Gideon Vos

Gideon Vos, Kelly Trinh, Zoltan Sarnyai et al.

Introduction. The stress response has both subjective, psychological and objectively measurable, biological components. Both of them can be expressed differently from person to person, complicating the development of a generic stress measurement model. This is further compounded by the lack of large, labeled datasets that can be utilized to build machine learning models for accurately detecting periods and levels of stress. The aim of this review is to provide an overview of the current state of stress detection and monitoring using wearable devices, and where applicable, machine learning techniques utilized. Methods. This study reviewed published works contributing and/or using datasets designed for detecting stress and their associated machine learning methods, with a systematic review and meta-analysis of those that utilized wearable sensor data as stress biomarkers. The electronic databases of Google Scholar, Crossref, DOAJ and PubMed were searched for relevant articles and a total of 24 articles were identified and included in the final analysis. The reviewed works were synthesized into three categories of publicly available stress datasets, machine learning, and future research directions. Results. A wide variety of study-specific test and measurement protocols were noted in the literature. A number of public datasets were identified that are labeled for stress detection. In addition, we discuss that previous works show shortcomings in areas such as their labeling protocols, lack of statistical power, validity of stress biomarkers, and generalization ability. Conclusion. Generalization of existing machine learning models still require further study, and research in this area will continue to provide improvements as newer and more substantial datasets become available for study.

Gideon Vos, Kelly Trinh, Zoltan Sarnyai et al.

Introduction. We investigate the generalization ability of models built on datasets containing a small number of subjects, recorded in single study protocols. Next, we propose and evaluate methods combining these datasets into a single, large dataset. Finally, we propose and evaluate the use of ensemble techniques by combining gradient boosting with an artificial neural network to measure predictive power on new, unseen data. Methods. Sensor biomarker data from six public datasets were utilized in this study. To test model generalization, we developed a gradient boosting model trained on one dataset (SWELL), and tested its predictive power on two datasets previously used in other studies (WESAD, NEURO). Next, we merged four small datasets, i.e. (SWELL, NEURO, WESAD, UBFC-Phys), to provide a combined total of 99 subjects,. In addition, we utilized random sampling combined with another dataset (EXAM) to build a larger training dataset consisting of 200 synthesized subjects,. Finally, we developed an ensemble model that combines our gradient boosting model with an artificial neural network, and tested it on two additional, unseen publicly available stress datasets (WESAD and Toadstool). Results. Our method delivers a robust stress measurement system capable of achieving 85% predictive accuracy on new, unseen validation data, achieving a 25% performance improvement over single models trained on small datasets. Conclusion. Models trained on small, single study protocol datasets do not generalize well for use on new, unseen data and lack statistical power. Ma-chine learning models trained on a dataset containing a larger number of varied study subjects capture physiological variance better, resulting in more robust stress detection.

Gideon Vos, Liza van Eijk, Zoltan Sarnyai et al.

Machine Learning is transforming medical research by improving diagnostic accuracy and personalizing treatments. General ML models trained on large datasets identify broad patterns across populations, but their effectiveness is often limited by the diversity of human biology. This has led to interest in subject-specific models that use individual data for more precise predictions. However, these models are costly and challenging to develop. To address this, we propose a novel validation approach that uses a general ML model to ensure reproducible performance and robust feature importance analysis at both group and subject-specific levels. We tested a single Random Forest (RF) model on nine datasets varying in domain, sample size, and demographics. Different validation techniques were applied to evaluate accuracy and feature importance consistency. To introduce variability, we performed up to 400 trials per subject, randomly seeding the ML algorithm for each trial. This generated 400 feature sets per subject, from which we identified top subject-specific features. A group-specific feature importance set was then derived from all subject-specific results. We compared our approach to conventional validation methods in terms of performance and feature importance consistency. Our repeated trials approach, with random seed variation, consistently identified key features at the subject level and improved group-level feature importance analysis using a single general model. Subject-specific models address biological variability but are resource-intensive. Our novel validation technique provides consistent feature importance and improved accuracy within a general ML model, offering a practical and explainable alternative for clinical research.

Gideon Vos, Maryam Ebrahimpour, Liza van Eijk et al.

Understanding how emotional expression in language relates to brain function is a challenge in computational neuroscience and affective computing. Traditional neuroimaging is costly and lab-bound, but abundant digital text offers new avenues for emotion-brain mapping. Prior work has largely examined neuroimaging-based emotion localization or computational text analysis separately, with little integration. We propose a computational framework that maps textual emotional content to anatomically defined brain regions without requiring neuroimaging. Using OpenAI's text-embedding-ada-002, we generate high-dimensional semantic representations, apply dimensionality reduction and clustering to identify emotional groups, and map them to 18 brain regions linked to emotional processing. Three experiments were conducted: i) analyzing conversational data from healthy vs. depressed subjects (DIAC-WOZ dataset) to compare mapping patterns, ii) applying the method to the GoEmotions dataset and iii) comparing human-written text with large language model (LLM) responses to assess differences in inferred brain activation. Emotional intensity was scored via lexical analysis. Results showed neuroanatomically plausible mappings with high spatial specificity. Depressed subjects exhibited greater limbic engagement tied to negative affect. Discrete emotions were successfully differentiated. LLM-generated text matched humans in basic emotion distribution but lacked nuanced activation in empathy and self-referential regions (medial prefrontal and posterior cingulate cortex). This cost-effective, scalable approach enables large-scale analysis of naturalistic language, distinguishes between clinical populations, and offers a brain-based benchmark for evaluating AI emotional expression.

Gideon Vos, Liza van Eijk, Zoltan Sarnyai et al.

Synaptic pruning in biological brains removes weak connections to improve efficiency. In contrast, dropout regularization in artificial neural networks randomly deactivates neurons without considering activity-dependent pruning. We propose a magnitude-based synaptic pruning method that better reflects biology by progressively removing low-importance connections during training. Integrated directly into the training loop as a dropout replacement, our approach computes weight importance from absolute magnitudes across layers and applies a cubic schedule to gradually increase global sparsity. At fixed intervals, pruning masks permanently remove low-importance weights while maintaining gradient flow for active ones, eliminating the need for separate pruning and fine-tuning phases. Experiments on multiple time series forecasting models including RNN, LSTM, and Patch Time Series Transformer across four datasets show consistent gains. Our method ranked best overall, with statistically significant improvements confirmed by Friedman tests (p < 0.01). In financial forecasting, it reduced Mean Absolute Error by up to 20% over models with no or standard dropout, and up to 52% in select transformer models. This dynamic pruning mechanism advances regularization by coupling weight elimination with progressive sparsification, offering easy integration into diverse architectures. Its strong performance, especially in financial time series forecasting, highlights its potential as a practical alternative to conventional dropout techniques.

Gideon Vos, Maryam Ebrahimpour, Liza van Eijk et al.

Electroencephalogram (EEG) data is crucial for diagnosing mental health conditions but is costly and time-consuming to collect at scale. Synthetic data generation offers a promising solution to augment datasets for machine learning applications. However, generating high-quality synthetic EEG that preserves emotional and mental health signals remains challenging. This study proposes a method combining correlation analysis and random sampling to generate realistic synthetic EEG data. We first analyze interdependencies between EEG frequency bands using correlation analysis. Guided by this structure, we generate synthetic samples via random sampling. Samples with high correlation to real data are retained and evaluated through distribution analysis and classification tasks. A Random Forest model trained to distinguish synthetic from real EEG performs at chance level, indicating high fidelity. The generated synthetic data closely match the statistical and structural properties of the original EEG, with similar correlation coefficients and no significant differences in PERMANOVA tests. This method provides a scalable, privacy-preserving approach for augmenting EEG datasets, enabling more efficient model training in mental health research.