Management of post-stroke depression (PSD) by electroencephalography for effective rehabilitation

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.

Keywords

• Post-stroke depression
• Stroke rehabilitation
• Electroencephalography
• Machine learning
• Brain-computer interface