Why are you working on the secondary problem of depression when solving the primary problem of 100% recovery eliminates the need for such research?
1 Department of Biomedical Engineering, The Hong Kong Polytechnic University, Hong Kong, China
- 2 National
Research Centre for Rehabilitation Technical Aids Beijing, Beijing Key
Laboratory of Rehabilitation Technical Aids for Old‐Age Disability,
Beijing, China
- 3 University
Research Facility in Behavioral and Systems Neuroscience (UBSN), The
Hong Kong Polytechnic University, Hong Kong, China
- 4 The Hong Kong Polytechnic University Shenzhen Research Institute, Shenzhen, China
- 5 Research Institute for Smart Ageing (RISA), The Hong Kong Polytechnic University, Hong Kong, China
Received 5 August 2022, Revised 24 November 2022, Accepted 28 November 2022, Available online 30 November 2022.
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Highlights
- •
In
this study, we provided a unique and systematic review on the most
related literature of the clinical applications of EEG for PSD and
offered a cross-section that is useful for determining optimal
practices.
- •
Post-stroke
depression (PSD) has significant negative impacts on the daily life of
stroke survivors and delays their neurologic recovery.
- •
However,
the traditional post-stroke rehabilitation mainly focused on motor
restoration, whereas little attention was given to the affective
deficits.
- •
The
aims of the study were to gather EEG based empirical evidence for PSD
diagnosis, to review interventions for managing PSD, and to analyse the
evaluation approaches.
- •
The
review showed the needs for understanding the cortical responses of
PSD, in order to improve its diagnosis and precision treatment.
Abstract
Post-stroke
depression (PSD) has negative impacts on the daily life of stroke
survivors and delays their neurological recovery. However, traditional
post-stroke rehabilitation mainly focused on motor restoration, whereas
little attention was given to the affective deficits. Effective
management of PSD, including diagnosis, intervention, and follow-ups, is
essential for post-stroke rehabilitation. As an objective measurement
of the nervous system, electroencephalography (EEG) has been applied to
the diagnosis and evaluation of PSD. In this paper, we reviewed the
literature most related to the clinical applications of EEG for PSD and
offered a cross-section that is useful for selecting appropriate
approaches in practice. This study aimed to gather EEG-based empirical
evidence for PSD diagnosis, review interventions for managing PSD, and
analyze the evaluation approaches. In total, 33 diagnostic studies and
19 intervention studies related to PSD and depression were selected from
the literature. It was found that the EEG features analyzed by both
band-based and nonlinear dynamic approaches were capable of quantifying
the abnormal neural responses on the cortical level for PSD diagnosis
and intervention evaluation/prediction. Meanwhile, EEG-based machine
learning has also been applied to the diagnosis and evaluation of
depression to automate and speed up the process, and the results have
been promising. Although brain-computer interface (BCI) interventions
have been widely applied to post-stroke motor rehabilitation and
cognitive training, BCI emotional training has not been directly used in
PSD yet. This review showed the need for understanding the cortical
responses of PSD to improve its diagnosis and precision treatment. It
also revealed that future post-stroke rehabilitation plans should
include training sessions for motor, affect, and cognitive functions and
closely monitor their improvements.
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