A Between-Subject fNIRS-BCI Study on Detecting Self-Regulated Intention During Walking
| Autor: | Min Su, Chunguang Li, Lining Sun, Jiacheng Xu, Hedian Jin |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Male
Support Vector Machine Computer science Entropy Biomedical Engineering Decision tree Poison control Intention Walking 02 engineering and technology Young Adult 03 medical and health sciences 0302 clinical medicine Gait (human) 0202 electrical engineering electronic engineering information engineering Internal Medicine Humans False Positive Reactions Entropy (energy dispersal) Gait Brain–computer interface Spectroscopy Near-Infrared business.industry General Neuroscience Decision Trees Rehabilitation Reproducibility of Results Pattern recognition Healthy Volunteers Support vector machine Area Under Curve Brain-Computer Interfaces Functional near-infrared spectroscopy Female 020201 artificial intelligence & image processing Artificial intelligence False positive rate business Algorithms 030217 neurology & neurosurgery |
| Zdroj: | IEEE Transactions on Neural Systems and Rehabilitation Engineering. 28:531-540 |
| ISSN: | 1558-0210 1534-4320 |
| Popis: | Objective: Most BCI (brain-computer interface) studies have focused on detecting motion intention from a resting state. However, the dynamic regulation of two motion states, which usually happens in real life, is rarely studied. Besides, popular within-subject methods also require an extensive and time-consuming learning stage when testing on a new subject. This paper proposed a method to discriminate dynamic gait- adjustment intention with strong adaptability for different subjects. Methods: Cerebral hemoglobin signals obtained from 30 subjects were studied to decode gait-adjustment intention. Cerebral hemoglobin information was recorded by using fNIRS (functional near infrared spectroscopy) technology. Mathematical morphology filtering was applied to remove zero drift and EWM (Entropy Weight Method) was used to calculate the average hemoglobin values over Regions of Interest (ROIs). The gradient boosting decision tree (GBDT) was utilized to detect the onset of self-regulated intention. A 2-layer-GA-SVM (Genetic Algorithm-Support Vector Machine) model based on stacking algorithm was further proposed to identify the four types of self-regulated intention (speed increase, speed reduction, step increase, and step reduction). Results: It was found that GBDT had a good performance to detect the onset intention with an average AUC (Area Under Curve) of 0.894. The 2-layer-GA-SVM model boosted the average ACC (accuracy) of four types of intention from 70.6% to 84.4% ( ${p} =0.005$ ) from the single GA-SVM model. Furthermore, the proposed method passed pseudo-online test with the average results as following: AUC = 0.883, TPR (True Positive Rate) = 97.5%, FPR (False Positive Rate) = 0.11%, and LAY (Detection Latency) = −0.52 ± 2.57 seconds for the recognition of gait-adjustment intention; ACC = 80% for the recognition of adjusted gait. Conclusion: The results indicate that it is feasible to decode dynamic gait-adjustment intentions from a motion state for different subjects based on fNIRS technology. It has a potential to realize the practical application of fNIRS-based brain-computer interface technology in controlling walking-assistive devices. |
| Databáze: | OpenAIRE |
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