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An Adaptive Closed-loop Control Based Machine Learning For Rehabilitation Parkinson’s Patients
Saeed Mollahoseini Paghale, Milad Fallahzade, Mohammadreza Amirseyfadini,
Volume 23, Issue 30 (1-2026)
Abstract
Background and Aims: Controlling hand tremors in neurological disorders like Parkinson's has gotten a lot of attention in recent decades. The number of theories about closed-loop deep brain stimulation is rapidly growing. The goal of this work is to offer a machine learning-based automated closed loop system for the rehabilitation of Parkinson's patients with hand tremor symptoms.
Materials and Methods: In the current study, vibration was simulated using a mathematical model that included a muscle model, basal ganglia, cortex, and supplementary motor area. To manage hand tremor, the non-integer PID proportional controller, as well as the intelligent Proximal Policy Optimization (PPO) algorithm as a subset of reinforcement learning, are employed to adapt the coefficients.
 Results: One of the advantages of the proposed method, aside from reducing hand tremor and automatic learning to use at various levels of the disease, which has yielded acceptable results when compared to other control methods, is its practical implementation in the real world due to the simplicity of the controller. The automatic adjustment of artificial intelligence network coefficients in the presented strategy (PPO) makes it simple to create intelligent system.
Conclusion: The proposed intelligent system significantly reduces the side effects of continuous brain stimulation in the open-loop manner stimulation, in addition to optimizing output signals such as hand tremor compared to other controllers and being usable for all levels of the disease due to its adaptability.

Effects Of 10-Session Dual Transcranial Direct Current Stimulation (TDCS) On Professional Swimmers Performance, Mental Toughness And Perceived Exertion
Mina Khantan, Behrouz Abdoli, Alireza Farsi,
Volume 23, Issue 30 (1-2026)
Abstract
Aim: Transcranial direct current stimulation (tDCS) is one of the newest methods in order to improve the athletic performance and mental preparation of professional athletes. In this study, we investigated the effects of 10-session unihemispheric concurrent dual-site anodal-tDCS (a-tDCS) of the primary motor cortex (M1) and dorsolateral prefrontal cortex (DLPFC), on swimming performance, mental toughness (MT) and perceived exertion.
Methods: 20 male professional swimmers (Age: 19.00±2.86 yrs) were randomly divided into tDCS (n=10) and sham (n=10) groups. On the first day, the Mental Toughness Questionnaire (SMTQ), next day, the 100-meter freestyle swimming performance test, and the rate of perceived exertion scale (RPE) were evaluated as pre-tests. From the third day, 10 sessions of tDCS were applied, each session a current of 2mA for 20 min, half an hour after the usual swimming exercise, three days a week. 48 hrs following 10th session of tDCS, evaluations were repeated. ANCOVA was used for statistical analysis.
Result: After 10 sessions of tDCS, swimming performance improved significantly; The total MT score increased significantly and no significant change was observed in RPE.
Conclusion: Based on this, multi-session tDCS combined with regular training is recommended to improve swimmers performance and psychological aspects that could be considered as a brain conditioning method to increase mental toughness and sports performance.

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