Brain-Computer Interface (BCI)-Based Rehabilitation for Patients with Prolonged Disorders of Consciousness (pDoC)

PhD Qualifying Examination


Title: "Brain-Computer Interface (BCI)-Based Rehabilitation for Patients 
with Prolonged Disorders of Consciousness (pDoC)"

by

Mr. Jianming WANG


Abstract:

The growing population of patients with prolonged disorders of 
consciousness (pDoC) underscores the urgent need to refine assessment and 
treatment paradigms during the early post-injury stage. Current clinical 
practice faces fundamental limitations. Behavioral scales, such as the Coma 
Recovery Scale—Revised (CRS-R), are subjective and prone to 
misdiagnosis. Neurophysiological and neuroimaging biomarkers are objective 
but lack interpretability and clinical integration. This gap renders 
neuro-modulation therapies largely empirical and non-personalized.

This survey aims to explore the paradigm shift led by intelligent 
brain-computer interface (BCI) systems. These are based on multi-modal 
data fusion and deep learning. The survey examines the current state of 
research. It highlights that machine learning models in this field are 
often seen as "black boxes" and are further constrained by data 
heterogeneity and limited sample sizes.

Furthermore, this survey outlines future directions for research. It 
focuses on developing multimodal and interpretable systems capable of 
real-time consciousness monitoring and personalized treatment optimization. 
Central to realize this vision is overcoming key challenges, including 
multi-modal data alignment, improved model interpretability, and dynamic 
state tracking. Next-generation BCIs are expected to bridge the gap 
between subjective and objective assessments. They will provide 
data-driven, precise, and interpretable decision support for the 
rehabilitation and long-term care of patients with pDoC.


Date:                   Friday, 5 December 2025

Time:                   2:00pm - 4:00pm

Venue:                  Room 3494
                        Lift 25/26

Committee Members:      Prof. Nevin Zhang (Supervisor)
                        Prof. Gary Chan (Chairperson)
                        Dr. Xiaomin Ouyang