First Demonstration of an Alertness Monitoring Management System

Abstract

A first laboratory version of an Alertness Monitoring/Management (AMM) system has been designed and implemented. The system continually estimates the level of alertness of a human subject using EEG spectral information recorded from the subject's scalp, and delivers auditory feedback to assist the subject in managing his or her own level of alertness in work environments requiring constant vigilance. The system allows experimenters to monitor its input and output via real-time color graphics displays.As a first demonstration and evaluation of the system, six subjects participated in five halfhour sessions (three training and two feedback sessions), which involved dual detection tasks simulating the passive sonar environment. Auditory targets, 300-ms noisebursts presented at 6 dB above a noise background, were presented at a mean rate of 10 targets per minute. A continuoup visual waterfall display presented illuminated vertical line targets at a mean rate of one per minute. Subjects pressed one response button to report noisebursts and another to report visual targets.Neural net estimation algorithms were trained for each subject to estimate the current probability of detecting auditory targets using electroencephalogram (EEG) and performance data collected during one or more of the initial training sessions. During feedback sessions, real-time signal processing and individualized neural network analysis of EEG recorded from a central scalp electrode were used to estimate continuously, in near real-time, the current probability-ofdetection of auditory targets. Whenever this probability-of-detection measure declined below a preset threshold (e.g., when it predicted more than a 40% chance of failure to detect the auditory targets), the system sounded an alarm in the subject's headphones. When training sessions comprising a relatively wide range of detection rates were used to train the estimation algorithms, the alertness estimates followed changes in observed detection probability relatively accurately.Four of the six subjects reported that the alertness feedback helped them to maintain detection performance. A fifth subject did not produce enough detection lapses to fairly evaluate the system. Review of data from the sixth subject suggested that future versions of the system may be able to provide useful feedback to this subject as well. Review of results of the demonstration "experiment have suggested several improvements to signal processing and training procedures used in the system. Effects of these enhancements on system performance are being evaluated.

MINIMA Project: Detecting and Mitigating the Negative Impact of Automation

“…The general evidence is that lower levels of vigilance are related to increases in lower frequencies (theta and alpha) in EEG spectrum [62]. Several BCI systems have been designed based on this idea [56][57] [63][64]. Interestingly, these studies show that mental (de)activation may be monitored by changing balance between brain activity regions.…”