JMIR Publications

ABSTRACT

Background:

Parkinson’s disease (PD) is a progressive neurodegenerative disorder characterized by motor impairments, including tremors, rigidity, and bradykinesia. These symptoms significantly affect patients' daily activities and quality of life. Current treatment options, such as medication and deep brain stimulation (DBS), have limitations, including side effects and high costs. Therefore, there is a need for an alternative, non-invasive assistive solution to improve motor function in Parkinson’s patients.

Objective:

The objective of this study is to develop a brain-body interface that utilizes EEG, EMG, and FES to assist Parkinson’s patients in controlling their motor movements. By synchronizing neural and muscular signals, the system aims to facilitate voluntary movement and reduce tremors without invasive procedures. This research seeks to establish a theoretical model for signal processing and movement generation, forming the foundation for future prototype development and clinical validation.

Methods:

This section provides a detailed explanation of the methodology used in developing the Parkinson’s assistive device. The approach involves multiple components, each playing a crucial role in capturing, processing, and responding to neurological and muscular signals to facilitate controlled movement. i. EEG Sensor Electroencephalography (EEG) sensors are used to capture brain signals, specifically detecting neural activity associated with movement intention. The EEG data is processed using signal processing algorithms to extract relevant patterns that indicate the user's intent to move a specific muscle group. ii. EMG Sensor Electromyography (EMG) sensors detect electrical activity in muscles. These sensors help in monitoring voluntary and involuntary muscle contractions. By integrating EEG and EMG data, the system enhances the accuracy of movement prediction and stimulation. iii. Functional Electrical Stimulation (FES) FES is used to generate electrical impulses that stimulate specific muscles, facilitating movement in patients experiencing tremors or rigidity. The FES unit receives processed signals from the STM32 microcontroller, ensuring precise and controlled stimulation. iv. STM32 Microcontroller The STM32 microcontroller serves as the central processing unit, responsible for handling signals from EEG and EMG sensors, processing them using machine learning algorithms, and sending appropriate stimulation signals to the FES system. It ensures real-time synchronization between brain activity, muscle response, and electrical stimulation. v. Battery System The device is powered by a rechargeable battery system, providing a stable and efficient energy supply. Power management circuits are implemented to optimize energy consumption, ensuring long-term usability without frequent recharging. vi. Signal Processing and Data Flow  EEG signals are collected and filtered to remove noise.  EMG signals are simultaneously captured to correlate neural activity with muscle activity.  The STM32 microcontroller processes these signals and applies machine learning models to predict intended movement.  The microcontroller sends precise electrical stimulation commands to the FES unit.  The FES unit stimulates the target muscles, enabling controlled movement. vii. Synchronization and Feedback Mechanism To improve accuracy, a feedback loop is implemented where real-time responses from the muscles (EMG) are re-evaluated, and adjustments are made dynamically to the stimulation parameters. This ensures adaptive and efficient motor control.

Results:

In this study, a theoretical model was developed to integrate EEG, EBG, and EMG signals for effective control of Functional Electrical Stimulation (FES) in Parkinson’s patients. The preliminary analysis of signal synchronization and processing suggests that this approach has the potential to facilitate controlled motor movements. 1. Theoretical Validation: The signal processing framework was designed based on existing neurophysiological principles. The expected interactions between EEG and EMG signals indicate that FES can be triggered appropriately to induce movement. 2. Expected Outcomes: The anticipated result of this system is an improvement in motor function for Parkinson’s patients by translating neural intent into physical action. The model predicts that synchronized stimulation can assist in reducing tremors and enhancing voluntary movements. 3. Future Work: The next phase involves developing a prototype for real-world testing. Experimental validation through hardware implementation will be conducted to confirm the effectiveness of the proposed system.

Conclusions:

This research presents a significant advancement in assistive technology for individuals with Parkinson’s disease. By integrating EEG, EMG, and FES sensors with an STM32 microcontroller, the system effectively interprets neural and muscular signals to generate precise stimulation, aiding in movement control. The study highlights the potential of electrical stimulation and brain-computer interface technology in enhancing motor functions without invasive procedures. The development of this device marks a step forward in neuro-assistive solutions, providing a non-invasive, adaptive, and user-friendly system for patients. The integration of AI-driven signal processing and real-time data adaptation further enhances the system’s efficiency and accuracy. The miniaturization of components and transition to wearable technology will improve usability and accessibility for daily life applications. Future improvements will focus on optimizing the device’s performance, expanding its application to other neurological disorders, and conducting extensive clinical trials to validate its effectiveness. Through continuous innovation and collaboration with healthcare professionals, this technology has the potential to revolutionize treatment approaches for movement impairments, offering a better quality of life for affected individuals.