scholarly journals Speech token detection and discrimination in individual infants using functional near-infrared spectroscopy

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Darren Mao ◽  
Julia Wunderlich ◽  
Borislav Savkovic ◽  
Emily Jeffreys ◽  
Namita Nicholls ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared ◽  
False Positive Rate ◽  
Objective Measure ◽  
Speech Discrimination ◽  
Functional Near Infrared Spectroscopy ◽  
Speech Detection ◽  
Discrimination Ability ◽  
Positive Rate ◽  
Speech Token

AbstractSpeech detection and discrimination ability are important measures of hearing ability that may inform crucial audiological intervention decisions for individuals with a hearing impairment. However, behavioral assessment of speech discrimination can be difficult and inaccurate in infants, prompting the need for an objective measure of speech detection and discrimination ability. In this study, the authors used functional near-infrared spectroscopy (fNIRS) as the objective measure. Twenty-three infants, 2 to 10 months of age participated, all of whom had passed newborn hearing screening or diagnostic audiology testing. They were presented with speech tokens at a comfortable listening level in a natural sleep state using a habituation/dishabituation paradigm. The authors hypothesized that fNIRS responses to speech token detection as well as speech token contrast discrimination could be measured in individual infants. The authors found significant fNIRS responses to speech detection in 87% of tested infants (false positive rate 0%), as well as to speech discrimination in 35% of tested infants (false positive rate 9%). The results show initial promise for the use of fNIRS as an objective clinical tool for measuring infant speech detection and discrimination ability; the authors highlight the further optimizations of test procedures and analysis techniques that would be required to improve accuracy and reliability to levels needed for clinical decision-making.

scholarly journals Objective measurement of tinnitus using functional near-infrared spectroscopy and machine learning

PLoS ONE ◽  
2020 ◽  
Vol 15 (11) ◽  
pp. e0241695 ◽  
Author(s):  
Mehrnaz Shoushtarian ◽  
Roohallah Alizadehsani ◽  
Abbas Khosravi ◽  
Nicola Acevedo ◽  
Colette M. McKay ◽  
...  
Keyword(s):  
Machine Learning ◽  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Resting State ◽  
Near Infrared ◽  
Objective Measure ◽  
Chronic Tinnitus ◽  
Subjective Ratings ◽  
Functional Near Infrared Spectroscopy ◽  
Auditory Evoked Responses

Chronic tinnitus is a debilitating condition which affects 10–20% of adults and can severely impact their quality of life. Currently there is no objective measure of tinnitus that can be used clinically. Clinical assessment of the condition uses subjective feedback from individuals which is not always reliable. We investigated the sensitivity of functional near-infrared spectroscopy (fNIRS) to differentiate individuals with and without tinnitus and to identify fNIRS features associated with subjective ratings of tinnitus severity. We recorded fNIRS signals in the resting state and in response to auditory or visual stimuli from 25 individuals with chronic tinnitus and 21 controls matched for age and hearing loss. Severity of tinnitus was rated using the Tinnitus Handicap Inventory and subjective ratings of tinnitus loudness and annoyance were measured on a visual analogue scale. Following statistical group comparisons, machine learning methods including feature extraction and classification were applied to the fNIRS features to classify patients with tinnitus and controls and differentiate tinnitus at different severity levels. Resting state measures of connectivity between temporal regions and frontal and occipital regions were significantly higher in patients with tinnitus compared to controls. In the tinnitus group, temporal-occipital connectivity showed a significant increase with subject ratings of loudness. Also in this group, both visual and auditory evoked responses were significantly reduced in the visual and auditory regions of interest respectively. Naïve Bayes classifiers were able to classify patients with tinnitus from controls with an accuracy of 78.3%. An accuracy of 87.32% was achieved using Neural Networks to differentiate patients with slight/ mild versus moderate/ severe tinnitus. Our findings show the feasibility of using fNIRS and machine learning to develop an objective measure of tinnitus. Such a measure would greatly benefit clinicians and patients by providing a tool to objectively assess new treatments and patients’ treatment progress.

scholarly journals Identification of ∆9-tetrahydrocannabinol (THC) impairment using functional brain imaging

2022 ◽  
Author(s):  
Jodi M. Gilman ◽  
William A. Schmitt ◽  
Kevin Potter ◽  
Brian Kendzior ◽  
Gladys N. Pachas ◽  
...  
Keyword(s):  
False Positive ◽  
Time Course ◽  
Near Infrared ◽  
False Positive Rate ◽  
Functional Brain Imaging ◽  
Imaging Method ◽  
Functional Near Infrared Spectroscopy ◽  
Post Dose ◽  
Standard Drug ◽  
Positive Rate

AbstractThe primary cannabinoid in cannabis, Δ9-tetrahydrocannabinol (THC), causes intoxication and impaired function, with implications for traffic, workplace, and other situational safety risks. There are currently no evidence-based methods to detect cannabis-impaired driving, and current field sobriety tests with gold-standard, drug recognition evaluations are resource-intensive and may be prone to bias. This study evaluated the capability of a simple, portable imaging method to accurately detect individuals with THC impairment. In this double-blind, randomized, cross-over study, 169 cannabis users, aged 18–55 years, underwent functional near-infrared spectroscopy (fNIRS) before and after receiving oral THC and placebo, at study visits one week apart. Impairment was defined by convergent classification by consensus clinical ratings and an algorithm based on post-dose tachycardia and self-rated “high.” Our primary outcome, PFC oxygenated hemoglobin concentration (HbO), was increased after THC only in participants operationalized as impaired, independent of THC dose. ML models using fNIRS time course features and connectivity matrices identified impairment with 76.4% accuracy, 69.8% positive predictive value (PPV), and 10% false-positive rate using convergent classification as ground truth, which exceeded Drug Recognition Evaluator-conducted expanded field sobriety examination (67.8% accuracy, 35.4% PPV, and 35.4% false-positive rate). These findings demonstrate that PFC response activation patterns and connectivity produce a neural signature of impairment, and that PFC signal, measured with fNIRS, can be used as a sole input to ML models to objectively determine impairment from THC intoxication at the individual level. Future work is warranted to determine the specificity of this classifier to acute THC impairment.ClinicalTrials.gov Identifier: NCT03655717

A Study of Frontal Signals in Brain Computer Interfaces: Interpretation of EEG & FNIRS

2020 ◽  
pp. 136-142
Author(s):  
S. Srilekha ◽  
B. Vanathi
Keyword(s):  
Infrared Spectroscopy ◽  
Healthy Subject ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Economic Conditions ◽  
Brain Computer Interfaces ◽  
Functional Near Infrared Spectroscopy ◽  
Computer Interfaces ◽  
Rehabilitation Devices

This paper focuses on electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) comparison to help the rehabilitation patients. Both methods have unique techniques and placement of electrodes. Usage of signals are different in application based on the economic conditions. This study helps in choosing the signal for the betterment of analysis. Ten healthy subject datasets of EEG & FNIRS are taken and applied to plot topography separately. Accuracy, Sensitivity, peaks, integral areas, etc are compared and plotted. The main advantages of this study are to prompt their necessities in the analysis of rehabilitation devices to manage their life as a typical individual.

61-LB: Use of Functional Near-Infrared Spectroscopy (fNIRS) to Assess Cognitive Effort in KATP-Related Neonatal Diabetes (KATP-NDM)

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 61-LB
Author(s):  
LISA R. LETOURNEAU-FREIBERG ◽  
KIMBERLY L. MEIDENBAUER ◽  
ANNA M. DENSON ◽  
PERSEPHONE TIAN ◽  
KYOUNG WHAN CHOE ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Neonatal Diabetes ◽  
Cognitive Effort ◽  
Functional Near Infrared Spectroscopy

The Use of fNIRS for Unique Contributions to Social and Affective Neuroscience

2019 ◽  
Author(s):  
Shannon Burns ◽  
Matthew D. Lieberman
Keyword(s):  
Infrared Spectroscopy ◽  
Social Interaction ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Affective Neuroscience ◽  
Functional Near Infrared Spectroscopy ◽  
Psychological Experience ◽  
The World ◽  
And Behavior ◽  
Meaningful Interaction

Social and affective neuroscience studies the neurophysiological underpinnings of psychological experience and behavior as it relates to the world around us. Yet, most neuroimaging methods require the removal of participants from their rich environment and the restriction of meaningful interaction with stimuli. In this Tools of the Trade article, we explain functional near infrared spectroscopy (fNIRS) as a neuroimaging method that can address these concerns. First, we provide an overview of how fNIRS works and how it compares to other neuroimaging methods common in social and affective neuroscience. Next, we describe fNIRS research that highlights its usefulness to the field – when rich stimuli engagement or environment embedding is needed, studies of social interaction, and examples of how it can help the field become more diverse and generalizable across participant populations. Lastly, this article describes how to use fNIRS for neuroimaging research with points of advice that are particularly relevant to social and affective neuroscience studies.

Toward Neuroscience of the Everyday World (NEW) using functional near-infrared spectroscopy

2021 ◽  
Vol 18 ◽  
pp. 100272
Author(s):  
Alexander von Lühmann ◽  
Yilei Zheng ◽  
Antonio Ortega-Martinez ◽  
Swathi Kiran ◽  
David C. Somers ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Functional Near Infrared Spectroscopy ◽  
The Everyday ◽  
Everyday World

scholarly journals Classification of Prefrontal Cortex Activity Based on Functional Near-Infrared Spectroscopy Data upon Olfactory Stimulation

2021 ◽  
Vol 11 (6) ◽  
pp. 701
Author(s):  
Cheng-Hsuan Chen ◽  
Kuo-Kai Shyu ◽  
Cheng-Kai Lu ◽  
Chi-Wen Jao ◽  
Po-Lei Lee
Keyword(s):  
Prefrontal Cortex ◽  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Kernel Functions ◽  
Olfactory Stimulation ◽  
Support Vector ◽  
Hemodynamic Response Function ◽  
Functional Near Infrared Spectroscopy ◽  
Data Set

The sense of smell is one of the most important organs in humans, and olfactory imaging can detect signals in the anterior orbital frontal lobe. This study assessed olfactory stimuli using support vector machines (SVMs) with signals from functional near-infrared spectroscopy (fNIRS) data obtained from the prefrontal cortex. These data included odor stimuli and air state, which triggered the hemodynamic response function (HRF), determined from variations in oxyhemoglobin (oxyHb) and deoxyhemoglobin (deoxyHb) levels; photoplethysmography (PPG) of two wavelengths (raw optical red and near-infrared data); and the ratios of data from two optical datasets. We adopted three SVM kernel functions (i.e., linear, quadratic, and cubic) to analyze signals and compare their performance with the HRF and PPG signals. The results revealed that oxyHb yielded the most efficient single-signal data with a quadratic kernel function, and a combination of HRF and PPG signals yielded the most efficient multi-signal data with the cubic function. Our results revealed superior SVM analysis of HRFs for classifying odor and air status using fNIRS data during olfaction in humans. Furthermore, the olfactory stimulation can be accurately classified by using quadratic and cubic kernel functions in SVM, even for an individual participant data set.

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