Harrivel, Angela

NASA Technical Reports Server

Monitoring attentional state using  
functional near infrared spectroscopy 
 
Angela Harrivel 
PhD Candidate, Biomedical Engineering, University of Michigan 
NASA Glenn Research Center 
fNIRS Symposium 
Center for Human Growth and Development 
5/4/2012 
https://ntrs.nasa.gov/search.jsp?R=20150010122 2019-08-31T07:55:40+00:00Z
Monitoring attentional state using fNIRS:  
Motivation 
 Attentional lapses during 
safety-critical tasks such as 
piloting are a source of risk. 
 In-task detection allows 
error-prone states to be 
avoided before off-nominal 
events occur, reducing 
accidents. 
photo by Marv Smith 
funded by NASA’s Aviation Safety Program 
 To determine the accuracy with which attentional state can be 
discriminated during task performance using:  
• pattern classification with training based on behavior 
• and the detection of functionally-connected attentional (ATN)  
and default mode (DMN) networks with fNIRS. 
 
Monitoring attentional state using fNIRS:  
Aims 
credit ISS, Inc. 
Monitoring attentional state using fNIRS:  
 Default Mode Network 
 The default mode network, one of many resting state functional 
networks: 
• deactivates with goal-oriented behavior.  
(Greicius, 2003; Raichle, 2001) 
• has been shown to reveal attentional lapses (Weissmann, 2006) 
• and to be associated with poor performance due to sleep 
deprivation. (Drummond, 2005) 
 
 DMN measurement may allow improved specificity of state prediction 
by differentiating forehead-only measures.  
 Frontal oxygenation as measured by fNIRS has been show to be 
sensitive to workload during a complex task, but not necessarily 
predictive of performance decrement. (Izzetoglu, 2004) 
tissue projection tool available at  
http://wwwneuro03.uni-muenster.de/ger/t2tconv/conv3d.html 
[Steinstrater, Knecht, 2001] 
Monitoring attentional state using fNIRS:  
Head Probe Locations 
 anti-correlated ATN and 
DMN (task-positive and 
task-negative) activations 
are expected  
(Kelly, 2008; Fox, 2005) 
 for example, 8 locations 
as shown 
 functional connectivity 
can be used as a check on 
probe placement or as  
a classifier input feature 
Monitoring attentional state using fNIRS:  
fNIRS-fMRI Study  
 We plan to use pilot fMRI studies to verify 
and inform probe locations 
 Protocol:  one 8min rest  plus  
5 x { 4min continuous performance task 
followed by one minute of rest } 
 We will assess cross-network correlation  
and classification accuracy  
for various input features 
 Each of the four probe  
locations has both  
a main and a shallow  
physiological source 
 Using the Imagent by ISS, Inc. 
probes from ISS, Inc. and fMRI-compatible headgear 
fSCOPE: real time plotting, classification and correlation software 
written by Dr. Tristan Hearn, GRC co-op 
in Python using Lib SVM (Chang, 2001) 
We are still adding motion artifact and physiology removal. 
Glenn Research Center new technology number 1312303591 
Classification Optimization 
40 min  
 Add training labels to the time series from session one 
based on behavioral measures, avoiding artifact 
 Rest began at 400s 
 optimize the Support Vector Machine parameters  
 select c, gamma based on 4-fold cross validation accuracy 
Classification Optimization 
Real time classification process: 
session one 
train: 
 
 
 
predict: 
 
MFG 
PFC  
PFC  
MFG 
 train with time series from two probes during session one   
 predict all of session one with the selected c and gamma 
 obtain 97% cross validation accuracy  
 save the SVM model 
normalized to the mean, relative [HbOx-HbDeOx], 0.01 Hz to 0.5 Hz band pass 
Real time classification process: 
session two 
label: 
 
 
predict:  
Ox-DeOx: 
 
 
raw DC: 
 
MFG 
PFC  
PFC  
MFG 
MFG 
PFC  
 open session two and predict with that SVM model 
 obtain 74% accuracy  
with respect to the labels shown at the top 
Acknowledgements 
This work is performed in the fMRI Laboratory at the University of 
Michigan, where Ms. Harrivel is a PhD candidate.  
Co-advisors: Prof. Douglas Noll and Dr. Scott Peltier.   
Committee members: Prof. Luis Hernandez, Prof. Daniel Weissman, 
and Prof. Theodore Huppert.  
 
It is supported by the NASA Aviation Safety Program. All subjects 
consented to take part in this human subject study as approved by the 
NASA Committee for the Protection of Human Subjects.  We gratefully 
acknowledge colleagues at the NASA Glenn Research Center.  
Questions? 
angelarh@umich.edu 
angela.r.harrivel@nasa.gov 
References 
Chih-Chung Chang and Chih-Jen Lin (2001), ‘LIBSVM: a library for support vector machines’, 
Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm 
Christoff, K., et al., (2009), ‘Experience sampling during fMRI reveals default network and 
executive system contrbutions to mind wandering’, PNAS, 106(21): 8719-8724. 
Drummond, S.P.A., et al., Compensatory recruitment after sleep deprivation and the relationship 
with performance. Psychiatry Research-Neuroimaging, 2005. 140(3): p. 211-223. 
Fox, M.D., Raichle, M.E. et al. (2005), ‘The human brain is intrinsically organized into dynamic, 
anticorrelated functional networks’, PNAS, 102(27): 9673-9678. 
Greicius, M.D., et al., Functional connectivity in the resting brain: A network analysis of the 
default mode hypothesis. Proceedings of the National Academy of Sciences of the United 
States of America, 2003. 100(1): p. 253-258. 
Harrivel, A., et al. 2009. Toward Improved Headgear for Monitoring with Functional Near 
Infrared Spectroscopy, 15th Annual Meeting of the Organization for Human Brain Mapping, 
July, 2009, San Francisco, CA. NeuroImage, 47: S141. Technology case number LEW-18280-1 
Harrivel, A., et al. 2010. A System for Attentional State Detection with Functional Near Infrared 
Spectroscopy. Human Computer Interaction - Aerospace, November 2010, Cape Canaveral, 
Florida. 
Harrivel, A., et al. 2011. Monitoring attentional state using functional near infrared spectroscopy: 
A pilot study. 17th Annual Meeting of the Organization for Human Brain Mapping, June, 
2011. Quebec City, Canada, poster #519WTh. 
Huppert, et al. (2009), ‘HomER: a review of time-series analysis methods for near-infrared 
spectroscopy of the brain’, Applied Optics, 48(10): D280-D298. 
Izzetoglu, K., et al. (2004), ‘Functional Optical Brain Imaging Using Near-Infrared During 
Cognitive Tasks’, International Journal of Human-Computer Interaction, 17(2): 211-227. 
References 
Kelly, et al. (2008), ‘Competition between functional brain networks mediates behavioral 
variability’, NeuroImage, 39: 527–537. 
LaConte, S. M., Peltier, S. J. & Hu, X. P. P. (2007), ‘Real-time fMRI using brain-state 
classification’, Human Brain Mapping, 28: 1033-1044. 
Matthews, F., Pearlmutter, B.A. et al. (2008), ‘Hemodynamics for brain computer interfaces’, IEEE 
Signal Processing Magazine, 25(1): 87-94. 
Mesquita, et al. (2010), ‘Resting state functional connectivity of the whole head with near-infrared 
spectroscopy’, Biomedical Optics Express, 1(1): 324-336.  
Raichle, M.E., et al., A default mode of brain function. Proceedings of the National Academy of 
Sciences of the United States of America, 2001. 98(2): p. 676-682. 
Raichle, M.E. and A.Z. Snyder, A default mode of brain function: a brief history of an evolving idea. 
Neuroimage, 2007. 37(4): p. 1083-90; discussion 1097-9. 
Sarter, Gehring, Kozak. 2006. More Attention must be paid: The neurobiology of attentional effort. 
Brain Res. Reviews, 51: 145-160. 
Smallwood, J., et al. (2008), ‘Going AWOL in the Brain: Mind Wandering Reduces Cortical 
Analysis of External Events’,  The Journal of Cognitive Neuroscience, 20(3): 458–469. 
Steinstrater O., Sommer J., Frank A., Jansen A., Knecht S. (2001 ), ‘Transcranial magnetic 
stimulation and language: how variable are our stimulation sites?’ Neuroimage, vol. 13, p. 
S257. 
Uddin, L.Q., et al., Functional Connectivity of Default Mode Network Components: Correlation, 
Anticorrelation, and Causality. Human Brain Mapping, 2009. 30(2): p. 625-637. 
Weissman, D.H., Roberts, K.C., et al. (2006), ‘The neural bases of momentary lapses in attention’, 
Nature Neuroscience, 9(7): 971-978. 
B-Alert Cognitive State software  
with proprietary metrics to classify data from  
B-Alert Wirless EEG systems  
by Biopac Systems, Inc. 
Existing commercial EEG product 
Existing commercial EEG product 
Emotiv EPOC  
Brain Activity Map  
 
real-time map of: 
 
Delta (0.5-4Hz) - indicating deep sleep, 
restfulness, and conversely excitement or 
agitation when delta waves are suppressed  
 
Theta (4-8Hz) - indicating deep meditative 
states, daydreaming and automatic tasks  
 
Alpha (8-15Hz) - indicating relaxed 
alertness, restful and meditative states  
 
Beta (15-30Hz) - indicating wakefulness, 
alertness, mental engagement and conscious 
processing of information.  
 


Monitoring Attentional State Using Functional Near Infrared Spectroscopy

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