Cognitive fatigue and electroencephalographic energy density Bioelectric Technology for Human Performance Workshop NASA Ames Research Center July 31, 2001 Cognitive fatigue and electroencephalographic energy density by Leslie D. Montgomery, Ph.D. Lockheed Martin Engineering & Sciences CO. Richard W. Montgomery, Ph.D. San Jose State Foundation

Presentation Overview Background of our work at NASA Explanation of TEEG Energy Density Applications of TEEG Energy Density Suggested research/Open questions

Overall Objective To develop practical, easily applied, measures of cerebral activity that can be used under operational conditions in human factors research: - Topographic electroencephalography - Rheoencephalography - Electrophysiology

Background Cerebral blood flow during exposure to microgravity Internal research project at SRI International Phase I and II SBIR through NASA Langley Research Center MSTTP at NASA Ames Research Center DDF at NASA Ames Research Center AOS Program element at Ames Research Center

International 10 - 20 TEEG electrode assignments

Typical TEEG voltage traces

Stimulus used during mathematical tests

Typical event-related potential (ERP) from one TEEG electrode

Typical TEEG voltage values at a given point in time

Model TEEG regression equation V = (a + bX + cY + dXY)3 where: V = voltage at electrode coordinates X, Y X = side-to-side direction in flattened scalp electrode grid Y = front-to-back direction in electrode grid V = (b1 + b2X + b3X2 + .......+ b15X2Y3 + b16 X3Y3)

Derivation of topographic electro-encephalographic energy density surface

R-Square peaks: Mental task error index vs. ERP energy density Electrode Period (Msec) R-Square Slope T T5 47-86 0.913 -5.624 T5 55-94 0.948 -7.402 FPZ 86-126 0.877 -4.624 FPZ 94-133 0.897 -5.122 F3 102-141 0.878 -4.639 FPZ 110-149 0.856 -4.609 FPZ 118-157 0.876 -4.223 P4 141-180 0.851 4.134 P4 149-188 0.878 4.657 P4 157-196 0.882 4.740 P4 165-204 0.869 4.458 T5 172-211 0.914 -5.637

Typical correlation of group performance with TEEG energy density

Locus of peak TEEG energy density during mental arithmetic task

Male vs. female performance of mental arithmetic task

Normal vs. disabled readers

Visual discrimination task format

ERP voltage traces of target and non-target stimuli

ERP energy density traces of target and non-target stimuli

Correlation of TEEG energy density to visual task performance

Typical energy and performance profiles during long term mental arithmetic task performance

This subject was ill. The general pattern is the same as the end point of longer term tasks.

In spite of a surge of energy starting at ERP three, this subject was unable to prevent marked deterioration of performance after that.

This subject's energy level seemed to be sufficient to maintain performance until the drop in energy at the eighth ERP.

Simulation Model Flow Variables H = share of available metabolic energy used for homeostasis functions, M = share of available energy used for a cognitive task (e.g., mental arithmetic), NH = extent of neglected homeostasis functions (measured in energy units), NM = extent of neglected mental arithmetic requirements (in energy units), RH = required energy to meet homeostasis demands RM = required energy to maintain an adequate mental arithmetic performance.

Simulation Model Relationships 1. M is a diversion of a flow otherwise fully available as H: M = 1 - H. 2. Homeostasis neglect accumulates and its ‘repair cost’ rises exponentially: NH = (RH-H) ert dt. 3. Mental arithmetic neglect is simply the shortfall, relative to required energy: NM = RM - M. 4. The H-flow is regulated to balance the neglected functions: dH / dt = a (NH - NM).

Simulation Model Solution At this level of sophistication only two coefficients are involved: r = the rate of exponential growth of the importance of the homeostasis neglect. a = the rate of response to that neglect. Of course it also is necessary to give values to RH and RM. In terms of operator notation, this system of equations reduces to the following second-order differential equation with a time-varying parameter: (D2 + a D + a ert ) H = a RH

Simulation of short term task

Simulation of medium term task

Simulation of long term task

Whole head total ERP integrated energy density values

Graphical Display Techniques

3 D Representation of Energy Density Metric of Mental Fatigue

3 D Representations of Energy Density for Target/Non-Target Stimuli

Impedance Plethysmography for measurement of segmental volume and blood flow

Typical impedance electrode configuration

Pulsatile resistance changes indicate blood flow changes

Metrics used to quantify segmental hemodynamics

Correlation of brain blood flow and neural activity during a mental arithmetic task

Summary Impedance techniques can be used to assess an operator’s circulatory responses to long term mental tasks and to evaluate countermeasure concepts TEEG energy density procedures can be used to investigate neural activity during varied and long term cognitive tasks An operator’s performance degradation during a cognitive task may be influenced by his allocation of mental resources during the task lmontgomery@mail.arc.nasa.gov

Suggested Research Continued testing of energy density technique Combined analysis of ERP and on-going EEG data Joint measures of cerebral EEG and metabolic activity Refine cerebral resource allocation model Inclusion of cerebral blood flow measurements