Automated real-time classification of functional states: significance of individual tuning stage

1. IntroductionClassification of the human functional state of an individual (Danilova, 1992; Hockey et al., 2003) is an important problem in modern psychology. It is a key subject of the EU FP integrated project "Advanced sensor development for attention, stress, vigilance and sleep/wakefulness monitoring (SENSATION)" (Maglavera et al., 2006), The Research and Technology Organization (RTO of NATO) program "Operator Functional State Assessment" (Hockey et al., 2003; Veltman et al., 2004), and a number of other projects. Applications of the automated classification of functional states include stress resistance evaluation, supervision over operators of critical infrastructure (air traffic controller, power plant operator, engine-driver, etc.), teaching, and phobia therapy. Such classification is particularly efficient in systems for teaching and/or phobia therapy that include a virtual reality module (e.g., virtual cave) and provide the capability for dynamic adjustment of task complexity: in this case the implementation of feedback based on real-time automated functional state classification can be used for the automated optimal selection of task complexity, and such a selection is a key factor for successful teaching and therapy.Standard mathematical approaches to the automated classification problem are based on the detection of characteristic patterns of classes, and on the construction of classifier functions that give close values for elements of the same class, and distinguishable values for elements from different classes. If characteristic patterns and classifier functions cannot be revealed using solely specific expert knowledge of the domain, automated and semi-automated machine-learning methods are used: Artifical Neural Networks (Wasserman, 1989), Support Vector Machine (Cortes and Vapnik, 1995), Logistic Regression (Hosmer and Lemeshow, 2000), Random Forest (Breiman, 2001), etc.However, these methods can be efficiently applied only if the size of learning samples is considerably larger than the dimension of the feature (measurement) arrays used for the classification. In the case of functional state classification based on electroencephalographic (EEG) data, this limitation is violated: the typical size of learning samples here does not exceed a few hundred (in most cases -- several dozen), and the number of measurements associated even with short (e.g., one second) time windows exceeds one thousand (and generally exceeds ten thousand). Recently we developed a specialized method for the automated construction of classifier functions for binary functional state classification, which provided reliable classification of stress vs. state of calm wakefulness (also referred to as a normal functional state) despite the relatively small size of learning samples (Galatenko et al., 2012). Along with the efficient procedure for dimension reduction, this method included an individual tuning stage that allowed the involvement of certain information on individual peculiarities in the classification. This stage is non-standard for conventional machine-learning methods. In this paper we show that the individual tuning stage significantly increases the reliability of the classification.The rest of the paper is organized as follows. In the section "Algorithm scheme" a short description of the algorithm for automated real-time classification of a functional state based on EEG data is given. A detailed description of the algorithm was given (Galatenko et al., 2012). In the section "Experiment design", an outline of an experiment for evaluating the importance of the individual tuning stage for classification reliability is given. In the section "Results", experiment results are discussed.2. Algorithm scheme2.1 Dimension reductionMeasurements of EEG signals are performed independently by N sensors with frequency F Hz, where a typical value of N is several dozen (up to 256) and the typical value of F is several hundred (up to 5000). …