The public reporting burden for this coliection of infornnation is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing this collection of information. Send comments regarding this burden estimate or any other aspect of this collection of infonmation, including suggestions for reducing this burden, PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES) 8. PERFORMING ORGANIZATION REPORT NUMBER SPONSORING / MONITORING AGENCY NAME(S) AND ADDRESS(ES)Air Force SPONSOR/MONITOR'S REPORT NUMBER(S)Approved for public release; distribution is unlimited. SUPPLEMENTARY NOTES 20040324 044 ABSTRACTThe functional state of the human operator is critical to optimal system performance. Degraded states of operator functioning can lead to errors and overall suboptimal system performance. Accurate assessment of operator functional state is crucial to the successful implementation of an adaptive aiding system. One method of determining operators' functional state is by monitoring their physiology. In the present study, artificial neural networks using physiological signals were used to continuously monitor, in real time, the functional state of 7 participants while they performed the Multi-Attribute Task Battery with two levels of task difficulty. Six channels of brain electrical activity and eye, heart and respiration measures were evaluated on line. The accuracy of the classifier was determined to test its utility as an on-line measure of operator state. The mean classification accuracies were 85%, 82%, and 86% for the baseline, low task difficultv. and high task difficulty conditions, respectivelv. The functional state of the human operator is critical to optimal system performance. Degraded states of operator functioning can lead to errors and overall suboptimal system performance. Accurate assessment of operator functional state is crucial to the successful implementation of an adaptive aiding system. One method of determining operators' functional state is by monitoring their physiology. In the present study, artificial neural networks using physiological signals were used to continuously monitor, in real time, the functional state of 7 participants while they performed the Multi-Attribute Task Battery with two levels of task difficulty Six channels of brain electrical activity and eye, heart and respiration measures were evaluated on line. The accuracy of the classifier was determined to test its utility as an on-line measure of operator state. The mean classification accuracies were 85%, 82%, and 86% for the baseline, low task difficulty, and high task difficulty conditions, respectively The high levels of accuracy suggest that these procedures can be used to provide accurate estimates of operator functional state that can be used to provide adaptive aiding. The relative contribution of each of the 43 psychophysiological features was also determined. Actual or potential applications of this r...

The electro-encephalogram (EEG) is useful for clinical diagnosis and in biomedical research. EEG signals, however, especially those recorded from frontal channels, often contain strong electro-oculogram (EOG) artifacts produced by eye movements. Existing regression-based methods for removing EOG artifacts require various procedures for preprocessing and calibration that are inconvenient and time-consuming. The paper describes a method for removing ocular artifacts based on adaptive filtering. The method uses separately recorded vertical EOG and horizontal EOG signals as two reference inputs. Each reference input is first processed by a finite impulse response filter of length M (M = 3 in this application) and then subtracted from the original EEG. The method is implemented by a recursive least-squares algorithm that includes a forgetting factor (lambda = 0.9999 in this application) to track the non-stationary portion of the EOG signals. Results from experimental data demonstrate that the method is easy to implement and stable, converges fast and is suitable for on-line removal of EOG artifacts. The first three coefficients (up to M = 3) were significantly larger than any remaining coefficients.

Potential future applications of this research include enhanced workstations using adaptive aiding that would be driven by operator functional state.

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