A review on functional near-infrared spectroscopy and application in stroke rehabilitation

Effect of Sequential Repetitive Transcranial Magnetic Stimulation With Bilateral Arm Training on the Brain Effective Connectivity in Chronic Stroke

This study investigated the effects of combining repetitive transcranial magnetic stimulation (rTMS) with bilateral arm training (BAT) on effective brain connectivity in chronic stroke patients using functional near-infrared spectroscopy. Fifteen post-stroke patients and fifteen healthy individuals were enrolled. Coupling function analysis was performed to evaluate the effective connectivity inflow, outflow, and the dominant information flow (DIF) during standalone BAT and combined rTMS-BAT therapy. Significant task-related alterations were observed in the ipsilesional supplementary motor area and occipital lobe (OL) of stroke patients undergoing rTMS-BAT. During BAT, stroke patients exhibited more pronounced DIF from the OL to motor areas compared to healthy controls. Furthermore, in the rTMS-BAT condition, patients demonstrated enhanced DIF from the ipsilesional OL and contralesional prefrontal cortex to ipsilesional motor areas. These findings suggested a potential synergistic effect on cortical reorganization through the sequential combination of task-related training and TMS in chronic stroke patients, offering insights into rehabilitation strategies.

Deciphering brain activation during wrist movements: comparative fMRI and fNIRS analysis of active, passive, and imagery states

Understanding the complex activation patterns of brain regions during motor tasks is crucial. Integrated functional magnetic resonance imaging (fMRI) and functional near-infrared spectroscopy (fNIRS) offers advanced insights into how brain activity fluctuates with motor activities. This study explores neuronal activation patterns in the cerebral cortex during active, passive, and imagined wrist movements using these functional imaging techniques. Data were collected from 10 right-handed volunteers performing a motor task using fMRI and fNIRS. fMRI utilized a 3T scanner and a 20-channel head coil, while fNIRS recorded data with a 48-channel device at 765 nm and 855 nm. Analysis focused on key motor and sensory cortices using NIRS-SPM and SPM12, applying a significance threshold of p < 0.05 and a minimum cluster size of 10 voxels for group analysis. Super-threshold voxels were identified with FWE thresholding in SPM12. For activation map extraction we focused on the primary motor cortex, primary somatosensory cortex, somatosensory association cortex, premotor cortex, and supplementary motor cortex. Both fMRI and fNIRS detected activation in the primary motor cortex (M1). The primary somatosensory cortex was found to influence movement direction coding, with smaller activation sizes for upward movements. Combining fNIRS with fMRI provided clearer differentiation of brain activation patterns for wrist movements in various directions and conditions (p < 0.05). This study highlights variations in left motor cortex activity across different movement states. fNIRS proved effective in detecting brain function and showed strong correlation with fMRI results, suggesting it as a viable alternative for those unable to undergo fMRI.

fNIRS Classification of Adults with ADHD Enhanced by Feature Selection

Adult attention deficit hyperactivity disorder (ADHD), a prevalent psychiatric disorder, significantly impacts social, academic, and occupational functioning. However, it has been relatively less prioritized compared to childhood ADHD. This study employed a functional near-infrared spectroscopy (fNIRS) during verbal fluency tasks in conjunction with machine learning (ML) techniques to differentiate between healthy controls (N=75) and ADHD individuals (N=120). Efficient feature selection in high-dimensional fNIRS datasets is crucial for improving accuracy. To address this, we propose a hybrid feature selection method that combines a wrapper-based and embedded approach, termed Bayesian-Tuned Ridge RFECV (BTR-RFECV). The proposed method facilitated streamlined feature selection and hyperparameter tuning in high-dimensional data, thereby reducing the number of features while enhancing accuracy. HbO features from the combined frontal and temporal regions were key, with the models achieving precision (89.89%), recall (89.74%), F-1 score (89.66%), accuracy (89.74%), MCC (78.36%), and GDR (88.45%). The outcomes of this study highlight the promising potential of combining fNIRS with ML as diagnostic tools in clinical settings, offering a pathway to significantly reduce manual intervention.

Effectiveness of unilateral lower-limb exoskeleton robot on balance and gait recovery and neuroplasticity in patients with subacute stroke: a randomized controlled trial

BACKGROUND

Impaired balance and gait in stroke survivors are associated with decreased functional independence. This study aimed to evaluate the effectiveness of unilateral lower-limb exoskeleton robot-assisted overground gait training compared with conventional treatment and to explore the relationship between neuroplastic changes and motor function recovery in subacute stroke patients.

METHODS

In this randomized, single-blind clinical trial, 40 patients with subacute stroke were recruited and randomly assigned to either a robot-assisted training (RT) group or a conventional training (CT) group. All outcome measures were assessed at the enrollment baseline (T0), 2nd week (T1) and 4th week (T2) of the treatment. The primary outcome was the between-group difference in the change in the Berg balance scale (BBS) score from baseline to T2. The secondary measures included longitudinal changes in the Fugl-Meyer assessment of the lower limb (FMA-LE), modified Barthel index (mBI), functional ambulation category (FAC), and locomotion assessment with gait analysis. In addition, the cortical activation pattern related to robot-assisted training was measured before and after intervention via functional near-infrared spectroscopy.

RESULTS

A total of 30 patients with complete data were included in this study. Clinical outcomes improved after 4 weeks of training in both groups, with significantly better BBS (F = 6.341, p = 0.018, partial η2 = 0.185), FMA-LE (F = 5.979, p = 0.021, partial η2 = 0.176), FAC (F = 7.692, p = 0.010, partial η2 = 0.216), and mBI scores (F = 7.255, p = 0.042, partial η2 = 0.140) in the RT group than in the CT group. Both groups showed significant improvement in gait speed and stride cadence on the locomotion assessment. Only the RT group presented a significantly increased stride length (F = 4.913, p = 0.015, partial η2 = 0.267), support phase (F = 5.335, p = 0.011, partial η2 = 0.283), and toe-off angle (F = 3.829, p = 0.035, partial η2 = 0.228) on the affected side after the intervention. The RT group also showed increased neural activity response over the ipsilesional motor area and bilateral prefrontal cortex during robot-assisted weight-shift and gait training following 4 weeks of treatment.

CONCLUSIONS

Overground gait training with a unilateral exoskeleton robot showed improvements in balance and gait functions, resulting in better gait patterns and increased gait stability for stroke patients. The increased cortical response related to the ipsilesional motor areas and their related functional network is crucial in the rehabilitation of lower limb gait in post-stroke patients.

Brain-movement relationship during upper-limb functional movements in chronic post-stroke patients

BACKGROUND

Following a stroke, brain activation reorganisation, movement compensatory strategies, motor performance and their evolution through rehabilitation are matters of importance for clinicians. Two non-invasive neuroimaging methods allow for recording task-related brain activation: functional near-infrared spectroscopy (fNIRS) and electroencephalography (fEEG), respectively based on hemodynamic response and neuronal electrical activity. Their simultaneous measurement during movements could allow a better spatiotemporal mapping of brain activation, and when associated to kinematic parameters could unveil underlying mechanisms of functional upper limb (UL) recovery. This study aims to depict the motor cortical activity patterns using combined fNIRS-fEEG and their relationship to motor performance and strategies during UL functional tasks in chronic post-stroke patients.

METHODS

Twenty-one healthy old adults and 21 chronic post-stroke patients were recruited and completed two standardised functional tasks of the UL: a paced-reaching task where they had to reach a target in front of them and a circular steering task where they had to displace a target using a hand-held stylus, as fast as possible inside a circular track projected on a computer screen. The activity of the bilateral motor cortices and motor performance were recorded simultaneously utilizing a fNIRS-fEEG and kinematics platform.

RESULTS AND CONCLUSIONS

Kinematic analysis revealed that post-stroke patients performed worse in the circular steering task and used more trunk compensation in both tasks. Brain analysis of bilateral motor cortices revealed that stroke individuals over-activated during the paretic UL reaching task, which was associated with more trunk usage and a higher level of impairment (clinical scores). This work opens up avenues for using such combined methods to better track and understand brain-movement evolution through stroke rehabilitation.

Habituation of Brain Activity with Repetition in Color and Picture-Word Stroop Tests

As a widely used mental task for functional near-infrared spectroscopy (fNIRS), the original color-word Stroop task has the advantage of being difficult to habituate, but also the disadvantage of being difficult to understand, especially for children. While the introduction of derived Stroop tasks offers highly promising countermeasures, changes in brain activity during these tests have not been well tested. We investigated the degree of habituation between the original and a derived Stroop task by measuring brain activity to obtain a better fNIRS task design. Fourteen healthy adults participated in the study, and a 10-channel fNIRS device was used. A picture-word Stroop task with lower linguistic conflict than the original was conducted. The original and derived Stroop tests were repeated four times in a 1-week interval. We found that the original Stroop test did not show any significant changes in brain activity with repeated measures; however, brain activity decreased during the derived test. The differences in habituation between the original and derived tests may be due to the differences in the strength of the linguistic conflict. Our findings also highlight the need to consider the effects of habituation when using derived Stroop tasks in repeated measures.

Mental Fatigue Classification with High-Density Diffuse Optical Tomography: A Feasibility Study

High-Density Diffuse Optical Tomography (HD-DOT) presents as a promising tool for not only clinical use but also daily monitoring of mental states. This study employed wearable HD-DOT to evaluate mental fatigue, specifically examining the differences in functional near-infrared spectroscopy (fNIRS) data between states of low and high fatigue among healthy participants for data collection. Data processing involved filtering, channel selection, and dimensionality reduction through Uniform Manifold Approximation (UMAP) and Projection, followed by classification using Support Vector Machines (SVM). We developed two models to assess the accuracy and generalizability of our findings: one based on individually tailored models and another employing a leave-one-participant-out cross-validation strategy. We evaluated different kernel functions, resulting in various accuracy, F1 score, and Area Under the Curve (AUC) metrics. The study achieved an average accuracy of approximately 90% for participant-specific classifiers, underscoring the effectiveness of our approach to differentiate between low and high states of mental fatigue. Our analyses led to a robust model demonstrating high classification accuracy, proving its suitability and potential for real-time Brain-Computer Interface (BCI) applications.

Hemispheric Lateralization in Older Adults Who Habitually Play Darts: A Cross-Sectional Study Using Functional Near-Infrared Spectroscopy

Exercise training integrating physical and cognitive activities is gaining attention because of its potential benefits for brain health. This study focuses on exercise training using a dart game called Wellness Darts. Wellness Darts is a sport involving throwing darts and walking to pull them out of the board, memorizing the score, and subtracting this from the total score, thus requiring the simultaneous performance of two tasks: exercise and calculation. This is expected to maintain and improve cognitive function, and whether this continual darts training affects brain function is of great interest. Before conducting the longitudinal study revealing its effect on brain function, we aimed to cross-sectionally confirm the difference in hemispheric lateralization between expert and non-expert players. Functional near-infrared spectroscopy (fNIRS) was used to measure brain activity for three groups: an expert older group who practiced darts continually, a non-expert older control group, and a non-expert younger control group. Their brain activity patterns were quantified by the lateralization index (LI) and compared between groups. The results showed that the younger and the expert older groups had significantly higher LI values than the non-expert older group, and there was no difference between the expert older and the younger groups. Our results suggest that the Wellness Darts game possibly promotes hemispheric lateralization.

Unlocking the neural mechanisms of consumer loan evaluations: an fNIRS and ML-based consumer neuroscience study

Introduction

This study conducts a comprehensive exploration of the neurocognitive processes underlying consumer credit decision-making using cutting-edge techniques from neuroscience and machine learning (ML). Employing functional Near-Infrared Spectroscopy (fNIRS), the research examines the hemodynamic responses of participants while evaluating diverse credit offers.

Methods

The experimental phase of this study investigates the hemodynamic responses collected from 39 healthy participants with respect to different loan offers. This study integrates fNIRS data with advanced ML algorithms, specifically Extreme Gradient Boosting, CatBoost, Extra Tree Classifier, and Light Gradient Boosted Machine, to predict participants' credit decisions based on prefrontal cortex (PFC) activation patterns.

Results

Findings reveal distinctive PFC regions correlating with credit behaviors, including the dorsolateral prefrontal cortex (dlPFC) associated with strategic decision-making, the orbitofrontal cortex (OFC) linked to emotional valuations, and the ventromedial prefrontal cortex (vmPFC) reflecting brand integration and reward processing. Notably, the right dorsomedial prefrontal cortex (dmPFC) and the right vmPFC contribute to positive credit preferences.

Discussion

This interdisciplinary approach bridges neuroscience, machine learning and finance, offering unprecedented insights into the neural mechanisms guiding financial choices regarding different loan offers. The study's predictive model holds promise for refining financial services and illuminating human financial behavior within the burgeoning field of neurofinance. The work exemplifies the potential of interdisciplinary research to enhance our understanding of human financial decision-making.

fNIRS-EEG BCIs for Motor Rehabilitation: A Review

Motor impairment has a profound impact on a significant number of individuals, leading to a substantial demand for rehabilitation services. Through brain-computer interfaces (BCIs), people with severe motor disabilities could have improved communication with others and control appropriately designed robotic prosthetics, so as to (at least partially) restore their motor abilities. BCI plays a pivotal role in promoting smoother communication and interactions between individuals with motor impairments and others. Moreover, they enable the direct control of assistive devices through brain signals. In particular, their most significant potential lies in the realm of motor rehabilitation, where BCIs can offer real-time feedback to assist users in their training and continuously monitor the brain's state throughout the entire rehabilitation process. Hybridization of different brain-sensing modalities, especially functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG), has shown great potential in the creation of BCIs for rehabilitating the motor-impaired populations. EEG, as a well-established methodology, can be combined with fNIRS to compensate for the inherent disadvantages and achieve higher temporal and spatial resolution. This paper reviews the recent works in hybrid fNIRS-EEG BCIs for motor rehabilitation, emphasizing the methodologies that utilized motor imagery. An overview of the BCI system and its key components was introduced, followed by an introduction to various devices, strengths and weaknesses of different signal processing techniques, and applications in neuroscience and clinical contexts. The review concludes by discussing the possible challenges and opportunities for future development.

Premotor and Posterior Parietal Cortex Activity is Increased for Slow, as well as Fast Walking Poststroke: An fNIRS Study

Methods

Twenty individuals in the chronic stage of stroke walked: (1) at their normal pace, (2) slower than normal, and (3) as fast as possible. Functional near-infrared spectroscopy was used to assess bilateral prefrontal, premotor, sensorimotor, and posterior parietal cortices during walking.

Results

No significant differences in laterality were observed between walking speeds. The ipsilesional prefrontal cortex was overall more active than the contralesional prefrontal cortex. Premotor and posterior parietal cortex activity were larger during slow and fast walking compared to normal-paced walking with no differences between slow and fast walking. Greater increases in brain activation in the ipsilesional prefrontal cortex during fast compared to normal-paced walking related to greater gait speed modulation.

Conclusions

Brain activation is not linearly related to gait speed. Ipsilesional prefrontal cortex, bilateral premotor, and bilateral posterior parietal cortices are important areas for gait speed modulation and could be an area of interest for neurostimulation.

Why people engage in corrupt collaboration: an observation at the multi-brain level

Recent studies suggest that corrupt collaboration (i.e. acquiring private benefits with joint immoral acts) represents a dilemma between the honesty and reciprocity norms. In this study, we asked pairs of participants (labeled as A and B) to individually toss a coin and report their outcomes; their collective benefit could be maximized by dishonestly reporting (a corrupt behavior). As expected, the likelihood of corrupt behavior was high; this probability was negatively correlated with player A's moral judgment ability but positively correlated with player B's empathic concern (EC). Functional near-infrared spectroscopy data revealed that the brain-to-brain synchronization in the right dorsolateral prefrontal cortex was associated with fewer corrupt behaviors, and that it mediated the relationship between player A's moral judgment ability and corrupt collaboration. Meanwhile, the right temporal-parietal junction synchronization was associated with more corrupt behaviors, and that it mediated the relationship between player B's EC and corrupt collaboration. The roles of these 2 regions are interpreted according to the influence of the honesty and reciprocity norms on corrupt collaboration. In our opinion, these findings provide insight into the underlying mechanisms and modulating factors of corrupt collaboration.

Alterations in learning-related cortical activation and functional connectivity by high-definition transcranial direct current stimulation after stroke: an fNIRS study

Introduction

Motor learning is a key component of stroke neurorehabilitation. High-definition transcranial direct current stimulation (HD-tDCS) was recently developed as a tDCS technique that increases the accuracy of current delivery to the brain using arrays of small electrodes. The purpose of this study was to investigate whether HD-tDCS alters learning-related cortical activation and functional connectivity in stroke patients using functional near-infrared spectroscopy (fNIRS).

Methods

Using a sham-controlled crossover study design, 16 chronic stroke patients were randomly assigned to one of two intervention conditions. Both groups performed the sequential finger tapping task (SFTT) on five consecutive days, either with (a) real HD-tDCS or (b) with sham HD-tDCS. HD-tDCS (1 mA for 20 min, 4 × 1) was administered to C3 or C4 (according to lesion side). fNIRS signals were measured during the SFTT with the affected hand before (baseline) and after each intervention using fNIRS measurement system. Cortical activation and functional connectivity of NIRS signals were analyzed using a statistical parametric mapping open-source software package (NIRS-SPM), OptoNet II^®.

Results

In the real HD-tDCS condition, oxyHb concentration increased significantly in the ipsilesional primary motor cortex (M1). Connectivity between the ipsilesional M1 and the premotor cortex (PM) was noticeably strengthened after real HD-tDCS compared with baseline. Motor performance also significantly improved, as shown in response time during the SFTT. In the sham HD-tDCS condition, functional connectivity between contralesional M1 and sensory cortex was enhanced compared with baseline. There was tendency toward improvement in SFTT response time, but without significance.

Discussion

The results of this study indicated that HD-tDCS could modulate learning-related cortical activity and functional connectivity within motor networks to enhance motor learning performance. HD-tDCS can be used as an additional tool for enhancing motor learning during hand rehabilitation for chronic stroke patients.

The analysis of brain functional connectivity of post-stroke cognitive impairment patients: an fNIRS study

Background

Post-stroke cognitive impairment (PSCI) is a considerable risk factor for developing dementia and reoccurrence of stroke. Understanding the neural mechanisms of cognitive impairment after stroke can facilitate early identification and intervention.

Objectives

Using functional near-infrared spectroscopy (fNRIS), the present study aimed to examine whether resting-state functional connectivity (FC) of brain networks differs in patients with PSCI, patients with Non-PSCI (NPSCI), and healthy controls (HCs), and whether these features could be used for clinical diagnosis of PSCI.

Methods

The present study recruited 16 HCs and 32 post-stroke patients. Based on the diagnostic criteria of PSCI, post-stroke patients were divided to the PSCI or NPSCI group. All participants underwent a 6-min resting-state fNRIS test to measure the hemodynamic responses from regions of interests (ROIs) that were primarily distributed in the prefrontal, somatosensory, and motor cortices.

Results

The results showed that, when compared to the HC group, the PSCI group exhibited significantly decreased interhemispheric FC and intra-right hemispheric FC. ROI analyses showed significantly decreased FC among the regions of somatosensory cortex, dorsolateral prefrontal cortex, and medial prefrontal cortex for the PSCI group than for the HC group. However, no significant difference was found in the FC between the PSCI and the NPSCI groups.

Conclusion

Our findings provide evidence for compromised interhemispheric and intra-right hemispheric functional connectivity in patients with PSCI, suggesting that fNIRS is a promising approach to investigate the effects of stroke on functional connectivity of brain networks.

Cortical response induced by task-oriented training of the upper limb in subacute stroke patients as assessed by functional near-infrared spectroscopy

Despite the popularity of task-oriented training for stroke, the cortical reorganization associated with this type of therapy remains to be fully elucidated due to the lack of dynamic assessment tools. A good tolerance for motion artifacts makes functional near-infrared spectroscopy (fNIRS) suitable for investigating task-induced cortical responses in stroke patients. Here, patients were randomly assigned to receive task oriented (n = 25) or cyclic rotary training (n = 25) with simultaneous cortical activation and effective connectivity network analysis between prefrontal and motor cortices (PFC/MC). Compared with cyclic rotary training, task-oriented training induced significantly increased activation in both hemispheres and enhanced influence of PFC on MC. In addition, significantly decreased activation lateralization and increased betweenness centrality of the contralesional MC suggested widespread involvement of the contralesional hemisphere during task-oriented training. This study verifies the feasibility of fNIRS combined with motor paradigms for assessing neural responses associated with stroke rehabilitation in real time.

Altered frontoparietal activity in acoustic startle priming tasks during reticulospinal tract facilitation: An fNIRS study

Background

Because it is one of the important pathways for promoting motor recovery after cortical injury, the function of the reticulospinal tract (RST) has received increasing attention in recent years. However, the central regulatory mechanism of RST facilitation and reduction of apparent response time is not well understood.

Objectives

To explore the potential role of RST facilitation in the acoustic startle priming (ASP) paradigm and observe the cortical changes induced by ASP reaching tasks.

Methods

Twenty healthy participants were included in this study. The reaching tasks were performed with their left and right hands. Participants were instructed to get ready after the warning cue and complete the reach as soon as they heard the Go cue. Half of the testing trials were set as control trials with an 80-dB Go cue. The other half of the trials had the Go cue replaced with 114-dB white noise to evoke the StartleReact effect, inducing reticulospinal tract facilitation. The response of the bilateral sternocleidomastoid muscle (SCM) and the anterior deltoid was recorded via surface electromyography. Startle trials were labeled as exhibiting a positive or negative StartleReact effect, according to whether the SCM was activated early (30-130 ms after the Go cue) or late, respectively. Functional near-infrared spectroscopy was used to synchronously record the oxyhemoglobin and deoxyhemoglobin fluctuations in bilateral motor-related cortical regions. The β values representing cortical responses were estimated via the statistical parametric mapping technique and included in the final analyses.

Results

Separate analyses of data from movements of the left or right side revealed significant activation of the right dorsolateral prefrontal cortex during RST facilitation. Moreover, left frontopolar cortex activation was greater in positive startle trials than in control or negative startle trials during left-side movements. Furthermore, decreased activity of the ipsilateral primary motor cortex in positive startle trials during ASP reaching tasks was observed.

Conclusion

The right dorsolateral prefrontal cortex and the frontoparietal network to which it belongs may be the regulatory center for the StartleReact effect and RST facilitation. In addition, the ascending reticular activating system may be involved. The decreased activity of the ipsilateral primary motor cortex suggests enhanced inhibition of the non-moving side during the ASP reaching task. These findings provide further insight into the SE and into RST facilitation.

Task-Related Hemodynamic Changes Induced by High-Definition Transcranial Direct Current Stimulation in Chronic Stroke Patients: An Uncontrolled Pilot fNIRS Study

High-definition transcranial direct current stimulation (HD-tDCS) has recently been proposed as a tDCS approach that can be used on a specific cortical region without causing undesirable stimulation effects. In this uncontrolled pilot study, the cortical hemodynamic changes caused by HD-tDCS applied over the ipsilesional motor cortical area were investigated in 26 stroke patients. HD-tDCS using one anodal and four cathodal electrodes at 1 mA was administered for 20 min to C3 or C4 in four daily sessions. Cortical activation was measured as changes in oxyhemoglobin (oxyHb) concentration, as found using a functional near-infrared spectroscopy (fNIRS) system during the finger tapping task (FTT) with the affected hand before and after HD-tDCS. Motor-evoked potential and upper extremity functions were also measured before (T0) and after the intervention (T1). A group statistical parametric mapping analysis showed that the oxyHb concentration increased during the FTT in both the affected and unaffected hemispheres before HD-tDCS. After HD-tDCS, the oxyHb concentration increased only in the affected hemisphere. In a time series analysis, the mean and integral oxyHb concentration during the FTT showed a noticeable decrease in the channel closest to the hand motor hotspot (hMHS) in the affected hemisphere after HD-tDCS compared with before HD-tDCS, in accordance with an improvement in the function of the affected upper extremity. These results suggest that HD-tDCS might be helpful to rebalance interhemispheric cortical activity and to reduce the hemodynamic burden on the affected hemisphere during hand motor tasks. Noticeable changes in the area adjacent to the affected hMHS may imply that personalized HD-tDCS electrode placement is needed to match each patient's individual hMHS location.

Comparative study on the effect of color spaces and color formats on heart rate measurement using the imaging photoplethysmography (IPPG) method

BACKGROUND

The imaging photoplethysmography (IPPG) technology has been demonstrated to be an effective method for heart rate (HR) monitoring. However, some interference caused by the ambient illumination variation and facial motion severely influences the accuracy of the HR measurement. Some color spaces and color formats are assumed to reduce the interference, and enhance the accuracy of HR estimation.

OBJECTIVE

The aim is to identify the optimal color space and format for IPPG based HR measurement.

METHODS

Six color spaces and 3 color formats are compared in this study, based on an IPPG based HR measurement system. 424 pieces of videos captured by the system are used for the selection of the optimal color channel and color space; while 10 pieces of videos are for the identification of the optimal color format.

RESULTS

The results shows that the green channel of RGB space is the optimal color channel, and RGB is the optimal color space, in respect of the mean squared error of HR estimation. BayerBG 8bit is found to be the optimal color format for video recording, which can significantly reduce the HR estimation error.

CONCLUSIONS

BayerBG 8bit color format for video recording, and RGB color space for video analysis is suggested for the IPPG based HR measurement system. The suitable configuration of color space and format could enhance the accuracy of HR measurement.