Integrating Virtual Reality, Electrodermal Activity and Machine Learning for the Early Detection of Mild Cognitive Impairment

Brand-Patient, R ORCID: 0000-0003-2002-8740 (2026) Integrating Virtual Reality, Electrodermal Activity and Machine Learning for the Early Detection of Mild Cognitive Impairment. Doctoral thesis, Liverpool John Moores University.

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Abstract

Mild Cognitive Impairment (MCI) is an early stage between normal ageing and dementia, where recognising subtle changes in memory and thinking can enable timely treatment and lifestyle interventions. This thesis develops and evaluates an integrated framework that combines Virtual Reality (VR), Electrodermal Activity (EDA) sensing, and Machine Learning (ML) to support early, non-invasive, and user friendly detection of cognitive decline (CD).

The system comprises two components: the MCIVR application, which uses vir tual environments to engage participants in cognitive tasks while measuring physiological signals, and the MCIFR framework, which applies Explainable Artificial Intelligence (XAI) to interpret these signals and generate personalised risk insights. The MCIVR system was iteratively refined through calibration sessions and Unity-based testing to ensure accurate physiological capture and reliable task interaction.

A usability study with 50 ethically approved participants employed Oculus Quest 2 and Fitbit Sense 2 sensors to record multimodal data spanning physiological arousal, interaction performance, and self-reported workload. These datasets were benchmarked against Alzheimer’s Disease Neuroimaging Initiative (ADNI) data to validate predictive performance. Data processing included preprocessing, feature engineering, and classification within an XAI pipeline, using SHapley Additive ex Planations (SHAP) for interpretability.

Findings show that combining VR and EDA features improves MCI detection compared with single-modality models. Participants rated the MCIVR system above industry usability standards, confirming feasibility and engagement. Physiological responses varied with the virtual environment, and explainability analyses high lighted clinically plausible markers, such as stress- and memory-related features. The study identifies posture, environment, and calibration precision as important design factors for effective VR-based Cognitive Assessment (CA). It demonstrates the potential of affordable consumer devices to support multimodal screening for cognitive decline. Overall, this research advances the integration of VR, EDA, and ML technologies and offers a reproducible, interpretable, and practical pathway towards earlier and more accessible cognitive health assessments.

Item Type:	Thesis (Doctoral)
Uncontrolled Keywords:	Mild Cognitive Impairment (MCI); Machine Learning (ML); Virtual Reality (VR); Electrodermal Activity (EDA); Cognitive Health Assessment; Early Detection of Cognitive Decline; Multimodal Data Fusion; Physiological Signal Analysis; Explainable Artificial Intelligence (XAI); SHAP Interpretability; Feature Ranking Systems; Human–Computer Interaction (HCI); Usability Evaluation; NASA-TLX Cognitive Workload; Mixed-Methods Research; Cognitive Task Simulation; Behavioural and Physiological Analytics; Digital Biomarkers; Wearable Sensors; Human–Computer Interaction
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software Q Science > QP Physiology R Medicine > RC Internal medicine > RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry
Divisions:	Computer Science and Mathematics
Date of acceptance:	26 February 2026
Date of first compliant Open Access:	19 March 2026
Date Deposited:	19 Mar 2026 09:34
Last Modified:	19 Mar 2026 09:35
DOI or ID number:	10.24377/LJMU.t.00028230
Supervisors:	Kolivand, H, Aldhaibani, O, Topham, L and Hurst, W
URI:	https://researchonline.ljmu.ac.uk/id/eprint/28230

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