Repetitive tasks can lead to long-term cardiovascular problems due to continuous strain and inadequate recovery. The automobile operators on the assembly line are exposed to these risks when workload volume changes according to the workstation type. However, the current ergonomic assessments focus primarily on observational and, in some cases, biomechanical methods that are subjective and time-consuming, overlooking cardiorespiratory adaptations. This study aimed to analyze the cardiorespiratory response to distinct workload volumes and ergonomic risk (ER) scores for an automotive assembly line. Sixteen male operators (age = 38 ± 8 years; BMI = 25 ± 3 kg·m²) volunteered from three workstations (H1, H2, and H3) with specific work cycle duration (1, 3, and 5 min respectively). Electrocardiogram (ECG), respiratory inductance plethysmography (RIP), and accelerometer (ACC) data were collected during their shift. The results showed significant differences from the first to the last 10 min, where H3 had its SDRRi reduced (p = 0.014), H1’s phase synchrony and H2’s coordination between thoracic and abdominal movements decreased (p < 0.001, p = 0.039). In terms of ergonomic risk, the moderate-high rank showed a reduction in SDRRi (p = 0.037) and moderate-risk activities had diminished phase synchrony (p = 0.018) and correlation (p = 0.004). Thus, the explored parameters could have the potential to develop personalized workplace adaptation and risk assessment systems.

The generation of synthetic electroencephalography (EEG) data has emerged as a promising strategy to mitigate the scarcity of real datasets in neuroscience. Medical data collection, particularly of intracranial EEG (iEEG), remains constrained by the complexity of acquisition procedures and the limited availability of eligible participants. While generative models offer a powerful means to produce realistic synthetic data, their progress is hindered by the absence of standardized evaluation protocols. Existing metrics are largely confined to basic statistical comparisons and fail to capture the intrinsic multi-domain characteristics of EEG signals such as non-stationarity, spectral complexity and fractal dynamics. To address this gap, we present a comprehensive evaluation framework that assesses synthetic (i)EEG across three complementary dimensions: fidelity, diversity, and privacy. The framework introduces novel metrics to quantify time-, frequency-, time–frequency-, and complexity-based similarities, evaluate population-level coverage and variability, and estimate identity disclosure risk. Each metric is provided with interpretation guidelines and integrated into composite scores, enabling transparent benchmarking and systematic comparison across generative models. This framework, released as the open-source Python library seege_, advances methodological rigour and interpretability, providing a comprehensive and standardized evaluation toolkit for generative models in clinical and neuroscientific applications.