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In an attempt to understand human physiological signals when an individual is subjected to pain, we set up a tonic pain experiment in a laboratory setting. The subjects’ physiological signals were recorded, timestamped, and compared to an initial 30 second baseline measurement. Subjects were also asked to verbally state their level of pain based on a visual analog scale in order to compare reported pain levels with physiological signals. The physiological signals measured were: Electroencephalography (EEG), Pupillary Unrest Under Ambient Light (PUAL), Skin Conductance (SC), Electromyography (EMG), Respiration Rate (RR), Blood Volume Pulse (BVP), Skin Temperature (ST), Blood Pressure (BP), and Facial Expression (FE). ANOVA and frequency domain analyses were conducted on the data in order to determine whether there was a significant difference between the ‘pain’ and ‘no pain’ (baseline) states of an individual. Based on our results, skin conductance, PUAL, facial expression, and EEG signals were theorized to be good signals for the classification of tonic pain, or any pain applied directly to an individual.
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On the Effects of Pain on fNIRS Classification
We present the first study of the effects of pain on the classification of fNIRS recordings, and evaluate the performance of a model trained on the pain-free data for classifying the data with the presence of pain.
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- Award ID(s):
- 1841087
- PAR ID:
- 10169032
- Date Published:
- Journal Name:
- The OSA Biophotonics Congress
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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