mEDA: Mobile DC-EDA Circuit Validation
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. The preprocessing pipeline consisted of low-pass filter and artifact (sharp peaks and flat line) removal. Cleaned signals were converted into frequency domain components for decomposition into low and high frequency components, skin conductance level (SCL), and skin conductance response (SCR) respectively. SCL and SCR were converted back to the time domain to analyze performance metrics between both devices. Pearson correlation, coherence, and Dynamic Time Warping (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 in capturing both tonic and phasic EDA across electrode configurations.
Authors
- K. Venkatasubramanian (ORCID: https://orcid.org/0000-0002-2708-9530)
- Neeru Bhagat (ORCID: https://orcid.org/0000-0002-9943-5439)
- Dhaval Solanki (ORCID: https://orcid.org/0000-0003-4039-4406)
- Suparna Veeturi (ORCID: https://orcid.org/0009-0007-3039-744X)
- Stephanie Carreiro
- Kunal Mankodiya
- Vignesh Ravichandran
- Ben Annicelli
Institutions
- University of Massachusetts Chan Medical School (US)
- University of Rhode Island (US)
Publication Details
- Published
- 2025-11-03
- DOI
- https://doi.org/10.1109/bsn66969.2025.11337617
- Primary Topic
- Emotion and Mood Recognition
- Type
- article
- Field-Weighted Citation Impact
- 0.00