scholarly journals Bayesian filtering of human brain hemodynamic activity elicited by visual short-term maintenance recorded through functional near-infrared spectroscopy (fNIRS)

2010 ◽  
Vol 18 (25) ◽  
pp. 26550 ◽  
Author(s):  
F. Scarpa ◽  
S. Cutini ◽  
P. Scatturin ◽  
R. Dell’Acqua ◽  
G. Sparacino
Keyword(s):  
Infrared Spectroscopy ◽  
Human Brain ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Bayesian Filtering ◽  
Short Term ◽  
Functional Near Infrared Spectroscopy ◽  
Term Maintenance

scholarly journals Imaging System Based on Silicon Photomultipliers and Light Emitting Diodes for Functional Near-Infrared Spectroscopy

2020 ◽  
Vol 10 (3) ◽  
pp. 1068 ◽  
Author(s):  
Giovanni Maira ◽  
Antonio M. Chiarelli ◽  
Stefano Brafa ◽  
Sebania Libertino ◽  
Giorgio Fallica ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Human Brain ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Light Emitting Diodes ◽  
Optical Tomography ◽  
System Optimization ◽  
Infrared Light ◽  
Functional Near Infrared Spectroscopy ◽  
Light Emitting

We built a fiber-less prototype of an optical system with 156 channels each one consisting of an optode made of a silicon photomultiplier (SiPM) and a pair of light emitting diodes (LEDs) operating at 700 nm and 830 nm. The system uses functional near-infrared spectroscopy (fNIRS) and diffuse optical tomography (DOT) imaging of the cortical activity of the human brain at frequencies above 1 Hz. In this paper, we discuss testing and system optimization performed through measurements on a multi-layered optical phantom with mechanically movable parts that simulate near-infrared light scattering inhomogeneities. The baseline optical characteristics of the phantom are carefully characterized and compared to those of human tissues. Here we discuss several technical aspects of the system development, such as LED light output drift and its possible compensation, SiPM linearity, corrections of channel signal differences, and signal-to-noise ratio (SNR). We implement an imaging algorithm that investigates large phantom regions. Thanks to the use of SiPMs, very large source-to-detector distances are acquired with a high SNR and 2 Hz time resolution. The overall results demonstrate the high potentialities of a system based on SiPMs for fNIRS/DOT human brain imaging applications.

Design and Development of Two Channel Portable Near Infrared Spectroscopy System for Studies of Cerebral Oxygenation in Prefrontal Cortex of Human Brain

2014 ◽  
Vol 573 ◽  
pp. 814-818
Author(s):  
S. Bagyaraj ◽  
G. Ravindran ◽  
S. Shenbaga Devi
Keyword(s):  
Prefrontal Cortex ◽  
Infrared Spectroscopy ◽  
Human Brain ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Continuous Wave ◽  
Cerebral Oxygenation ◽  
Wave Length ◽  
Spatial Task ◽  
Functional Near Infrared Spectroscopy

Functional near infrared spectroscopy is a noninvasive, non harmful, low cost and safe optical technique that can be used to study the functional activities in the human brain. This paper describes the development of two channel Near InfraRed Spectroscopy (NIRS) system and the results of the cerebral oxygenation changes during the different cognitive tasks. The objective of the study is to design, develop a portable non-invasive continuous wave NIRS system with dual wave length for determining the hemoglobin content of the blood chromophores during different activities of the prefrontal cortex of the brain. The two channel NIRS system designed and it was tested with 20 healthy, ie.,15 males and 5 females with an average age group of 21±2.25, they were given a 2 different mental tasks such as sequential subtraction (mathematical task) and spot the difference (Visuo-spatial task) and their Oxy & de-Oxy hemoglobin concentration was measured which showed more changes during the task period when compared to relaxation in both left and right part of pre-frontal cortex.

scholarly journals PR-SVM algorithm for recognition of human hand tapping using functional near infrared spectroscopy

2013 ◽  
Vol 16 (3) ◽  
pp. 5-17
Author(s):  
Hai Thanh Nguyen ◽  
Cuong Quoc Ngo ◽  
Hung Viet Nguyen
Keyword(s):  
Infrared Spectroscopy ◽  
Human Brain ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Invasive Technique ◽  
Support Vector ◽  
Functional Near Infrared Spectroscopy ◽  
Right Hand ◽  
Svm Algorithm ◽  
Hand Tapping

Researches of human Brain Computer Interface (BCI) for the objective of diagnosis and rehabilitation have been recently increased. Cerebral oxygenation and blood flow on particular regions of human brain can be measured using a non-invasive technique – fNIRS (functional Near Infrared Spectroscopy). In this paper, a study of recognition algorithm will be described for recognizing whether one taps his/her left hand or right hand. Data with noises and artifacts collected from a multi-channel system will be pre-processed using a Savitzky- Golay filter for getting more smoothly fNIRS data. Characteristics of the filtered signals during left and right hand tapping process will be extracted using a Polynomial Regression (PR)-Support Vector Machine (SVM) algorithm. Coefficients of the polynomial determined by the PR algorithm, which correspond to Oxygen-Hemoglobin (Oxy- Hb) concentration changes, will be applied for the recognition of hand tapping. Then the SVM will be employed to validate the obtained coefficient data for the hand tapping recognition. Experimental results have been done many trials on 3 subjects to illustrate the effectiveness of the proposed method.

Classification of affect using deep learning on brain blood flow data

2019 ◽  
Vol 27 (3) ◽  
pp. 206-219 ◽  
Author(s):  
Danushka Bandara ◽  
Leanne Hirshfield ◽  
Senem Velipasalar
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Classification Accuracy ◽  
Near Infrared ◽  
Time Series Data ◽  
Self Report ◽  
Series Data ◽  
Spectroscopy Data ◽  
Short Term ◽  
Functional Near Infrared Spectroscopy

We present a convolutional neural network- and long short-term memory-based method to classify the valence level of a computer user based on functional near infrared spectroscopy data. Convolutional neural networks are well suited for capturing the spatial characteristics of functional near infrared spectroscopy data. And long short-term memories are demonstrated to be good at learning temporal patterns of unknown length in time series data. We explore these methods in a combined layered architecture in order to improve classification accuracy. We conducted an experiment with 20 participants, wherein they were subjected to emotion inducing stimuli while their brain activity was measured using functional near infrared spectroscopy. Self-report surveys were administered after each stimulus to gauge participants' self-assessment of their valence. The resulting classification using these survey labels as ground truth provided a three-class classification accuracy 77.89% in across subject cross-validation. This method also shows promise for generalization to other classification tasks using functional near infrared spectroscopy data.

Sign in / Sign up

Export Citation Format

Share Document