Research

Research at the BRAIN Lab

OVERVIEW

The BRAIN Lab at the University of Santo Tomas advances interdisciplinary research at the intersection of neuroscience, engineering, artificial intelligence, and healthcare. Our work is centered on three interconnected research areas: Computational Neuroscience, Neurorehabilitation and Assistive Technologies, and Neurodegenerative and Neuropsychiatric Disorders.

We develop and investigate innovative approaches for understanding, monitoring, and modulating brain and physiological function, integrating techniques such as neuroimaging and electrophysiology, wearable and multimodal sensing, neuromodulation, brain–computer interfaces, and AI-driven data analytics. Through the integration of computational methods with experimental and translational research, we aim to transform complex neural and physiological signals into technologies that can support more personalized, adaptive, and accessible approaches to neurological and mental healthcare.

At the core of the BRAIN Lab is a commitment to translational and human-centered neurotechnology—bridging fundamental neuroscience with engineering innovation to develop solutions that can ultimately improve brain health, functional independence, and quality of life.

Research Areas

Computational Neuroscience

Our research in Computational Neuroscience focuses on developing and applying computational models, advanced signal processing, data analytics, and artificial intelligence to better understand the complex dynamics of the brain. We investigate how neural activity encodes and processes information, how patterns of brain activity relate to behavior and cognitive function, and how these patterns change across different physiological and neurological conditions.

By integrating neuroimaging, electrophysiological and multimodal physiological data with computational and machine learning approaches, we aim to uncover meaningful patterns in brain function and translate these insights into new approaches for brain monitoring, neurotechnology, and personalized interventions.

Neurorehabilitation and Assistive Technologies

Our research in Neurorehabilitation and Assistive Technologies focuses on developing innovative, technology-driven approaches to support the recovery, functional independence, and quality of life of individuals with neurological and cognitive impairments. We explore neuromodulation, brain–computer interfaces, wearable sensing, neurofeedback, and multimodal rehabilitation technologies to create more personalized and responsive interventions.

By integrating neural and physiological monitoring with artificial intelligence and adaptive systems, we aim to develop intelligent rehabilitation technologies that can respond to an individual’s changing needs and ultimately enable more personalized, accessible, and effective neurorehabilitation.

Neurodegenerative and Neuropsychiatric Disorders

Our research in Neurodegenerative and Neuropsychiatric Disorders seeks to better understand the neural and physiological changes associated with these conditions and translate this knowledge into improved approaches for early detection, monitoring, intervention, and rehabilitation. We investigate biomarkers and patterns derived from brain activity, physiological signals, behavior, and other multimodal data to characterize disease-related changes and individual responses to intervention.

Through the integration of neurotechnology, wearable sensing, neuromodulation, and artificial intelligence, we aim to develop innovative and personalized approaches that can support earlier identification, more effective interventions, and improved cognitive function, mental well-being, and quality of life.

Current Projects

The BRAIN Lab is actively engaged in interdisciplinary research that brings together neuroscience, artificial intelligence, wearable sensing, and neurotechnology to address challenges in brain health, rehabilitation, and human performance. Our current research initiatives include:

AI-Driven Neurorehabilitation for Dementia and Mild Cognitive Impairment
- - Our flagship research program develops intelligent, multimodal technologies for monitoring and rehabilitating older adults with early-stage dementia and mild cognitive impairment (MCI). The project integrates EEG, wearable physiological sensing, and multimodal interventions—including neuromodulation, music-based interventions, and movement-based rehabilitation. By analyzing patterns in neural and physiological responses, we are developing AI-driven closed-loop approaches that can adapt intervention and stimulation parameters to individual responses. This work is supported by the Department of Science and Technology–Philippine Council for Health Research and Development (DOST-PCHRD).
Intelligent and Adaptive Neuromodulation
- - We investigate non-invasive and multimodal neuromodulation approaches for influencing neural activity and supporting cognitive and neurological rehabilitation. Our work explores technologies such as photobiomodulation, thermal and vibrotactile stimulation, auditory stimulation, and neurofeedback, with an emphasis on developing personalized and adaptive stimulation strategies informed by real-time neural and physiological signals.
Closed-Loop Music-based Neurotherapy
- - We explore how music and auditory stimulation can be integrated with neurophysiological monitoring and artificial intelligence to develop responsive interventions for cognitive and emotional well-being. Our research investigates how EEG and physiological responses can be used to characterize individual responses to music and ultimately enable adaptive, closed-loop music-based interventions.
Wearable Neurophysiology and Human Performance
- - We develop and apply wearable and multimodal sensing technologies to understand how the brain and body respond to stress, cognitive demands, and challenging real-world environments. By integrating physiological signals such as heart rate variability, electrodermal activity, movement, and temperature with computational analysis, we seek to identify meaningful markers of stress, cognitive state, and human performance and inform more effective monitoring and intervention strategies.

Across these projects, the BRAIN Lab works toward a common goal: developing intelligent, adaptive, and human-centered neurotechnologies that translate advances in neuroscience and engineering into practical solutions for brain health and well-being.

Collaborations

The BRAIN Lab collaborates with leading institutions, industry partners, and researchers worldwide. Our collaborative projects aim to accelerate discoveries and translate research into real-world applications. The Lab's current and past collaborators are as follows:

National Center for Geriatric Health (NCGH) - Philippines
UST Faculty of Medicine and Surgery - Philippines
UST Conservatory of Music - Philippines
UST Hospital - Philippines
Chung Yuan Christian University (CYCU) - Taiwan
Imperial College London - United Kingdom
The Hong Kong University of Science and Technology (HKUST) - Hong Kong

Publications

We are committed to sharing our findings with the scientific community and the public. Our publications reflect the breadth and depth of our research efforts.

Preprints (Under Processing)

Amyloid pathology reduces dynamic range and disrupts neural coding in a mouse model of Alzheimer’s Disease (co-authored with Imperial College London)

Progressive Physiological Modulation in Late-Stage Dementia Patients via Music Therapy: Evidence from Manifold Learning

2026

Early spatial and contextual coding deficits in hippocampal CA1 precede performance decline in an Alzheimer’s disease model (co-authored with Imperial College London, University of Melbourne, and the Chinese Academy of Sciences)

2025

Characterization of Physiological, Psychological, and Physical Stress Responses from Wearable Technology

2024

Characterizing the Effects of Different Music Genres in Brain Circuit Dynamics of Patients with Depression using Neural Manifold Learning

Understanding the Effects of Naturalistic Music Listening in Brain Circuit Dynamics using Neural Manifold Learning in Patients with Alzheimer's Disease

2023

EEG-based characterization and classification of severity for the diagnosis of post-traumatic stress disorder (PTSD)