Affect Detection and Classification from the Non-stationary Physiological Data

Abstract

Affect detection from physiological signals has received a great deal of attention recently. One arising challenge is that physiological measures are expected to exhibit considerable variations or non-stationarities over multiple days/sessions recordings. These variations pose challenges to effectively classify affective sates from future physiological data. The present study collects affective physiological data (electrocardiogram (ECG), electromyogram (EMG), skin conductivity (SC), and respiration (RSP)) from four participants over five sessions each. The study provides insights on how diagnostic physiological features of affect change over time. We compare the classification performance of two feature sets, pooled features (obtained from pooled day data) and day-specific features using an up datable classifier ensemble algorithm. The study also provides an analysis on the performance of individual physiological channels for affect detection. Our results show that using pooled feature set for affect detection is more accurate than using day-specific features. The corrugator and zygomatic facial EMGs were more reliable measures for detecting valence than arousal compared to ECG, RSP and SC over the span of multi-session recordings. It is also found that corrugator EMG features and a fusion of features from all physiological channels have the highest affect detection accuracy for both valence and arousal.