EC-CEG01 25A: A dataset of Neurological and Physiological Data from Physical and Digital Twin Laboratories in Control Systems Education

During both sessions, neurophysiological data were recorded continuously in order to characterize cognitive states such as attention, alertness, fatigue, and stress while students performed equivalent tasks in the physical and virtual environments. Biometric Device Used: Muse 2 EEG Headband: Records brain electrical activity (EEG) at 256 Hz from four channels (TP9, AF7, AF8, TP10), capturing five primary brainwave frequencies (delta, theta, alpha, beta, and gamma).

These signals support the examination of cognitive workload, concentration, and mental fatigue throughout the learning activities. Embrace Plus Wristband (Empatica): Physiological activity was recorded using the Embrace Plus, a multimodal wearable device designed for continuous monitoring of autonomic nervous system responses. The device captures several biomarkers relevant to cognitive and emotional states.

Biomarkers collected: ●Electrodermal Activity (EDA): Records phasic and tonic skin conductance responses associated with arousal, stress, engagement, and emotional activation. Sampling frequency: approximately 4 Hz. ●Blood Volume Pulse (BVP): Measures peripheral blood flow through photoplethysmography (PPG), supporting the calculation of: ● Heart Rate (HR), Heart Rate Variability (HRV), Indicators associated with stress regulation, workload, and autonomic balance. ●Skin Temperature: Captures peripheral thermoregulation changes related to stress, cognitive effort, and affective responses.

Sampling frequency: ~1 Hz. The purpose of this dataset is to document and compare neurological and physiological responses associated with student performance in physical and digital twin laboratory environments within control engineering education. The collected signals provide a basis for identifying differences in cognitive engagement and mental load across experimental conditions, supporting the design of more effective instructional strategies in engineering laboratory-based learning.