Affective State Detection using fNIRs and Machine Learning
Ritam Ghosh
Affective states regulate our day to day to function and has a tremendous
effect on mental and physical health. Detection of affective states is of
utmost importance for mental health monitoring, smart entertainment selection
and dynamic workload management. In this paper, we discussed relevant
literature on affective state detection using physiology data, the benefits and
limitations of different sensors and methods used for collecting physiology
data, and our rationale for selecting functional near-infrared spectroscopy. We
present the design of an experiment involving nine subjects to evoke the
affective states of meditation, amusement and cognitive load and the results of
the attempt to classify using machine learning. A mean accuracy of 83.04% was
achieved in three class classification with an individual model; 84.39%
accuracy was achieved for a group model and 60.57% accuracy was achieved for
subject independent model using leave one out cross validation. It was found
that prediction accuracy for cognitive load was higher (evoked using a pen and
paper task) than the other two classes (evoked using computer bases tasks). To
verify that this discrepancy was not due to motor skills involved in the pen
and paper task, a second experiment was conducted using four participants and
the results of that experiment has also been presented in the paper.