Utilizing functional near-infrared spectroscopy for prediction of cognitive workload in noisy work environments

Abstract. Occupational noise frequently occurs in the work environment in military intelligence, surveillance, and reconnaissance operations. This impacts cognitive performance by acting as a stressor, potentially interfering with the analysts’ decision-making process. We investigated the effects of different noise stimuli on analysts’ performance and workload in anomaly detection by simulating a noisy work environment. We utilized functional near-infrared spectroscopy (fNIRS) to quantify oxy-hemoglobin (HbO) and deoxy-hemoglobin concentration changes in the prefrontal cortex (PFC), as well as behavioral measures, which include eye tracking, reaction time, and accuracy rate. We hypothesized that noisy environments would have a negative effect on the participant in terms of anomaly detection performance due to the increase in workload, which would be reflected by an increase in PFC activity. We found that HbO for some of the channels analyzed were significantly different across noise types (p<0.05). Our results also indicated that HbO activation for short-intermittent noise stimuli was greater in the PFC compared to long-intermittent noises. These approaches using fNIRS in conjunction with an understanding of the impact on human analysts in anomaly detection could potentially lead to better performance by optimizing work environments.

[1]  Kevin W Williams,et al.  A Summary of Unmanned Aircraft Accident/Incident Data: Human Factors Implications , 2004 .

[2]  Alyssa M. Piasecki,et al.  Improving Anomaly Detection Through Identification of Physiological Signatures of Unconscious Awareness , 2017 .

[3]  E. Donchin,et al.  Performance of concurrent tasks: a psychophysiological analysis of the reciprocity of information-processing resources. , 1983, Science.

[4]  Huijuan Zhao,et al.  Maps of optical differential pathlength factor of human adult forehead, somatosensory motor and occipital regions at multi-wavelengths in NIR. , 2002, Physics in medicine and biology.

[5]  Luc Van Gool,et al.  You'll never walk alone: Modeling social behavior for multi-target tracking , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[6]  Tanja Schultz,et al.  Mental workload during n-back task—quantified in the prefrontal cortex using fNIRS , 2014, Front. Hum. Neurosci..

[7]  Ulrich Ettinger,et al.  Understanding noise stress-induced cognitive impairment in healthy adults and its implications for schizophrenia. , 2014, Noise & health.

[8]  Witold Pedrycz,et al.  A novel approach for anomaly detection in data streams: Fuzzy-statistical detection mode , 2016, J. Intell. Fuzzy Syst..

[9]  Masashi Kiguchi,et al.  Mental stress grading based on fNIRS signals , 2016, 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[10]  Bente E. Moen,et al.  Noise exposure and cognitive performance: A study on personnel on board Royal Norwegian Navy vessels , 2015, Noise & health.

[11]  Diane Beale,et al.  Noise, psychosocial stress and their interaction in the workplace , 2003 .

[12]  Etienne Grandjean,et al.  Results of an Ergonomic Investigation of Large-Space Offices , 1973 .

[13]  Daniela Nicklas,et al.  Brain activity measured with fNIRS for the prediction of cognitive workload , 2015, 2015 6th IEEE International Conference on Cognitive Infocommunications (CogInfoCom).

[14]  You-Yin Chen,et al.  Neurovascular coupling: in vivo optical techniques for functional brain imaging , 2013, BioMedical Engineering OnLine.

[15]  Robert J. K. Jacob,et al.  Using fNIRS brain sensing to evaluate information visualization interfaces , 2013, CHI.

[16]  Robert J. K. Jacob,et al.  Using fNIRS brain sensing in realistic HCI settings: experiments and guidelines , 2009, UIST '09.

[17]  Axel Buchner,et al.  Habituation of the irrelevant sound effect: evidence for an attentional theory of short-term memory disruption. , 2012, Journal of experimental psychology. Learning, memory, and cognition.

[18]  Ramzi W Nahhas,et al.  Hand-grasping and finger tapping induced similar functional near-infrared spectroscopy cortical responses , 2016, Neurophotonics.

[19]  Peter Chapman,et al.  Prefrontal Cortex Activation and Young Driver Behaviour: A fNIRS Study , 2016, PloS one.

[20]  M. Ferrari,et al.  A brief review on the use of functional near-infrared spectroscopy (fNIRS) for language imaging studies in human newborns and adults , 2012, Brain and Language.

[21]  Nasser H. Kashou,et al.  New Window on Optical Brain Imaging; Medical Development, Simulations and Applications , 2012 .

[22]  H. Gr.  Compensatory control in the regulation of human performance under stress and high workload; a cognitive-energetical framework. , 1997 .

[23]  Jonathan D. Cohen,et al.  Conflict monitoring and anterior cingulate cortex: an update , 2004, Trends in Cognitive Sciences.

[24]  Kazuo Hiraki,et al.  Near-infrared spectroscopy (NIRS) in functional research of prefrontal cortex , 2015, Front. Hum. Neurosci..

[25]  Aaron T. Buss,et al.  Validating an image-based fNIRS approach with fMRI and a working memory task , 2017, NeuroImage.

[26]  G. R. J. Hockey.  Compensatory control in the regulation of human performance under stress and high workload: A cognitive-energetical framework , 1997, Biological Psychology.

[27]  S. Narayanan,et al.  Cognitively-engineered multisensor image fusion for military applications , 2009, Inf. Fusion.

[28]  Philip N. Ainslie.  Applied Aspects of Ultrasonography in Humans , 2012 .

[29]  T. Jung,et al.  Task performance and eye activity: predicting behavior relating to cognitive workload. , 2007, Aviation, space, and environmental medicine.

[30]  R. Hughes,et al.  Auditory distraction: A duplex-mechanism account. , 2014, PsyCh journal.

[31]  S. Tremblay,et al.  Using near infrared spectroscopy and heart rate variability to detect mental overload , 2014, Behavioural Brain Research.

[32]  Vyacheslav P. Tuzlukov,et al.  Signal detection theory , 2001 .

[33]  N. Kersten,et al.  Occupational noise and myocardial infarction: Considerations on the interrelation of noise with job demands , 2015, Noise & health.

[34]  John G. Neuhoff,et al.  Spatiotemporal Pattern of Neural Processing in the Human Auditory Cortex , 2002, Science.

[35]  Yoko Hoshi,et al.  Spatiotemporal characteristics of hemodynamic changes in the human lateral prefrontal cortex during working memory tasks , 2003, NeuroImage.

[36]  Peter Eachus,et al.  A Brief Review of Research Using Near-Infrared Spectroscopy to Measure Activation of the Prefrontal Cortex during Emotional Processing: The Importance of Experimental Design , 2016, Front. Hum. Neurosci..

[37]  Richard S. J. Frackowiak,et al.  Representation of the temporal envelope of sounds in the human brain. , 2000, Journal of neurophysiology.

[38]  D. Boas,et al.  HomER: a review of time-series analysis methods for near-infrared spectroscopy of the brain. , 2009, Applied optics.

[39]  D. Delpy,et al.  Optical pathlength measurements on adult head, calf and forearm and the head of the newborn infant using phase resolved optical spectroscopy. , 1995, Physics in medicine and biology.

[40]  Klaus Scheffler,et al.  Effect of fMRI acoustic noise on non-auditory working memory task: comparison between continuous and pulsed sound emitting EPI , 2005, Magnetic Resonance Materials in Physics, Biology and Medicine.

[41]  Mary Fendley,et al.  Decision Aiding to Overcome Biases in Object Identification , 2012, Adv. Hum. Comput. Interact..

[42]  Ning Liu,et al.  Inferring deep-brain activity from cortical activity using functional near-infrared spectroscopy. , 2015, Biomedical optics express.

[43]  A. Owen,et al.  Anterior prefrontal cortex: insights into function from anatomy and neuroimaging , 2004, Nature Reviews Neuroscience.

[44]  S. Kosslyn,et al.  Impact of fMRI Acoustic Noise on the Functional Anatomy of Visual Mental Imagery , 2002, Journal of Cognitive Neuroscience.

[45]  Nasser H Kashou,et al.  Stimulus and optode placement effects on functional near-infrared spectroscopy of visual cortex , 2016, Neurophotonics.

[46]  VARUN CHANDOLA,et al.  Anomaly detection: A survey , 2009, CSUR.

[47]  Rik Warren,et al.  Use of Mahalanobis Distance for Detecting Outliers and Outlier Clusters in Markedly Non-Normal Data: A Vehicular Traffic Example , 2011 .

[48]  K. R. Ridderinkhof,et al.  The Role of the Medial Frontal Cortex in Cognitive Control , 2004, Science.

[49]  G. McCarthy,et al.  Perceiving patterns in random series: dynamic processing of sequence in prefrontal cortex , 2002, Nature Neuroscience.

[50]  M. Harms,et al.  Sound repetition rate in the human auditory pathway: representations in the waveshape and amplitude of fMRI activation. , 2002, Journal of neurophysiology.

[51]  Daniel Afergan,et al.  Using fNIRS to Measure Mental Workload in the Real World , 2014 .

[52]  Patrik Sörqvist.  On interpretation of the effects of noise on cognitive performance: the fallacy of confusing the definition of an effect with the explanation of that effect , 2015, Front. Psychol..

[53]  P. Goldman-Rakic,et al.  Prefrontal Activation Evoked by Infrequent Target and Novel Stimuli in a Visual Target Detection Task: An Event-Related Functional Magnetic Resonance Imaging Study , 2000, The Journal of Neuroscience.

[54]  S. Huettel,et al.  Resolving Response, Decision, and Strategic Control: Evidence for a Functional Topography in Dorsomedial Prefrontal Cortex , 2009, The Journal of Neuroscience.

[55]  Hasan Ayaz,et al.  Optical brain monitoring for operator training and mental workload assessment , 2012, NeuroImage.