mEDA: Mobile DC-EDA Circuit Validation

Suparna Veeturi, Nishtha Bhagat, Vignesh Ravichandran, Ben Annicelli, Stephanie Carreiro, Krishna Venkatasubramanian, Dhaval Solanki, Kunal Mankodiya

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Keywords: EDA, SCL, SCR, Dynamic Time Warping (DTW), BIOPAC, silver knit dry electrode, tonic, phasic

TL;DR: Validation of DC current EDA circuit (mEDA) against BIOPAC.

Abstract:

Electrodermal activity (EDA) provides a direct indicator of
sympathetic nervous system arousal through changes in skin
conductance. However, wearable EDA sensing poses
challenges such as inconsistent skin contact, electrode
impedance variability, motion artifacts, and power constraints.
To address these issues, this study presents Mobile EDA
(mEDA), a compact device driven by a stabilized direct-current
source. A validation study was conducted on ten healthy adult
participants in a time-synchronized protocol to collect data
from BIOPAC and mEDA concurrently. mEDA recordings
employed gel electrodes for P1–P5 and dry (textile) electrodes
for P6–P10, while the BIOPAC MP160 system used gel
electrodes for all participants. Participants underwent a 30
minute protocol of resting, deep breathing, and three cognitive
tasks. Preprocessing pipeline consists of lowpass filter and
artifact (sharp peaks and flat line) removal. Cleaned signals
were converted into frequency domain for decomposition into
low and high frequency component, skin conductance level
(SCL) and skin conductance response (SCR) respectively. SCL
and SCR were converted back to time domain for analyzing
performance metrics between both devices. Pearson
correlation, coherence, and DTW were computed on SCL,
while zero crossing peaks were counted for SCR analysis. With
gel electrodes, the average Pearson correlation was 0.92 and
the SCR peak count difference was 38. For textile electrodes,
the correlation was 0.88 with a peak count difference of 119.
Both configurations achieved coherence above 0.95 and DTW
below 0.5 for most participants. These results demonstrate
mEDA’s reliable performance to capture both tonic and phasic
EDA across electrode configurations.

Track: 2. Sensors and systems for digital health, wellness, and athletics

NominateReviewer: Kunal Mankodiya

Submission Number: 139