Multimodal biofeedback for Parkinson’s disease motor and nonmotor symptoms

Abstract

Parkinson’s disease (PD) is a neurodegenerative disorder characterized by motor retardation, myotonia, quiescent tremor, and postural gait abnormality, as well as nonmotor symptoms such as anxiety and depression. Biofeedback improves motor and nonmotor functions of patients by regulating abnormal electroencephalogram (EEG), electrocardiogram (ECG), photoplethysmography (PPG), electromyography (EMG), respiration (RSP), or other physiological signals. Given that multimodal signals are closely related to PD states, the clinical effect of multimodal biofeedback on patients with PD is worth exploring. Twenty-one patients with PD in Beijing Rehabilitation Hospital were enrolled and divided into three groups: multimodal (EEG, ECG, PPG, and RSP feedback signal), EEG (EEG feedback signal), and sham (random feedback signal), and they received biofeedback training five times in two weeks. The combined clinical scale and multimodal signal analysis results revealed that the EEG group significantly improved motor symptoms and increased Berg balance scale scores by regulating β band activity; the multimodal group significantly improved nonmotor symptoms and reduced Hamilton rating scale for depression scores by improving θ band activity. Our preliminary results revealed that multimodal biofeedback can improve the clinical symptoms of PD, but the regulation effect on motor symptoms is weaker than that of EEG biofeedback.