Advancements in Measuring Cognition Using EEG and fNIRS DOI
Sushil Chandra,

Abhinav Choudhury

Published: Jan. 1, 2023

Human cognition is the essential building block of human intelligence, and it what makes us who we are. Cognition defined as capacity to recognize respond appropriately external stimuli based on one's beliefs, actions, experiences, senses. It one fundamental reasons for existence most important aspects brain. In childhood, adolescence, maturity, cognitive processes humans are always evolving developing. Although some these abilities begin diminish grows older approaches others deteriorate when neurons die systems that replace them become insufficient. Understanding not just healthy growth survival but also treatment a variety neuropsychological conditions, such Alzheimer's disease. necessary examine functions brain before can comprehend cognition. fNIRS electroencephalography (EEG) low-cost methods assessing evaluating function. The principles functional near-infrared spectroscopy (fNIRS), well number preprocessing interpreting EEG data, therefore covered in this chapter. Lastly, use simultaneous EEG-fNIRS discussed along with its limitations advantages.

Language: Английский

Motion artifacts removal and evaluation techniques for functional near-infrared spectroscopy signals: A review DOI Creative Commons
Ruisen Huang, Keum‐Shik Hong, Dalin Yang

et al.

Frontiers in Neuroscience, Journal Year: 2022, Volume and Issue: 16

Published: Oct. 3, 2022

With the emergence of an increasing number functional near-infrared spectroscopy (fNIRS) devices, significant deterioration in measurement caused by motion artifacts has become essential research topic for fNIRS applications. However, a high requirement mathematics and programming limits related researches. Therefore, here we provide first comprehensive review artifact removal aiming to (i) summarize latest achievements, (ii) present solutions evaluation metrics from perspective application reproduction, (iii) predict future topics field. The synthesizes information fifty-one journal articles (screened according three criteria). Three hardware-based nine algorithmic are summarized, their requirements (compatible signal types, availability online applications, limitations) extensions discussed. Five noise suppression two distortion were synthesized evaluate methods. Moreover, highlight deficiencies existing research: balance between use auxiliary hardware that solution is not clarified; few studies mention filtering delay solutions, robustness stability under extreme conditions

Language: Английский

Citations

28
Enhancing fNIRS data analysis with a novel motion artifact detection algorithm and improved correction DOI
Weihao Huang, Jun Li

Biomedical Signal Processing and Control, Journal Year: 2024, Volume and Issue: 95, P. 106496 - 106496

Published: May 28, 2024

Language: Английский

Citations

5
Identification of Functional Cortical Plasticity in Children with Cerebral Palsy Associated to Robotic-Assisted Gait Training: An fNIRS Study DOI Open Access
David Perpetuini, Emanuele Francesco Russo, Daniela Cardone

et al.

Journal of Clinical Medicine, Journal Year: 2022, Volume and Issue: 11(22), P. 6790 - 6790

Published: Nov. 16, 2022

Cerebral palsy (CP) is a non-progressive neurologic condition that causes gait limitations, spasticity, and impaired balance coordination. Robotic-assisted training (RAGT) has become common rehabilitation tool employed to improve the pattern of people with neurological impairments. However, few studies have demonstrated effectiveness RAGT in children CP its effects through portable neuroimaging techniques, such as functional near-infrared spectroscopy (fNIRS). The aim study evaluate neurophysiological processes elicited by fNIRS, which was acquired during three sessions one month. repeated measure ANOVA applied β-values delivered General Linear Model (GLM) analysis used for fNIRS data analysis, showing significant differences activation both prefrontal cortex (F (1.652, 6.606) = 7.638; p 0.022), sensorimotor (1.294, 5.175) 11.92; 0.014) different sessions. In addition, cross-validated Machine Learning (ML) framework implemented estimate gross motor function (GMFM-88) from GLM β-values, obtaining an estimation correlation coefficient r 0.78. This approach can be tailor clinical treatment each child, improving CP.

Language: Английский

Citations

22
Can Data-Driven Supervised Machine Learning Approaches Applied to Infrared Thermal Imaging Data Estimate Muscular Activity and Fatigue? DOI Creative Commons
David Perpetuini, Damiano Formenti, Daniela Cardone

et al.

Sensors, Journal Year: 2023, Volume and Issue: 23(2), P. 832 - 832

Published: Jan. 11, 2023

Surface electromyography (sEMG) is the acquisition, from skin, of electrical signal produced by muscle activation. Usually, sEMG measured through electrodes with electrolytic gel, which often causes skin irritation. Capacitive contactless have been developed to overcome this limitation. However, EMG devices are still sensitive motion artifacts and not comfortable for long monitoring. In study, a non-invasive method estimate parameters indicative muscular activity fatigue, as they assessed EMG, infrared thermal imaging (IRI) cross-validated machine learning (ML) approaches described. Particularly, 10 healthy participants underwent five series bodyweight squats until exhaustion interspersed 1 min rest. During exercising, vastus medialis its temperature were IRI, respectively. The average rectified value (ARV) median frequency power spectral density (MDF) each estimated several ML applied IRI features, obtaining good estimation performances (r = 0.886, p < 0.001 ARV, r 0.661, MDF). Although measure physiological processes different nature interchangeable, these results suggest potential link between fostering employment methods deliver metrics in manner sports clinical applications.

Language: Английский

Citations

10
Identifying ADHD boys by very-low frequency prefrontal fNIRS fluctuations during a rhythmic mental arithmetic task DOI Creative Commons

Sergio Ortuño-Miró,

Sergio Molina‐Rodríguez, Carlos Belmonte

et al.

Journal of Neural Engineering, Journal Year: 2023, Volume and Issue: 20(3), P. 036018 - 036018

Published: May 23, 2023

Objective.Computer-aided diagnosis of attention-deficit/hyperactivity disorder (ADHD) aims to provide useful adjunctive indicators support more accurate and cost-effective clinical decisions. Deep- machine-learning (ML) techniques are increasingly used identify neuroimaging-based features for objective assessment ADHD. Despite promising results in diagnostic prediction, substantial barriers still hamper the translation research into daily clinic. Few studies have focused on functional near-infrared spectroscopy (fNIRS) data discriminate ADHD condition at individual level. This work develop an fNIRS-based methodological approach effective identification boys via technically feasible explainable methods.Approach.fNIRS signals recorded from superficial deep tissue layers forehead were collected 15 clinically referred (average age 11.9 years) non-ADHD controls during execution a rhythmic mental arithmetic task. Synchronization measures time-frequency plane computed find frequency-specific oscillatory patterns maximally representative or control group. Time series distance-based fed four popular ML linear models (support vector machine, logistic regression (LR), discriminant analysis naïve Bayes) binary classification. A 'sequential forward floating selection' wrapper algorithm was adapted pick out most discriminative features. Classifiers performance evaluated through five-fold leave-one-out cross-validation (CV) statistical significance by non-parametric resampling procedures.Main results.LR achieved accuracy, sensitivity specificity scores near 100% (p<.001) both CV schemes when trained with only three key wrapper-selected features, arising surface components very low frequency.Significance.We preliminary evidence that very-low frequency fNIRS fluctuations induced/modulated task accurately differentiate controls, outperforming other similar studies. The proposed holds promise finding biomarkers reliable interpretable enough inform practice.

Language: Английский

Citations

8
Assessment of fNIRS Signal Processing Pipelines: Towards Clinical Applications DOI Creative Commons
Augusto Bonilauri, Francesca Sangiuliano Intra, Giuseppe Baselli

et al.

Applied Sciences, Journal Year: 2021, Volume and Issue: 12(1), P. 316 - 316

Published: Dec. 29, 2021

Functional Near-Infrared Spectroscopy (fNIRS) captures activations and inhibitions of cortical areas implements a viable approach to neuromonitoring in clinical research. Compared more advanced methods, continuous wave fNIRS (CW-fNIRS) is currently used clinics for its simplicity mapping the whole sub-cranial cortex. Conversely, it often lacks hardware reduction confounding factors, stressing importance correct signal processing. The proposed pipeline includes movement artifact (MAR), bandpass filtering (BPF), principal component analysis (PCA). Eight MAR algorithms were compared among 23 young adult volunteers under motor-grasping task. Single-subject examples are shown followed by percentage energy (ERD%) statistics single steps cumulative values. block average hemodynamic response function was with generalized linear model fitting. Maps significant activation/inhibition illustrated. mean ERD% pre-processed signals concerning initial raw reached 4%. A tested multichannel variant showed overcorrection on 4-fold expansive windows. All found similar contralateral motor area. In conclusion, channel suggested BPF PCA. cortex integration applications also confirmed our results.

Language: Английский

Citations

19
Subject-specific information enhances spatial accuracy of high-density diffuse optical tomography DOI Creative Commons
Sruthi Srinivasan,

Deepshikha Acharya,

Emilia Butters

et al.

Frontiers in Neuroergonomics, Journal Year: 2024, Volume and Issue: 5

Published: Feb. 19, 2024

Functional near-infrared spectroscopy (fNIRS) is a widely used imaging method for mapping brain activation based on cerebral hemodynamics. The accurate quantification of cortical using fNIRS data highly dependent the ability to correctly localize positions light sources and photodetectors scalp surface. Variations in head size shape across participants greatly impact precise locations these optodes consequently, regions surface being reached. Such variations can therefore influence conclusions drawn NIRS studies that attempt explore specific regions. In order preserve spatial identity each channel, subject-specific differences array registration must be considered. Using high-density diffuse optical tomography (HD-DOT), we have demonstrated inter-subject variability same HD-DOT applied ten recorded resting state. We also compared three-dimensional image reconstruction results obtained positioning information those generic optode locations. To mitigate error introduced by all participants, photogrammetry was identify per-participant. present work demonstrates large variation between subjects terms which parcels are sampled equivalent channels array. particular, motor cortex recordings suffered from largest localization errors, with median 27.4 mm optodes, leading parcel sensitivity. These illustrate importance collecting wearable experiments, perform group-level analysis parcellation.

Language: Английский

Citations

2
Facial thermal imaging: A systematic review with guidelines and measurement uncertainty estimation DOI Creative Commons
Valentina Stanić, Gregor Geršak

Measurement, Journal Year: 2024, Volume and Issue: 242, P. 115879 - 115879

Published: Oct. 11, 2024

Language: Английский

Citations

2
mmBP DOI Open Access
Zhenguo Shi, Tao Gu, Yu Zhang

et al.

Published: Nov. 6, 2022

Blood pressure (BP) measurement is an indispensable tool in diagnosing and treating many diseases such as cardiovascular failure stroke. Traditional direct can be invasive, wearable-based methods may have limitations of discomfort inconvenience. Contact-free BP has been recently advocated a promising alternative. In particular, Millimetre-wave (mmWave) sensing demonstrated its potential, however it confronted with several challenges including noise vulnerability to human's tiny motions which occur intentionally inevitably. this paper, we propose mmBP, contact-free mmWave-based system high accuracy motion robustness. Due the frequency short wavelength, mmWave signals received time domain are dramatically susceptible ambient noise, deteriorating signal quality. To reduce novel delay-Doppler feature transformation method exploit signal's characteristics features significantly improve quality for pulse waveform construction. We also temporal referential functional link adaptive filter leveraging on periodic correlation alleviate impact motions. Extensive experiment results achieved by leave-one-out cross-validation (LOOCV) demonstrate that mmBP achieves mean errors 0.87mmHg 1.55mmHg systolic blood (SBP) diastolic (DBP), respectively; standard deviation 5.01mmHg 5.27mmHg SBP DBP, respectively.

Language: Английский

Citations

10
Is It Possible to Estimate Average Heart Rate from Facial Thermal Imaging? DOI Creative Commons
David Perpetuini, Andrea Di Credico, Chiara Filippini

et al.

Published: Nov. 22, 2021

The remote measurement of heart rate (HR) could have many applications, such as health and emotional conditions monitoring. Currently, methods based on visible cameras been developed for HR estimation. However, the employment techniques with scarce illumination be challenging. Infrared Thermography (IRT) a valuable tool to overcome this limitation. This study investigated possibility estimating average facial IRT through cross-validated machine learning (ML) approach. correlation coefficient between estimated measured was 0.7. Although preliminary, these results demonstrate feasibility IRT.

Language: Английский

Citations

14