Towards a near infrared spectroscopy-based estimation of operator attentional state

Given the critical risks to public health and safety that can involve lapses in attention (e.g., through implication in workplace accidents), researchers have sought to develop cognitive-state tracking technologies, capable of alerting individuals engaged in cognitively demanding tasks of potentiall...

Full description

Saved in:
Bibliographic Details
Published in:PloS one 2014-03, Vol.9 (3), p.e92045-e92045
Main Authors: Derosière, Gérard, Dalhoumi, Sami, Perrey, Stéphane, Dray, Gérard, Ward, Tomas
Format: Article
Language:English
Subjects:
Activities of daily living
Adult
Attention - physiology
Behavior - physiology
Biology
Cerebral Cortex - metabolism
Cerebral Cortex - physiology
Cognitive science
Computer Science
Engineering
Health aspects
Hemoglobins - metabolism
Humans
Infrared spectroscopy
Machine learning
Male
Medicine
Near infrared spectroscopy
Neuroscience
Occupations
Optical Phenomena
Oxyhemoglobins - metabolism
Reaction Time - physiology
Social and Behavioral Sciences
Spectroscopy, Near-Infrared
Stimulus salience
Workplace accidents
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Given the critical risks to public health and safety that can involve lapses in attention (e.g., through implication in workplace accidents), researchers have sought to develop cognitive-state tracking technologies, capable of alerting individuals engaged in cognitively demanding tasks of potentially dangerous decrements in their levels of attention. The purpose of the present study was to address this issue through an investigation of the reliability of optical measures of cortical correlates of attention in conjunction with machine learning techniques to distinguish between states of full attention and states characterized by reduced attention capacity during a sustained attention task. Seven subjects engaged in a 30 minutes duration sustained attention reaction time task with near infrared spectroscopy (NIRS) monitoring over the prefrontal and the right parietal areas. NIRS signals from the first 10 minutes of the task were considered as characterizing the 'full attention' class, while the NIRS signals from the last 10 minutes of the task were considered as characterizing the 'attention decrement' class. A two-class support vector machine algorithm was exploited to distinguish between the two levels of attention using appropriate NIRS-derived signal features. Attention decrement occurred during the task as revealed by the significant increase in reaction time in the last 10 compared to the first 10 minutes of the task (p
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0092045