Biosignal processing challenges in emotion recognition for adaptive learning
By Aniket Vartak
User-centered computer based learning is an emerging field of interdisciplinary research.
Research in diverse areas such as psychology, computer science, neuroscience and signal
processing is making contributions to take this field to the next level. Learning
systems built using contributions from these fields could be used in actual training and education
instead of just laboratory proof-of-concept. One of the important advances in this research is the
detection and assessment of the cognitive and emotional state of the learner using such systems.
This capability moves development beyond the use of traditional user performance metrics to
include system intelligence measures that are based on current theories in neuroscience. These
advances are of paramount importance in the success and wide spread use of learning systems
that are automated and intelligent.
Emotion is considered an important aspect of how learning occurs, and yet estimating it
and making adaptive adjustments are not part of most learning systems. In this research we focus
on one specific aspect of constructing an adaptive and intelligent learning system, that is,
estimation of the emotion of the learner as he/she is using the automated training system. The
challenge starts with the definition of the emotion and the utility of it in human life. The next
challenge is to measure the co-varying factors of the emotions in a non-invasive way, and find
consistent features from these measures that are valid across wide population. In this research we
use four physiological sensors that are non-invasive, and establish a methodology of utilizing the
data from these sensors using different signal processing tools. A validated set of visual stimuli
used worldwide in the research of emotion and attention, called International Affective Picture
System (IAPS), is used. A dataset is collected from the sensors in an experiment designed to
elicit emotions from these validated visual stimuli. We describe a novel wavelet method to
calculate hemispheric asymmetry metric using electroencephalography data. This method is
tested against typically used power spectral density method. We show overall improvement in
accuracy in classifying specific emotions using the novel method. We also show distinctions
between different discrete emotions from the autonomic nervous system activity using
electrocardiography, electrodermal activity and pupil diameter changes. Findings from different
features from these sensors are used to give guidelines to use each of the individual sensors in
the adaptive learning environment.
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