A Between-Subject fNIRS-BCI Study on Detecting Self-Regulated Intention During Walking

From brain to worksite: the role of fNIRS in cognitive studies and worker safety

Effective hazard recognition and decision-making are crucial factors in ensuring workplace safety in the construction industry. Workers' cognition closely relates to that hazard-handling behavior. Functional near-infrared spectroscopy (fNIRS) is a neurotechique tool that can evaluate the concentration vibration of oxygenated hemoglobin [ H b O 2 ] and deoxygenated hemoglobin [H b R ] to reflect the cognition process. It is essential to monitor workers' brain activity by fNIRS to analyze their cognitive status and reveal the mechanism in hazard recognition and decision-making process, providing guidance for capability evaluation and management enhancement. This review offers a systematic assessment of fNIRS, encompassing the basic theory, experiment analysis, data analysis, and discussion. A literature search and content analysis are conducted to identify the application of fNIRS in construction safety research, the limitations of selected studies, and the prospects of fNIRS in future research. This article serves as a guide for researchers keen on harnessing fNIRS to bolster construction safety standards and forwards insightful recommendations for subsequent studies.

The changes of neuroactivity of Tui Na (Chinese massage) at Hegu acupoint on sensorimotor cortex in stroke patients with upper limb motor dysfunction: a fNIRS study

BACKGROUND

Tui Na (Chinese massage) is a relatively simple, inexpensive, and non-invasive intervention, and has been used to treat stroke patients for many years in China. Tui Na acts on specific parts of the body which are called meridians and acupoints to achieve the role of treating diseases. Yet the underlying neural mechanism associated with Tui Na is not clear due to the lack of detection methods.

OBJECTIVE

Functional near-infrared spectroscopy (fNIRS) was used to explore the changes of sensorimotor cortical neural activity in patients with upper limb motor dysfunction of stroke and healthy control groups during Tui Na Hegu Point.

METHODS

Ten patients with unilateral upper limb motor dysfunction after stroke and eight healthy subjects received Tui Na. fNIRS was used to record the hemodynamic data in the sensorimotor cortex and the changes in blood flow were calculated based on oxygenated hemoglobin (Oxy-Hb), the task session involved repetitive Tui Na on Hegu acupoint, using a block design [six cycles: rest (20 seconds); Tui Na (20 seconds); rest (30 seconds)]. The changes in neural activity in sensorimotor cortex could be inferred according to the principle of neurovascular coupling, and the number of activated channels in the bilateral hemisphere was used to calculate the lateralization index.

RESULT

1. For hemodynamic response induced by Hegu acupoint Tui Na, a dominant increase in the contralesional primary sensorimotor cortex during Hegu point Tui Na of the less affected arm in stroke patients was observed, as well as that in healthy controls, while this contralateral pattern was absent during Hegu point Tui Na of the affected arm in stroke patients. 2. Concerning the lateralization index in stroke patients, a significant difference was observed between lateralization index values for the affected arm and the less affected arm (P < 0.05). Wilcoxon tests showed a significant difference between lateralization index values for the affected arm in stroke patients and lateralization index values for the dominant upper limb in healthy controls (P < 0.05), and no significant difference between lateralization index values for the less affected arm in stroke patients and that in healthy controls (P = 0.36).

CONCLUSION

The combination of Tui Na and fNIRS has the potential to reflect the functional status of sensorimotor neural circuits. The changes of neuroactivity in the sensorimotor cortex when Tui Na Hegu acupoint indicate that there is a certain correlation between acupoints in traditional Chinese medicine and neural circuits.

Explainable artificial intelligence model to predict brain states from fNIRS signals

Objective: Most Deep Learning (DL) methods for the classification of functional Near-Infrared Spectroscopy (fNIRS) signals do so without explaining which features contribute to the classification of a task or imagery. An explainable artificial intelligence (xAI) system that can decompose the Deep Learning mode's output onto the input variables for fNIRS signals is described here. Approach: We propose an xAI-fNIRS system that consists of a classification module and an explanation module. The classification module consists of two separately trained sliding window-based classifiers, namely, (i) 1-D Convolutional Neural Network (CNN); and (ii) Long Short-Term Memory (LSTM). The explanation module uses SHAP (SHapley Additive exPlanations) to explain the CNN model's output in terms of the model's input. Main results: We observed that the classification module was able to classify two types of datasets: (a) Motor task (MT), acquired from three subjects; and (b) Motor imagery (MI), acquired from 29 subjects, with an accuracy of over 96% for both CNN and LSTM models. The explanation module was able to identify the channels contributing the most to the classification of MI or MT and therefore identify the channel locations and whether they correspond to oxy- or deoxy-hemoglobin levels in those locations. Significance: The xAI-fNIRS system can distinguish between the brain states related to overt and covert motor imagery from fNIRS signals with high classification accuracy and is able to explain the signal features that discriminate between the brain states of interest.

Experiment protocols for brain-body imaging of locomotion: A systematic review

Introduction

Human locomotion is affected by several factors, such as growth and aging, health conditions, and physical activity levels for maintaining overall health and well-being. Notably, impaired locomotion is a prevalent cause of disability, significantly impacting the quality of life of individuals. The uniqueness and high prevalence of human locomotion have led to a surge of research to develop experimental protocols for studying the brain substrates, muscle responses, and motion signatures associated with locomotion. However, from a technical perspective, reproducing locomotion experiments has been challenging due to the lack of standardized protocols and benchmarking tools, which impairs the evaluation of research quality and the validation of previous findings.

Methods

This paper addresses the challenges by conducting a systematic review of existing neuroimaging studies on human locomotion, focusing on the settings of experimental protocols, such as locomotion intensity, duration, distance, adopted brain imaging technologies, and corresponding brain activation patterns. Also, this study provides practical recommendations for future experiment protocols.

Results

The findings indicate that EEG is the preferred neuroimaging sensor for detecting brain activity patterns, compared to fMRI, fNIRS, and PET. Walking is the most studied human locomotion task, likely due to its fundamental nature and status as a reference task. In contrast, running has received little attention in research. Additionally, cycling on an ergometer at a speed of 60 rpm using fNIRS has provided some research basis. Dual-task walking tasks are typically used to observe changes in cognitive function. Moreover, research on locomotion has primarily focused on healthy individuals, as this is the scenario most closely resembling free-living activity in real-world environments.

Discussion

Finally, the paper outlines the standards and recommendations for setting up future experiment protocols based on the review findings. It discusses the impact of neurological and musculoskeletal factors, as well as the cognitive and locomotive demands, on the experiment design. It also considers the limitations imposed by the sensing techniques used, including the acceptable level of motion artifacts in brain-body imaging experiments and the effects of spatial and temporal resolutions on brain sensor performance. Additionally, various experiment protocol constraints that need to be addressed and analyzed are explained.

The Identification of Elderly People with High Fall Risk Using Machine Learning Algorithms

Falling is an important public health issue, and predicting the fall risk can reduce the incidence of injury events in the elderly. However, most of the existing studies may have additional human and financial costs for community workers and doctors. Therefore, it is socially important to identify elderly people who are at high fall risk through a reasonable and cost-effective method. We evaluated the potential of multifractal, machine learning algorithms to identify the elderly at high fall risk. We developed a 42-point calibration model of the human body and recorded the three-dimensional coordinate datasets. The stability of the motion trajectory is calculated by the multifractal algorithm and used as an input dimension to compare the performance of the six classifiers. The results showed that the instability of the faller group was significantly greater than that of the no-faller group in the male and female cohorts (p < 0.005), and the Gradient Boosting Decision Tree classifier showed the best performance. The findings could help elderly people at high fall risk to identify individualized risk factors and facilitate tailored fall interventions.

Optimising the classification of feature-based attention in frequency-tagged electroencephalography data

Brain-computer interfaces (BCIs) are a rapidly expanding field of study and require accurate and reliable real-time decoding of patterns of neural activity. These protocols often exploit selective attention, a neural mechanism that prioritises the sensory processing of task-relevant stimulus features (feature-based attention) or task-relevant spatial locations (spatial attention). Within the visual modality, attentional modulation of neural responses to different inputs is well indexed by steady-state visual evoked potentials (SSVEPs). These signals are reliably present in single-trial electroencephalography (EEG) data, are largely resilient to common EEG artifacts, and allow separation of neural responses to numerous concurrently presented visual stimuli. To date, efforts to use single-trial SSVEPs to classify visual attention for BCI control have largely focused on spatial attention rather than feature-based attention. Here, we present a dataset that allows for the development and benchmarking of algorithms to classify feature-based attention using single-trial EEG data. The dataset includes EEG and behavioural responses from 30 healthy human participants who performed a feature-based motion discrimination task on frequency tagged visual stimuli.

Crossing time windows optimization based on mutual information for hybrid BCI

Hybrid EEG-fNIRS brain-computer interface (HBCI) is widely employed to enhance BCI performance. EEG and fNIRS signals are combined to increase the dimensionality of the information. Time windows are used to select EEG and fNIRS singles synchronously. However, it ignores that specific modal signals have their own characteristics, when the task is stimulated, the information between the modalities will mismatch at the moment, which has a significant impact on the classification performance. Here we propose a novel crossing time windows optimization for mental arithmetic (MA) based BCI. The EEG and fNIRS signals were segmented separately by sliding time windows. Then crossing time windows (CTW) were combined with each one segment from EEG and fNIRS selected independently. Furthermore, EEG and fNIRS features were extracted using Filter Bank Common Spatial Pattern (FBCSP) and statistical methods from each sample. Mutual information was calculated for FBCSP and statistical features to characterize the discrimination of crossing time windows, and the optimal window would be selected based on the largest mutual information. Finally, a sparse structured framework of Fisher Lasso feature selection (FLFS) was designed to select the joint features, and conventional Linear Discriminant Analysis (LDA) was employed to perform classification. We used proposed method for a MA dataset. The classification accuracy of the proposed method is 92.52 ± 5.38% and higher than other methods, which shows the rationality and superiority of the proposed method.

Decoding different working memory states during an operation span task from prefrontal fNIRS signals

We propose an effective and practical decoding method of different mental states for potential applications for the design of brain-computer interfaces, prediction of cognitive behaviour, and investigation of cognitive mechanism. Functional near infrared spectroscopy (fNIRS) signals that interrogated the prefrontal and parietal cortices and were evaluated by generalized linear model were recorded when nineteen healthy adults performed the operation span (OSPAN) task. The oxygenated hemoglobin changes during OSPAN, response, and rest periods were classified with a support vector machine (SVM). The relevance vector regression algorithm was utilized for prediction of cognitive performance based on multidomain features of fNIRS signals from the OSPAN task. We acquired decent classification accuracies for OSPAN vs. response (above 91.2%) and for OSPAN vs. rest (above 94.7%). Eight of the ten cognitive testing scores could be predicted from the combination of OSPAN and response features, which indicated the brain hemodynamic responses contain meaningful information suitable for predicting cognitive performance.

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

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.

Decoding of Walking Imagery and Idle State Using Sparse Representation Based on fNIRS

Objectives

Brain-computer interface (BCI) based on functional near-infrared spectroscopy (fNIRS) is expected to provide an optional active rehabilitation training method for patients with walking dysfunction, which will affect their quality of life seriously. Sparse representation classification (SRC) oxyhemoglobin (HbO) concentration was used to decode walking imagery and idle state to construct fNIRS-BCI based on walking imagery.

Methods

15 subjects were recruited and fNIRS signals were collected during walking imagery and idle state. Firstly, band-pass filtering and baseline drift correction for HbO signal were carried out, and then the mean value, peak value, and root mean square (RMS) of HbO and their combinations were extracted as classification features; SRC was used to identify the extracted features and the result of SRC was compared with those of support vector machine (SVM), K-Nearest Neighbor (KNN), linear discriminant analysis (LDA), and logistic regression (LR).

Results

The experimental results showed that the average classification accuracy for walking imagery and idle state by SRC using three features combination was 91.55±3.30%, which was significantly higher than those of SVM, KNN, LDA, and LR (86.37±4.42%, 85.65±5.01%, 86.43±4.41%, and 76.14±5.32%, respectively), and the classification accuracy of other combined features was higher than that of single feature.

Conclusions

The study showed that introducing SRC into fNIRS-BCI can effectively identify walking imagery and idle state. It also showed that different time windows for feature extraction have an impact on the classification results, and the time window of 2–8 s achieved a better classification accuracy (94.33±2.60%) than other time windows. Significance. The study was expected to provide a new and optional active rehabilitation training method for patients with walking dysfunction. In addition, the experiment was also a rare study based on fNIRS-BCI using SRC to decode walking imagery and idle state.

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

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.

Subject-Specific feature selection for near infrared spectroscopy based brain-computer interfaces

BACKGROUND AND OBJECTIVE

Brain-computer interfaces (BCIs) enable people to control an external device by analyzing the brain's neural activity. Functional near-infrared spectroscopy (fNIRS), which is an emerging optical imaging technique, is frequently used in non-invasive BCIs. Determining the subject-specific features is an important concern in enhancing the classification accuracy as well as reducing the complexity of fNIRS based BCI systems. In this study, the effectiveness of subject-specific feature selection on classification accuracy of fNIRS signals is examined.

METHODS

In order to determine the subject-specific optimal feature subsets, stepwise regression analysis based on sequential feature selection (SWR-SFS) and ReliefF methods were employed. Feature selection is applied on time-domain features of fNIRS signals such as mean, slope, peak, skewness and kurtosis values of signals. Linear discriminant analysis, k nearest neighborhood and support vector machines are employed to evaluate the performance of the selected feature subsets. The proposed techniques are validated on benchmark motor imagery (MI) and mental arithmetic (MA) based fNIRS datasets collected from 29 healthy subjects.

RESULTS

Both SWR-SFS and reliefF feature selection methods have significantly improved the classification accuracy. However, the best results (88.67% (HbR) and 86.43% (HbO) for MA dataset and 77.01% (HbR) and 71.32% (HbO) for MI dataset) were achieved using SWR-SFS while feature selection provided extremely high feature reduction rates (89.50% (HbR) and 93.99% (HbO) for MA dataset and 94.04% (HbR) and 97.73% (HbO) for MI dataset).

CONCLUSIONS

The results of the study indicate that employing feature selection improves both MA and MI-based fNIRS signals classification performance significantly.