (2014). Decoding Individual Finger Movements from One Hand Using Human EEG Signals. PLoS ONE, 9.","DOI":"10.1371\/journal.pone.0085192"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Kondo, G., Kato, R., Yokoi, H., and Arai, T. (2010, January 3\u20137). Classification of individual finger motions hybridizing electromyogram in transient and converged states. Proceedings of the 2010 IEEE International Conference on Robotics and Automation, Anchorage, AK, USA.","DOI":"10.1109\/ROBOT.2010.5509493"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"608","DOI":"10.1109\/JBHI.2013.2249590","article-title":"Classification of finger movements for the dexterous hand prosthesis control with surface electromyography","volume":"17","author":"Bugmann","year":"2013","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1109\/TNSRE.2013.2274657","article-title":"Novel method for predicting dexterous individual finger movements by imaging muscle activity using a wearable ultrasonic system","volume":"22","author":"Sikdar","year":"2013","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Samiee, S., Hajipour, S., and Shamsollahi, M.B. (2010, January 15\u201317). Five-class finger flexion classification using ECoG signals. Proceedings of the 2010 International Conference on Intelligent and Advanced Systems, Kuala Lumpur, Malaysia.","DOI":"10.1109\/ICIAS.2010.5716225"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"29","DOI":"10.3389\/fnins.2012.00029","article-title":"Decoding Finger Movements from ECoG Signals Using Switching Linear Models","volume":"6","author":"Flamary","year":"2012","journal-title":"Front. Neurosci."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"056025","DOI":"10.1088\/1741-2552\/abb417","article-title":"Enhancing classification accuracy of fNIRS-BCI using features acquired from vector-based phase analysis","volume":"17","author":"Nazeer","year":"2020","journal-title":"J. Neural Eng."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Bak, S., Park, J., Shin, J., and Jeong, J. (2019). Open-access fNIRS dataset for classification of unilateral finger-and foot-tapping. Electronics, 8.","DOI":"10.3390\/electronics8121486"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/1743-0003-8-34","article-title":"Single-trial classification of motor imagery differing in task complexity: A functional near-infrared spectroscopy study","volume":"8","author":"Holper","year":"2011","journal-title":"J. Neuroeng. Rehabil."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"10","DOI":"10.3389\/fnbot.2020.00010","article-title":"Reduction of onset delay in functional near-infrared spectroscopy: Prediction of HbO\/HbR signals","volume":"14","author":"Zafar","year":"2020","journal-title":"Front. Neurorobotics"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"62","DOI":"10.3389\/fdata.2021.659146","article-title":"Conditional-GAN Based Data Augmentation for Deep Learning Task Classifier Improvement Using fNIRS Data","volume":"4","author":"Wickramaratne","year":"2021","journal-title":"Front. Big Data"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"24558","DOI":"10.1109\/JSEN.2021.3115405","article-title":"Classification of fNIRS Finger Tapping Data With Multi-Labeling and Deep Learning","volume":"21","author":"Sommer","year":"2021","journal-title":"IEEE Sens. J."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"025006","DOI":"10.1117\/1.NPh.3.2.025006","article-title":"Hand-grasping and finger tapping induced similar functional near-infrared spectroscopy cortical responses","volume":"3","author":"Kashou","year":"2016","journal-title":"Neurophotonics"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"645","DOI":"10.1007\/s10548-016-0507-1","article-title":"Effective connectivity of cortical sensorimotor networks during finger movement tasks: A simultaneous fNIRS, fMRI, EEG study","volume":"29","author":"Anwar","year":"2016","journal-title":"Brain Topogr."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"5326","DOI":"10.1364\/BOE.8.005326","article-title":"Dynamics of the human brain network revealed by time-frequency effective connectivity in fNIRS","volume":"8","author":"Vergotte","year":"2017","journal-title":"Biomed. Opt. Express"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"108618","DOI":"10.1016\/j.jneumeth.2020.108618","article-title":"An EEG-fNIRS hybridization technique in the four-class classification of alzheimer\u2019s disease","volume":"336","author":"Cicalese","year":"2020","journal-title":"J. Neurosci. Methods"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"35","DOI":"10.3389\/fnbot.2017.00035","article-title":"Hybrid brain\u2013computer interface techniques for improved classification accuracy and increased number of commands: A review","volume":"11","author":"Hong","year":"2017","journal-title":"Front. Neurorobotics"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1177\/1094428116658959","article-title":"Functional near-infrared spectroscopy (fNIRS) for assessing cerebral cortex function during human behavior in natural\/social situations: A concise review","volume":"22","author":"Quaresima","year":"2019","journal-title":"Organ. Res. Methods"},{"key":"ref_30","first-page":"012101","article-title":"Best practices for fNIRS publications","volume":"8","author":"Scholkmann","year":"2021","journal-title":"Neurophotonics"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"89093","DOI":"10.1109\/ACCESS.2020.2993620","article-title":"Task-specific stimulation duration for fNIRS brain-computer interface","volume":"8","author":"Khan","year":"2020","journal-title":"IEEE Access"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Santosa, H., Zhai, X., Fishburn, F., and Huppert, T. (2018). The NIRS brain AnalyzIR toolbox. Algorithms, 11.","DOI":"10.3390\/a11050073"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"505","DOI":"10.3389\/fnhum.2018.00505","article-title":"Current Status and Issues Regarding Pre-processing of fNIRS Neuroimaging Data: An Investigation of Diverse Signal Filtering Methods Within a General Linear Model Framework","volume":"12","author":"Pinti","year":"2019","journal-title":"Front. Hum. Neurosci."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"624","DOI":"10.1016\/j.bbe.2019.06.004","article-title":"Selecting the optimal conditions of Savitzky\u2013Golay filter for fNIRS signal","volume":"39","author":"Rahman","year":"2019","journal-title":"Biocybern. Biomed. Eng."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"246","DOI":"10.3389\/fnhum.2018.00246","article-title":"Feature extraction and classification methods for hybrid fNIRS-EEG brain-computer interfaces","volume":"12","author":"Hong","year":"2018","journal-title":"Front. Hum. Neurosci."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"237","DOI":"10.3389\/fnhum.2016.00237","article-title":"Determining optimal feature-combination for LDA classification of functional near-infrared spectroscopy signals in brain-computer interface application","volume":"10","author":"Naseer","year":"2016","journal-title":"Front. Hum. Neurosci."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/j.neulet.2017.03.013","article-title":"Optimal feature selection from fNIRS signals using genetic algorithms for BCI","volume":"647","author":"Noori","year":"2017","journal-title":"Neurosci. Lett."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"33","DOI":"10.3389\/fnbot.2017.00033","article-title":"Enhancing classification performance of functional near-infrared spectroscopy-brain\u2013computer interface using adaptive estimation of general linear model coefficients","volume":"11","author":"Qureshi","year":"2017","journal-title":"Front. Neurorobotics"},{"key":"ref_39","unstructured":"Elkan, C. (2012). Evaluating Classifiers, University of California."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"630","DOI":"10.1093\/neuros\/nyaa026","article-title":"Classification of individual finger movements using intracortical recordings in Human Motor Cortex","volume":"87","author":"Jorge","year":"2020","journal-title":"Neurosurgery"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Power, S.D., Kushki, A., and Chau, T. (2012). Automatic single-trial discrimination of mental arithmetic, mental singing and the no-control state from prefrontal activity: Toward a three-state NIRS-BCI. BMC Res. Notes, 5.","DOI":"10.1186\/1756-0500-5-141"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1016\/j.neulet.2014.12.029","article-title":"Classification of prefrontal and motor cortex signals for three-class fNIRS-BCI","volume":"587","author":"Hong","year":"2015","journal-title":"Neurosci. Lett."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1016\/j.heares.2016.01.009","article-title":"Decoding four different sound-categories in the auditory cortex using functional near-infrared spectroscopy","volume":"333","author":"Hong","year":"2016","journal-title":"Hear. Res."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"151","DOI":"10.3389\/fnbeh.2015.00151","article-title":"Optimal hemodynamic response model for functional near-infrared spectroscopy","volume":"9","author":"Kamran","year":"2015","journal-title":"Front. Behav. Neurosci."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"24392","DOI":"10.1109\/ACCESS.2019.2900127","article-title":"Discrimination of mental workload levels from multi-channel fNIRS using deep leaning-based approaches","volume":"7","author":"Ho","year":"2019","journal-title":"IEEE Access"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"85","DOI":"10.3389\/fnhum.2018.00085","article-title":"Suppressing systemic interference in fNIRS monitoring of the hemodynamic cortical response to motor execution and imagery","volume":"12","author":"Wu","year":"2018","journal-title":"Front. Hum. Neurosci."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"017003","DOI":"10.1117\/1.JBO.18.1.017003","article-title":"Reduction of trial-to-trial variability in functional near-infrared spectroscopy signals by accounting for resting-state functional connectivity","volume":"18","author":"Hu","year":"2013","journal-title":"J. Biomed. Opt."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Naseer, N., and Hong, K.S. (2012, January 22\u201323). Functional near-infrared spectroscopy based brain activity classification for development of a brain-computer interface. Proceedings of the 2012 International Conference of Robotics and Artificial Intelligence, Rawalpindi, Pakistan.","DOI":"10.1109\/ICRAI.2012.6413395"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Khan, M.J., Hong, K.S., Bhutta, M.R., and Naseer, N. (2014, January 22\u201324). fNIRS based dual movement control command generation using prefrontal brain activity. Proceedings of the 2014 International Conference on Robotics and Emerging Allied Technologies in Engineering (iCREATE), Islamabad, Pakistan.","DOI":"10.1109\/iCREATE.2014.6828373"},{"key":"ref_50","first-page":"1","article-title":"A Weakly Supervised Semantic Segmentation Network by Aggregating Seed Cues: The Multi-Object Proposal Generation Perspective","volume":"17","author":"Xiao","year":"2021","journal-title":"ACM J."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1657","DOI":"10.1152\/jappl.2001.90.5.1657","article-title":"Interpretation of near-infrared spectroscopy signals: A study with a newly developed perfused rat brain model","volume":"90","author":"Hoshi","year":"2001","journal-title":"J. Appl. Physiol."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/1475-925X-9-82","article-title":"Kalman estimator-and general linear model-based on-line brain activation mapping by near-infrared spectroscopy","volume":"9","author":"Hu","year":"2010","journal-title":"Biomed. Eng. Online"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1850031","DOI":"10.1142\/S0129065718500314","article-title":"Neuronal activation detection using vector phase analysis with dual threshold circles: A functional near-infrared spectroscopy study","volume":"28","author":"Zafar","year":"2018","journal-title":"Int. J. Neural Syst."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"5939","DOI":"10.1364\/BOE.434936","article-title":"Most favorable stimulation duration in the sensorimotor cortex for fNIRS-based BCI","volume":"12","author":"Khan","year":"2021","journal-title":"Biomed. Opt. 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