The effects of day-to-day variability of physiological data on operator functional state classification

The application of pattern classification techniques to physiological data has undergone rapid expansion. Tasks as varied as the diagnosis of disease from magnetic resonance images, brain-computer interfaces for the disabled, and the decoding of brain functioning based on electrical activity have been accomplished quite successfully with pattern classification. These classifiers have been further applied in complex cognitive tasks to improve performance, in one example as an input to adaptive automation. In order to produce generalizable results and facilitate the development of practical systems, these techniques should be stable across repeated sessions. This paper describes the application of three popular pattern classification techniques to EEG data obtained from asymptotically trained subjects performing a complex multitask across five days in one month. All three classifiers performed well above chance levels. The performance of all three was significantly negatively impacted by classifying across days; however two modifications are presented that substantially reduce misclassifications. The results demonstrate that with proper methods, pattern classification is stable enough across days and weeks to be a valid, useful approach.