Measurement Of Physiological Signals Research Articles

Understanding the neural principles of interacting multiple internal states

In dynamic ethological environments, animals are constantly confronted with conflicting choices driven by interacting internal motivations. This intricate dance between internal states prompts the question: How do these states interact to determine the optimal innate behavior at any given moment? Specifically, when hungry animals encounter food and potential intruders, how do they navigate between feeding, attack, and mating behaviors? Despite extensive neuroscience research on instinctive behaviors, our understanding of the mechanisms governing adaptive behavioral decisions in the face of competing internal signals remains limited. This research aims to shed light on the complex interplay of internal physiological homeostasis in modulating defensive, aggressive, and sexual behaviors. Our focus lies on exploring the intricate network between the body and the brain to unravel the most adaptive responses within a concise time window. Currently, our investigations center around the hypothalamus, a crucial region known for representing emotional states upon encountering conspecifics or predators. Intriguingly, this same hypothalamic nucleus plays a pivotal role in regulating homeostatic metabolic states, including glucose homeostasis. Here, I will share the observation from innate behaviors of animals and the associated neural activity patterns in the hypothalamus. We will explore the current understanding of how these behaviors are modulated by internal physiological states and discuss the future directions of our research. Additionally, we aim to highlight the significance of combining the measurement of physiological signals to comprehensively address how multiple internal states interact with each other. By unraveling the complexities of these interactions, our research contributes to a deeper comprehension of the adaptive decision-making processes that animals employ in the face of conflicting choices. Helen Hay Whitney Foundation, HHMI. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

An effective cross-scenario remote heart rate estimation network based on global-local information and video transformer.

Remote photoplethysmography (rPPG) technology is a non-contact physiological signal measurement method, characterized by non-invasiveness and ease of use. It has broad application potential in medical health, human factors engineering, and other fields. However, current rPPG technology is highly susceptible to variations in lighting conditions, head pose changes, and partial occlusions, posing significant challenges for its widespread application. In order to improve the accuracy of remote heart rate estimation and enhance model generalization, we propose PulseFormer, a dual-path network based on transformer. By integrating local and global information and utilizing fast and slow paths, PulseFormer effectively captures the temporal variations of key regions and spatial variations of the global area, facilitating the extraction of rPPG feature information while mitigating the impact of background noise variations. Heart rate estimation results on the popular rPPG dataset show that PulseFormer achieves state-of-the-art performance on public datasets. Additionally, we establish a dataset containing facial expressions and synchronized physiological signals in driving scenarios and test the pre-trained model from the public dataset on this collected dataset. The results indicate that PulseFormer exhibits strong generalization capabilities across different data distributions in cross-scenario settings. Therefore, this model is applicable for heart rate estimation of individuals in various scenarios.

Facile Fabrication of "Tacky", Stretchable, and Aligned Carbon Nanotube Sheet-Based Electronics for On-Skin Health Monitoring.

Point-of-care monitoring of physiological signals such as electrocardiogram, electromyogram, and electroencephalogram is essential for prompt disease diagnosis and quick treatment, which can be realized through advanced skin-worn electronics. However, it is still challenging to design an intimate and nonrestrictive skin-contact device for physiological measurements with high fidelity and artifact tolerance. This research presents a facile method using a "tacky" surface to produce a tight interface between the ACNT skin-like electronic and the skin. The method provides the skin-worn electronic with a stretchability of up to 70% strain, greater than that of most common epidermal electrodes. Low-density ACNT bundles facilitate the infiltration of adhesive and improve the conformal contact between the ACNT sheet and the skin, while dense ACNT bundles lessen this effect. The stretchability and conformal contact allow the ACNT sheet-based electronics to create a tight interface with the skin, which enables the high-fidelity measurement of physiological signals (the Pearson's coefficient of 0.98) and tolerance for motion artifacts. In addition, our method allows the use of degradable substrates to enable reusability and degradability of the electronics based on ACNT sheets, integrating "green" properties into on-skin electronics.

Low-cost and portable physiological signal monitor using PhysRate model

In this work, we propose a lightweight non-contact physiological signal monitor. The proposed system is equipped with the modified DeepPhys model and the rate estimation model PhysRate that enable non-contact physiological signal measurements without markers using the low-cost color camera. We introduce the constraint term guided by scene information into the original DeepPhys model to mitigate the overfitting problem of the original DeepPhys model. The results of validation experiments in ICU observation ward show that the modified DeepPhys model can effectively counteract the overfitting problem of the original DeepPhys model. The experimental results of PhysRate indicate that it exhibits certain performance improvements compared to other rate prediction models. In terms of system integration, the proposed physiological signal detection monitor includes Raspberry Pi 4B, RGB camera, 4G module, mobile power, and mobile terminal. With the help of above five modules, the system can analyze the captured signals at the edge and then transmit the processed data to the cloud via 4G network. The edge side is equipped with physiological signal detection model and the other data processing algorithms. On mobile devices, the users can view the relevant data through the developed Mini App. The PhysRate model code is available at https://github.com/ShawnTan86/PhysRate.

Patchable Transparent Standalone Piezoelectric P(VDF-TrFE) Film for Radial Artery Pulse Detection

Wearable or patchable biosensors have attracted tremendous attention due to their continuous health-monitoring capabilities. In particular, self-powered passive biosensors based on a piezoelectric nanogenerator (PENG) have demonstrated measurements of physiological signals from which cardiovascular information can be analyzed such as heart rate and blood pressure. However, challenges still remain with regard to both material and device aspects. For the effective and accurate measurement of extremely weak physiological signals, various methods have been introduced, including employment of inorganic lead-based piezoelectric materials and design of a complex material or device structure. In spite of their effectiveness in enhancing the piezoelectric output response, the introduced methods brought concomitant issues, such as toxicity and complexity. We present unique methods to produce a transparent standalone piezoelectric polymer film which can be directly transferred to any surface such as the human skin. Through a room temperature solvent vapor annealing process, we further enhance the crystallinity and a portion of the ferroelectric β-phase of the transparent standalone polymer film, resulting in an improved piezoelectric output response. Based on these two new methods introduced, we demonstrate a simple sandwich-structured, transparent, and patchable biosensor based on PENG for radial artery detection with significantly reduced complex manufacturing processes, providing great practical value.

GraphPhys: Facial video-based physiological measurement with graph neural network

Non-contact facial video-based physiological signal measurement using remote photoplethysmography (rPPG) has shown great potential in many applications (e.g. telemedicine, driver fatigue detection, sleep detection) and has achieved reliable results in the controlled environment. With the development of deep learning, more and more methods have been proposed. However, these methods were usually implemented based on model variations that have achieved better results in computer vision tasks, and finally chose average pooling layer or one-dimensional convolution layer directly reduce dimension. Such these operations ignored the fact that the physiological signals of one individual are consistent in different facial regions. In this paper, we propose a novel remote photoplethysmography framework that utilizes graph neural network to extract physiological signal, named GraphPhys. And we design the Average Relative GraphConv based on the fact that the physiological signals of one individual are always consistent in different facial regions for the remote physiological signal measurement task. We apply GraphPhys to two different architectures for rPPG methods, and the methods based on GraphPhys significantly outperform the original methods on the VIPL-HR (Niu et al., 2019) dataset. To the best of our knowledge, this is the first work to utilize graph neural network to aggregate physiological signals. The code is available at https://github.com/Xiong-JiaHao/GraphPhys.

Wearable devices and Machine Learning algorithms to assess indoor thermal sensation: metrological analysis

Personal comfort modeling is considered the most promising solution for indoor thermal comfort management in buildings. The use of wearable sensors is investigated for the real-time measurement of physiological signals to train comfort models for buildings monitoring and control. To achieve the required reliability, different uncertainty sources should be considered and weighted in the measurement results evaluation. This study presents an example of personal comfort model (PCM) development based on wearable sensors (i.e., Empatica E4 smartband and MUSE headband) acquiring multimodal signals (i.e., photoplethysmographic – PPG, electrodermal activity – EDA, skin temperature – SKT, and electroencephalographic – EEG ones), together with a metrological characterization of the modeling procedure. Starting from the data collected within an experimental campaign on 76 subjects, different Machine Learning (ML) algorithms were exploited to create comfort models capable of predicting the human thermal sensation (TS). The most accurate model was considered to investigate the impact of sensors uncertainty through a Monte Carlo simulation. Results showed that the Random Forest model is the best performing one (accuracy: 0.86). Monte Carlo simulation method proved that the model is very robust towards measurement uncertainties of input features (expanded uncertainty of the model accuracy: ± 0.04, k = 2). This confirms the possibility to derive the subject’s TS exploiting only physiological signals; measurement uncertainty is influenced mostly by PPG and EDA signals. This kind of investigation could lead to the development of PCMs, exploitable within control systems to optimize subjects’ well-being and building energy efficiency.

Highly Conductive and Sensitive Wearable Strain Sensors with Metal/Nanoparticle Double Layer for Noninterference Voice Detection.

Human voice recognition via skin-attachable devices has significant potential for gathering important physiological information from acoustic data without background noise interference. In this study, a highly sensitive and conductive wearable crack-based strain sensor was developed for voice-recognition systems. The sensor was fabricated using a double-layer structure of Ag nanoparticles (NPs) and Ag metal on a biocompatible polydimethylsiloxane substrate. The top metal layer acts as a conducting active layer, whereas the bottom Ag NP layer induces channel cracks in the upper layer, effectively hindering current flow. Subsequently, the double-layer film exhibits a low electrical resistance value (<5 × 10-5 Ω cm), ultrahigh sensitivity (gauge factor = 1870), and a fast response/recovery time (252/168 μs). A sound wave was detected at a high frequency of 15 kHz with a signal-to-noise ratio (SNR) over 40 dB. The sensor exhibited excellent anti-interference characteristics and effectively differentiated between different voice qualities (modal, pressed, and breathy), with a systematic analysis revealing successful detection of the laryngeal state and glottal source. This ultrasensitive wearable sensor has potential applications in various physiological signal measurement methods, personalized healthcare systems, and ubiquitous computing.

Recent Progress of Optical Imaging Approaches for Noncontact Physiological Signal Measurement: A Review

In recent years, optical imaging techniques have gained wide recognition for the measurement of vital signals, such as heart rate, respiratory rate, oxygen saturation, and blood pressure, which are crucial indicators for evaluating human health conditions in clinical examinations. There is a wide range of optical imaging methods for remote physiological signal monitoring, including RGB imaging, thermal imaging, hyperspectral imaging, depth imaging, and multimodal imaging, which provide spatial information compared to other noncontact measurement approaches, thereby allowing extensive applications in this area. In this survey, some fundamental knowledge about optical imaging methods for vital signal measurement is reviewed, including principles of various optical imaging techniques, processing methods for data analysis, discussion on advantages and disadvantages, application summary, and future prospects. This is a comprehensive overview of the noncontact physiological signal measurement of optical imaging approaches.

Methods for the metrological characterization of wearable devices for the measurement of physiological signals: state of the art and future challenges

Wearable devices are rapidly spreading in many different application fields and with diverse measurement accuracy targets. However, data on their metrological characterization are very often missing or obtained with non-standardized methods, hence resulting in barely comparable results. The aim of this review paper is to discuss the existing methods for the metrological characterization of wearable sensors exploited for the measurement of physiological signals, highlighting the room for research still available in this field. Furthermore, as a case study, the authors report a customized method they have tuned for the validation of wireless electrocardiographic monitors. The literature provides a plethora of test/validation procedures, but there is no shared consensus on test parameters (e.g. test population size, test protocol, output parameters of validation procedure, etc.); on the other hand, manufacturers rarely provide measurement accuracy values and, even when they do, the test protocol and data processing pipelines are generally not disclosed. Given the increasing interest and demand of wearable sensors also for medical and diagnostic purposes, the metrological performance of such devices should be always considered, to be able to adequately interpret the results and always deliver them associated with the related measurement accuracy.•The sensor metrological performance should be always properly considered.•There are no standard methods for wearable sensors metrological characterization.•It is important to define rigorous test protocols, easily tunable for specific target applications.

NEUROMARKETING AS A TOOL FOR THE ANALYSIS OF VISUAL COMFORT OF ARTIFICIAL LIGHTING IN PUBLIC SPACES

Currently, most large cities in developed economies are developing and implementing master plans for night lighting. A comparative analysis of 14 master plans unequivocally indicates that the visual comfort of night lighting of open public spaces is one of the most important functions of the organization of the urban environment. However, it should be noted that the understanding of visual comfort in all analyzed works has a very subjective nature and is based on the personal experience and aesthetic preferences of architects. The author has developed a new approach to the analysis of the visual comfort of night lighting. This approach is based on presenting the perception of the visible environment as a step-by-step process of visual perception of the environment by a person. There are three stages of perception in total, namely: psychophysiological level (stage) of perception, emotional-aesthetic and visual-artistic. This article examines the prospects of using the neuromarketing paradigm as a tool for assessing the emotional (emotional-aesthetic level of perception) reactions of recipients to the night lighting of open public spaces. Based on the above, it became possible to determine the emotional and aesthetic limits of the visual comfort of the architectural environment, which in the study are reduced to three main functions of visual comfort: Compensatory (protective) - helps a person in the process of perceiving the aesthetics of the environment to restore internal emotional harmony; Integrating (that unites) - creates a feeling of harmony, integrity and involvement in the environment in human perception; Hedonistic - forms satisfaction from the aesthetic qualities of the architectural environment. Each of the listed functions of comfortable perception of artificial lighting of open public spaces is evaluated according to two groups of characteristics: color and brightness of light. Measurement of physiological and neural signals helps in the creation of creative design, development of emotionally attractive lighting projects, detection of depressive zones and in other areas of architectural activity. Brain scans that measure its neural activity and tracking physiological responses (eg eye saccades, pressure changes, breathing rate, etc.) are the most common measurement methods. Neuromarketing tools can become the basis for identifying quantitative parameters of emotional reactions of target population groups to the visual qualities of lighting in public spaces. Keywords: visual comfort, function of visual comfort, neuromarketing, architecture, emotional and aesthetic level of perception.

Continuous physiological signal measurement over 24-hour periods to assess the impact of work-related stress and workplace violence

Work-related stress has long been recognized as an essential factor affecting employees' health and wellbeing. Repeated exposure to acute occupational stressors puts workers at high risk for depression, obesity, hypertension, and early death. Assessment of the effects of acute stress on workers' wellbeing usually relies on subjective self-reports, questionnaires, or measuring biometric and biochemical markers in long-cycle time intervals. This study aimed to develop and validate the use of a multiparameter wearable armband for continuous non-invasive monitoring of physiological states. Two worker populations were monitored 24 h/day: six loggers for one day and six ICU nurses working 12-hr shifts for one week. Stress responses in nurses were highly correlated with changes in heart rate variability (HRV) and pulse transit time (PTT). A rise in the low-to high-frequency (LF/LH) ratio in HRV was also coincident with stress responses. HRV on workdays decreased compared to non-work days, and PTT also exhibited a persistent decrease reflecting increased blood pressure. Compared to loggers, nurses were involved in high-intensity work activities 45% more often but were less active on non-work days. The wearable technology was well accepted by all worker participants and yielded high signal quality, critical factors for long-term non-invasive occupational health monitoring.

Potential Predictors for Cognitive Decline in Vascular Dementia: A Machine Learning Analysis

Vascular dementia (VD) is a cognitive impairment typical of advanced age with vascular etiology. It results from several vascular micro-accidents involving brain vessels carrying less oxygen and nutrients than it needs. This being a degenerative disease, the diagnosis often arrives too late, when the brain tissue is already damaged. Thus, prevention is the best solution to avoid irreversible cognitive impairment in patients with specific risk factors. Using the machine learning (ML) approach, our group evaluated Mini-Mental State Examination (MMSE) changes in patients affected by Alzheimer’s disease by considering different clinical parameters. We decided to apply a similar ML scheme to VD due to the consistent data obtained from the first work, including the assessment of various ML models (LASSO, RIDGE, Elastic Net, CART, Random Forest) for the outcome prediction (i.e., the MMSE modification throughout time). MMSE at recruitment, folate, MCV, PTH, creatinine, vitamin B12, TSH, and hemoglobinwere the best predictive parameters individuated by the best ML model: Random Forest. ML results can be useful inidentify predictive biomarkers for cognitive worsening in VD early and also for focusing on necessary examinations at the first visits to draw the most predictive features, saving time and money and reducethe burden on the patients themselves. Such results should be integrated with brain imaging, physiological signal measurements, and sensory patterns, particularly forthose senses already demonstrated to have a significant link with neurodegeneration. Adjusting compound deficit by administering nutraceuticals could support treatment effectiveness and lead to a better quality of life for patients, families, and caregivers, with a consistent impact on the national health systems load.

Novel Design of a Multimodal Technology-Based Smart Stethoscope for Personal Cardiovascular Health Monitoring

Heart sounds and heart rate (pulse) are the most common physiological signals used in the diagnosis of cardiovascular diseases. Measuring these signals using a device and analyzing their interrelationships simultaneously can improve the accuracy of existing methods and propose new approaches for the diagnosis of cardiovascular diseases. In this study, we have presented a novel smart stethoscope based on multimodal physiological signal measurement technology for personal cardiovascular health monitoring. The proposed device is designed in the shape of a compact personal computer mouse for easy grasping and attachment to the surface of the chest using only one hand. A digital microphone and photoplehysmogram sensor are installed on the bottom and top surfaces of the device, respectively, to measure heart sound and pulse from the user’s chest and finger simultaneously. In addition, a high-performance Bluetooth Low Energy System-on-Chip ARM microprocessor is used for pre-processing of measured data and communication with the smartphone. The prototype is assembled on a manufactured printed circuit board and 3D-printed shell to conduct an in vivo experiment to test the performance of physiological signal measurement and usability by observing users’ muscle fatigue variation.

Drowsiness Detection System Based on PERCLOS and Facial Physiological Signal.

Accidents caused by fatigue occur frequently, and numerous scholars have devoted tremendous efforts to investigate methods to reduce accidents caused by fatigued driving. Accordingly, the assessment of the spirit status of the driver through the eyes blinking frequency and the measurement of physiological signals have emerged as effective methods. In this study, a drowsiness detection system is proposed to combine the detection of LF/HF ratio from heart rate variability (HRV) of photoplethysmographic imaging (PPGI) and percentage of eyelid closure over the pupil over time (PERCLOS), and to utilize the advantages of both methods to improve the accuracy and robustness of drowsiness detection. The proposed algorithm performs three functions, including LF/HF ratio from HRV status judgment, eye state detection, and drowsiness judgment. In addition, this study utilized a near-infrared webcam to obtain a facial image to achieve non-contact measurement, alleviate the inconvenience of using a contact wearable device, and for use in a dark environment. Furthermore, we selected the appropriate RGB channel under different light sources to obtain LF/HF ratio from HRV of PPGI. The main drowsiness judgment basis of the proposed drowsiness detection system is the use of algorithm to obtain sympathetic/parasympathetic nervous balance index and percentage of eyelid closure. In the experiment, there are 10 awake samples and 30 sleepy samples. The sensitivity is 88.9%, the specificity is 93.5%, the positive predictive value is 80%, and the system accuracy is 92.5%. In addition, an electroencephalography signal was used as a contrast to validate the reliability of the proposed method.

Self-Supervised Representation Learning Framework for Remote Physiological Measurement Using Spatiotemporal Augmentation Loss

Recent advances in supervised deep learning methods are enabling remote measurements of photoplethysmography-based physiological signals using facial videos. The performance of these supervised methods, however, are dependent on the availability of large labelled data. Contrastive learning as a self-supervised method has recently achieved state-of-the-art performances in learning representative data features by maximising mutual information between different augmented views. However, existing data augmentation techniques for contrastive learning are not designed to learn physiological signals from videos and often fail when there are complicated noise and subtle and periodic colour/shape variations between video frames. To address these problems, we present a novel self-supervised spatiotemporal learning framework for remote physiological signal representation learning, where there is a lack of labelled training data. Firstly, we propose a landmark-based spatial augmentation that splits the face into several informative parts based on the Shafer’s dichromatic reflection model to characterise subtle skin colour fluctuations. We also formulate a sparsity-based temporal augmentation exploiting Nyquist–Shannon sampling theorem to effectively capture periodic temporal changes by modelling physiological signal features. Furthermore, we introduce a constrained spatiotemporal loss which generates pseudo-labels for augmented video clips. It is used to regulate the training process and handle complicated noise. We evaluated our framework on 3 public datasets and demonstrated superior performances than other self-supervised methods and achieved competitive accuracy compared to the state-of-the-art supervised methods. Code is available at https://github.com/Dylan-H-Wang/SLF-RPM.

Estimation of blood pressure waveform from facial video using a deep U-shaped network and the wavelet representation of imaging photoplethysmographic signals

Background:The remote measurement of physiological signals from video has gained a particular attention over the last past years. Estimating cardiovascular parameters like oxygen saturation and arterial blood pressure (BP) is covered by a limited volume of studies and remain a very challenging issue. Recent attempts demonstrated that BP can be estimated from facial video but under very controlled scenarios or with moderate performances. The data used in these works have not been publicly released or were gathered in a clinical setting. Methods:We, in contrast, propose a framework for estimating BP from publicly available data in order to allow replication and to facilitate fair comparison. We developed and trained a deep U-shaped neural network to recover the blood pressure waveform from its imaging photoplethysmographic (iPPG) signal counterpart. The model predicts the continuous wavelet transform (CWT) representation of a BP signal from the CWT of an iPPG signal. Inverse CWT transform is ultimately computed to recover the BP time series. Results:The proposed framework has been evaluated on 57 participants using international standards developed by the AAMI and the BHS. Results exhibit close agreement with ground truth BP values. The method satisfies all standards in the estimation of mean and diastolic BP (grade A) and nearly all standards in the estimation of systolic BP (grade B). Conclusions:This is, to the best of our knowledge, the first demonstration of a deep learning-oriented framework that manages to predict the continuous blood pressure waveform from facial video analysis. Codes developed during the study are publicly available (https://github.com/frederic-bousefsaf/ippg2bp).

Flexible Non-contact Electrodes for Wearable Biosensors System on Electrocardiogram Monitoring in Motion.

Electrocardiogram (ECG) is a critical physiological indicator that contains abundant information about human heart activities. However, it is a kind of weak low-frequency signal, which is easy to be interfered by various noises. Therefore, wearable biosensors (WBS) technique is introduced to overcome this challenge. A flexible non-contact electrode is proposed for wearable biosensors (WBS) system, which is made up of flexible printed circuits materials, and can monitor the ECG signals during exercise for a long time. It uses the principle of capacitive coupling to obtain high-quality signals, and reduces the impact of external noise through active shielding; The results showed that the proposed non-contact electrode was equivalent to a medical wet electrode. The correlation coefficient was as high as 99.70 ± 0.30% when the subject was resting, while it was as high as 97.53 ± 1.80% during exercise. High-quality ECG could still be collected at subjects walking at 7 km/h. This study suggested that the proposed flexible non-contact electrode would be a potential tool for wearable biosensors for medical application on long-term monitoring of patients’ health and provide athletes with physiological signal measurements.

Psychological and Emotional Recognition of Preschool Children Using Artificial Neural Network.

The artificial neural network (ANN) is employed to study children’s psychological emotion recognition to fully reflect the psychological status of preschool children and promote the healthy growth of preschool children. Specifically, the ANN model is used to construct the human physiological signal measurement platform and emotion recognition platform to measure the human physiological signals in different psychological and emotional states. Finally, the parameter values are analyzed on the emotion recognition platform to identify the children’s psychological and emotional states accurately. The experimental results demonstrate that the recognition ability of children aged 4–6 to recognize the three basic emotions of happiness, calm, and fear increases with age. Besides, there are significant age differences in children’s recognition of happiness, calm, and fear. In addition, the effect of 4-year-old children on the theory of mind tasks is less than that of 5- to 6-year-old children, which may be related to more complex cognitive processes. Preschool children are experiencing a stage of rapid emotional development. If children cannot be guided to reasonably identify and deal with emotions at this stage, their education level and social ability development will be significantly affected. Therefore, this study has significant reference value for preschool children’s emotional recognition and guidance and can promote children’s emotional processing and mental health.

Motion Artifacts Correction From EEG and fNIRS Signals Using Novel Multiresolution Analysis

Physiological signal measurement and processing are increasingly becoming popular in the ambulatory setting as the hospital-centric treatment is moving towards wearable and ubiquitous monitoring. Most of the physiological signals are highly susceptible to various types of noises, especially movement artifacts. The electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) signals are no exception to motion artifacts, which become prominent in the ambulatory setting. Since successful detection of various neurological disorders is greatly dependent upon clean EEG and fNIRS signals, it is a matter of utmost importance to remove motion artifacts from these two signal modalities using reliable and robust methods. This paper proposes three novel multiresolution analysis techniques: i) Variational mode decomposition (VMD), ii) VMD in combination with principal component analysis (VMD-PCA), and iii) VMD in combination with canonical correlation analysis (VMD-CCA), for motion artifact correction from single-channel EEG and fNIRS signals. The efficacy of these novel techniques is validated by computing the difference in the signal to noise ratio (<inline-formula> <tex-math notation="LaTeX">$\Delta SNR$ </tex-math></inline-formula>) and percentage reduction in motion artifacts (<inline-formula> <tex-math notation="LaTeX">$\eta$ </tex-math></inline-formula>). Among the three proposed novel methods, VMD-CCA decomposed with 15 intrinsic mode functions (IMFs) has shown the best denoising performance for EEG signals producing an average <inline-formula> <tex-math notation="LaTeX">$\Delta SNR$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$\eta $ </tex-math></inline-formula> values of 23.81 dB and 57.01&#x0025;, respectively for all 23 EEG recordings. On the other hand, for the available 16 fNIRS recordings, VMD-CCA decomposed with 10 IMFs produced an average <inline-formula> <tex-math notation="LaTeX">$\Delta SNR$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$\eta $ </tex-math></inline-formula> values of 15.97 dB and 39.01&#x0025;, respectively. The results reported using the proposed methods outperform most of the existing state-of-the-art techniques.