Joseph S. King

Age differences in dynamic measures of EEG.

Eighteen older adults and 18 younger adults were compared on two quantitative measures describing changes over time in the spatial distribution of running EEG. EEG was collected from 128 electrodes under resting eyes-open and eyes-closed conditions and during performance of a 13 minute sustained attention task. One EEG measure, the recrudescence rate, represented the number of changes in the location of the highest squared voltage per second. A second EEG measure consisted of the algorithmic complexity of changes in the location of the highest squared voltage over time. Regardless of the task condition, older adults had significantly higher scores than younger adults on both the recrudescence rate and the measure of algorithmic complexity. The implications of the results for neurologically-based theories of performance declines in older adults are discussed.

Age Differences in Factor Analysis of EEG

This study examined age differences in the factor structure of EEG using a 128-electrode system. Running EEG records were obtained from healthy younger and healthy older adults before, during, and after they performed a 13-minute Continuous Performance Task. Factor analyses were conducted on each five-second segment of EEG data by treating the voltages obtained at each electrode site as variables and each measurement epoch as a case. Results showed that the EEG records of older adults yielded significantly more factors than those of younger adults in every task condition. In addition, eigenvalues for the first common factor derived from EEG data sets were significantly larger in the EEG recordings of younger adults than older adults. The results are interpreted to indicate a greater degree of complexity in the spatial distribution of EEG activity in older adults, possibly reflecting an age-related decrease in the degree of coordination among cortical areas.

Some methods for dynamic analysis of the scalp recorded EEG

SummaryThis paper describes methods for quantifying the spatiotemporal dynamics of EEG. Development of these methods was motivated by watching computer-generated animations of EEG voltage records. These animations contain a wealth of information about the pattern of change across time in the voltages observed across the surface of the scalp. In an effort to quantify this pattern of changing voltages, we elected to extract a single quantifiable feature from each measurement epoch, the highest squared voltage among the various electrode sites. Nineteen channels of EEG were collected from subjects using an electrode cap with standard 10–20 system placements. Two minute records were obtained. Each record was sampled at a rate of 200 per second. Thirty seconds of artifact-free data were extracted from each 2 minute record. An algorithm then determined the location of the channel with the greatest amplitude for each 5 msec sampling epoch. We quantified these spatio-temporal dynamics as scalars, vectors and cluster analytic plots of EEG activity for finger tapping, cognitive effort (counting backwards) and relaxation to illustrate the utility of the techniques.