Effect of experimental factors on the recognition of affective mental states through physiological measures

Abstract

Reliable classification of an individual's affective state through processing of physiological response requires the use of appropriate machine learning techniques, and the analysis of how experimental factors influence the data recorded. While many studies have been conducted in this field, the effect of many of these factors is yet to be properly investigated and understood. This study investigates the relative effects of number of subjects, number of recording sessions, sampling rate and a variety of different classification approaches. Results of this study demonstrate accurate classification is possible in isolated sessions and that variation between sessions and subjects has a significant effect on classifier success. The effect of sampling rate is also shown to impact on classifier success. The results also indicate that affective space is likely to be continuous and that developing an understanding of the dimensions of this space may offer a reliable way of comparing results between subjects and studies. © Springer-Verlag Berlin Heidelberg 2009.