A Pilot Study on the Comparison of Prefrontal Cortex Activities of Robotic Therapies on Elderly with Mild Cognitive Impairment

Demographic shifts have led to an increase in mild cognitive impairment (MCI), and this study investigates the effects of cognitive training (CT) and reminiscence therapy (RT) conducted by humans or socially assistive robots (SARs) on prefrontal cortex activation in elderly individuals with MCI, aiming to determine the most effective therapy-modality combination for promoting cognitive function. This pilot study employs a randomized control trial (RCT) design. Additionally, the study explores the efficacy of Reminiscence Therapy (RT) in comparison to Cognitive Training (CT). Eight MCI subjects, with a mean age of 70.125 years, were randomly assigned to ``human-led'' or ``SAR-led'' groups. Utilizing Functional Near-infrared Spectroscopy (fNIRS) to measure oxy-hemoglobin concentration changes in the dorsolateral prefrontal cortex (DLPFC), the study found no significant differences in the effects of human-led and SAR-led cognitive training on DLPFC activation. However, distinct patterns emerged in memory encoding and retrieval phases between RT and CT, shedding light on the impacts of these interventions on brain activation in the context of MCI.

Dynamic Ensemble Bayesian Filter for Robust Control of a Human Brain-machine Interface

Objective: Brain-machine interfaces (BMIs) aim to provide direct brain control of devices such as prostheses and computer cursors, which have demonstrated great potential for mobility restoration. One major limitation of current BMIs lies in the unstable performance in online control due to the variability of neural signals, which seriously hinders the clinical availability of BMIs. Method: To deal with the neural variability in online BMI control, we propose a dynamic ensemble Bayesian filter (DyEnsemble). DyEnsemble extends Bayesian filters with a dynamic measurement model, which adjusts its parameters in time adaptively with neural changes. This is achieved by learning a pool of candidate functions and dynamically weighting and assembling them according to neural signals. In this way, DyEnsemble copes with variability in signals and improves the robustness of online control. Results: Online BMI experiments with a human participant demonstrate that, compared with the velocity Kalman filter, DyEnsemble significantly improves the control accuracy (increases the success rate by 13.9% and reduces the reach time by 13.5% in the random target pursuit task) and robustness (performs more stably over different experiment days). Conclusion: Our results demonstrate the superiority of DyEnsemble in online BMI control. Significance: DyEnsemble frames a novel and flexible framework for robust neural decoding, which is beneficial to different neural decoding applications.