New Citations | BIOPAC in EDA Research
Researchers utilize electrodermal activity (EDA) data in a wide array of protocols. These recent studies join thousands of BIOPAC citations for EDA and represent just a few of BIOPAC’s hardware options for wired, wireless, logged, or MRI protocols with reusable or disposable EDA accessories and AcqKnowledge software solutions for Automated EDA Analysis Routines and EDA Measurement Tools.
By using a caffeine oral film, also called a neuromodulator molecule, researchers evaluated the caffeine delivery profile into the body. Participant data for Baseline and Monitoring phases was recorded using the BIOPAC data acquisition system in conjunction with AcqKnowledge software to assess three psychophysiological signals: electrocardiogram (ECG) for heart rate, respiratory frequency (RF) for breath rate, and electrodermal activity (EDA). Read the full study: Validation of Psychophysiological Measures for Caffeine Oral Films Characterization by Machine Learning Approaches. Bioengineering, 9(3), 114. Batista, P., Rodrigues, P. M., Ferreira, M., Moreno, A., Silva, G., Alves, M., Pintado, M., & Oliveira-Silva, P. (2022)
Researchers seeking to improve deception detection efficiency combined brain imaging data from functional near-infrared spectroscopy (fNIRS), skin conductance response (SCR), heart rate (HR), and reaction time (RT) to determine if the combination of these indicators could improve the efficiency of deception detection in concealed information tests (CIT). A TSD203 EDA transducer was placed on the phalanges of the index and middle fingers of the left hand to record SCR; electrocardiogram (ECG) data were acquired using three electrodes placed in a lead II configuration. Read the full article: Detecting concealed information using functional near-infrared spectroscopy (fNIRS) combined with skin conductance, heart rate, and behavioral measures. Psychophysiology, 59, e14029. , , , , & (2022)
Researchers examined metrics of Heart Rate Variability (HRV), Electrodermal Activity (EDA), and Eye Tracking (ET) that are commonly used to predict cognitive performance to determine if any aspects of prolonged wakefulness make a metric a potent predictor. Read the full article: Archetypal physiological responses to prolonged wakefulness. Biomedical Signal Processing and Control, Volume 74, 103529. Daley, M. S., Diaz, K., Posada-Quintero, H. F., Kong, Y., Chon, K., & Bolkhovsky, J. B. (2022)
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