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

• Published in: NeuroImage (Orlando, Fla.) Vol. 59; no. 1; pp. 57 - 63
• Main Authors: Christensen, James C., Estepp, Justin R., Wilson, Glenn F., Russell, Christopher A.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 02.01.2012; Elsevier Limited
• Subjects: Accuracy; Automation; Classification; Discriminant analysis; EEG; Electroencephalography; Female; Humans; Independent sample; Interday variability; Male; Pattern classification; Pattern Recognition, Automated - methods; Signal processing; Signal Processing, Computer-Assisted; Task Performance and Analysis; User-Computer Interface; Workload; Workload - classification; Young Adult; EEG; Pattern classification; Interday variability; Workload
• ISSN: 1053-8119; 1095-9572; 1095-9572
• DOI: 10.1016/j.neuroimage.2011.07.091

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. ► Psychophysiological data exhibits variability or nonstationarity across days. ► We applied pattern classification to EEG data in order to monitor mental workload. ► Accuracy was low across days, but improved with multiple days in the training set. ► Including small amounts of data from a new day boosted accuracy as well. ► Either method produces 80%+classification accuracy, adequate for many applications.