Detecting self-paced walking intention based on fNIRS technology for the development of BCI

Li, Chunguang; Xu, Jiacheng; Zhu, Yufei; Kuang, Shaolong; Qu, Wei; Sun, Lining

doi:10.1007/s11517-020-02140-w

Cited by 12 publications

(8 citation statements)

References 28 publications

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“…As well, pre-processing and processing should account for differences in the noise due to time, ROI, and population. The information in this review contained in Table S1 [ 88 , 89 , 90 , 91 , 92 , 93 , 94 , 95 , 96 , 97 , 98 , 99 , 100 , 101 , 102 , 103 , 104 , 105 , 106 , 107 , 108 , 109 , 110 , 111 , 112 , 113 , 114 , 115 , 116 , 117 , 118 , 119 , 120 , 121 , 122 , 123 , 124 , 125 , 126 , 127 , 128 , 129 , 130 , 131 , 132 , 133 , 134 , 135 , 136 , 137 , 138 , 139 , 140 ,…”

Section: Discussionmentioning

confidence: 99%

Data Processing in Functional Near-Infrared Spectroscopy (fNIRS) Motor Control Research

2021

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FNIRS pre-processing and processing methodologies are very important—how a researcher chooses to process their data can change the outcome of an experiment. The purpose of this review is to provide a guide on fNIRS pre-processing and processing techniques pertinent to the field of human motor control research. One hundred and twenty-three articles were selected from the motor control field and were examined on the basis of their fNIRS pre-processing and processing methodologies. Information was gathered about the most frequently used techniques in the field, which included frequency cutoff filters, wavelet filters, smoothing filters, and the general linear model (GLM). We discuss the methodologies of and considerations for these frequently used techniques, as well as those for some alternative techniques. Additionally, general considerations for processing are discussed.

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Section: Discussionmentioning

confidence: 99%

Data Processing in Functional Near-Infrared Spectroscopy (fNIRS) Motor Control Research

2021

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“…Most of the filters have the characteristic of either band-pass filter (61.3%) or low pass filter (59.7%). Some other filter commonly used are Z-score based artifacts removal (Lopez-Larraz et al, 2016;Li et al, 2020b), regression analysis algorithm (Li et al, 2016), BW (Bulea et al, 2014;Hortal et al, 2016;Gui et al, 2017;Zhang et al, 2017;Contreras-Vidal et al, 2018;Liu et al, 2018), Chebyshev filter (Li et al, 2020a,b), Kalman filters (Khan R. A. et al, 2018). However, some recent studies showed high inter-subject variability and recorded the difficulty in eliminating gait-related movement artifacts from EEG signals (Kline et al, 2015;Snyder et al, 2015;Bradford et al, 2016;Nathan and Contreras-Vidal, 2016…”

Section: Other Common Filtersmentioning

confidence: 99%

“…In fNIRS studies HbO, HbR and HbT concentrations are commonly used as features in most of the fNIRS-based BCI studies (Blokland et al, 2013;Hedian et al, 2018;Li et al, 2020b). Some other common time domain feature used are: signal mean (SM), signal skewness (SK), kurtosis (Z), signal variance (Var), and signal peak show in Equations ( 13b) to (13d) (Naseer et al, 2016;Aghajani et al, 2017;Khan and Hong, 2017;Li et al, 2017Li et al, , 2020aKhan R. A. et al, 2018;Shin, 2020).…”

Section: Other Common Featuresmentioning

confidence: 99%

“…GBDT proposed by (Friedman, 2001) is suitable for the intention detection model in real-time and handle large scale data (Li et al, 2020b). The gradient boosting process involves three components: (1) loss function, which needs to be optimized; (2) weak learner for making a prediction; and (3) additive model, which is used to add weak learners to minimize the loss function.…”

Section: Gradient Boost Decision Tree (Gbdt)mentioning

confidence: 99%

“…Likewise, hemodynamic changes can also help to detect movement intentions. Li et al (2020b) performed an fNIRSbased study to detect self-paced walking intention, which forms a foundation for the fNIRS-based BCI system for control of gait assistive devices. Li et al (2020a) proved the feasibility of the fNIRS-based BCI system for decoding and detecting the motion intention in dynamic situations.…”

Section: Gait Intention Detectionmentioning

confidence: 99%

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Analysis of Human Gait Using Hybrid EEG-fNIRS-Based BCI System: A Review

Khan

Naseer

Yazidi

et al. 2021

Front. Hum. Neurosci.

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Human gait is a complex activity that requires high coordination between the central nervous system, the limb, and the musculoskeletal system. More research is needed to understand the latter coordination's complexity in designing better and more effective rehabilitation strategies for gait disorders. Electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) are among the most used technologies for monitoring brain activities due to portability, non-invasiveness, and relatively low cost compared to others. Fusing EEG and fNIRS is a well-known and established methodology proven to enhance brain–computer interface (BCI) performance in terms of classification accuracy, number of control commands, and response time. Although there has been significant research exploring hybrid BCI (hBCI) involving both EEG and fNIRS for different types of tasks and human activities, human gait remains still underinvestigated. In this article, we aim to shed light on the recent development in the analysis of human gait using a hybrid EEG-fNIRS-based BCI system. The current review has followed guidelines of preferred reporting items for systematic reviews and meta-Analyses (PRISMA) during the data collection and selection phase. In this review, we put a particular focus on the commonly used signal processing and machine learning algorithms, as well as survey the potential applications of gait analysis. We distill some of the critical findings of this survey as follows. First, hardware specifications and experimental paradigms should be carefully considered because of their direct impact on the quality of gait assessment. Second, since both modalities, EEG and fNIRS, are sensitive to motion artifacts, instrumental, and physiological noises, there is a quest for more robust and sophisticated signal processing algorithms. Third, hybrid temporal and spatial features, obtained by virtue of fusing EEG and fNIRS and associated with cortical activation, can help better identify the correlation between brain activation and gait. In conclusion, hBCI (EEG + fNIRS) system is not yet much explored for the lower limb due to its complexity compared to the higher limb. Existing BCI systems for gait monitoring tend to only focus on one modality. We foresee a vast potential in adopting hBCI in gait analysis. Imminent technical breakthroughs are expected using hybrid EEG-fNIRS-based BCI for gait to control assistive devices and Monitor neuro-plasticity in neuro-rehabilitation. However, although those hybrid systems perform well in a controlled experimental environment when it comes to adopting them as a certified medical device in real-life clinical applications, there is still a long way to go.

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Human–Robot Interaction for Rehabilitation Robotics

Guo

Yang

2021

Digitalization in Healthcare

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Detecting self-paced walking intention based on fNIRS technology for the development of BCI

Cited by 12 publications

References 28 publications

Data Processing in Functional Near-Infrared Spectroscopy (fNIRS) Motor Control Research

Data Processing in Functional Near-Infrared Spectroscopy (fNIRS) Motor Control Research

Analysis of Human Gait Using Hybrid EEG-fNIRS-Based BCI System: A Review

Human–Robot Interaction for Rehabilitation Robotics

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