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Classification of Individual Finger Movements from Right Hand Using fNIRS Signals
| Content Provider | MDPI |
|---|---|
| Author | Khan, Haroon Noori, Farzan M. Yazidi, Anis Uddin, Zia Khan, M. N. Afzal Mirtaheri, Peyman |
| Copyright Year | 2021 |
| Abstract | 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 |
| Starting Page | 7943 |
| e-ISSN | 14248220 |
| DOI | 10.3390/s21237943 |
| Journal | Sensors |
| Issue Number | 23 |
| Volume Number | 21 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2021-11-28 |
| Access Restriction | Open |
| Subject Keyword | Sensors Spectroscopy Functional Near-infrared Spectroscopy (fnirs) Finger-tapping Classification Motor Cortex Machine Learning |
| Content Type | Text |
| Resource Type | Article |
