Brain-computer interface for position control of a neuro-prosthetic artificial limb

Abstract

Brain Computer Interface (BCI), sometimes called a Brain Machine interface (BMI), is a direct communication link between the brain's electrical activity and an external device, most commonly a computer or robotic limb, bypassing traditional neuromuscular routes. BCIs are often directed at researching, mapping, assisting, augmenting, or repairing human cognitive or sensory-motor functions. They are often conceptualized as a human–machine interface that skips the intermediary of moving body parts although they also raise the possibility of erasing the distinction between brain and machine. Brain-Computer Interface (BCI) technology has emerged as a revolutionary approach for controlling neuroprosthetic devices, offering a new lease of functional independence to individuals with severe motor impairments. Sophisticated signal processing and machine learning algorithms decode these signals into precise control commands, facilitating intuitive and responsive prosthetic movement. Current research focuses on enhancing the accuracy, reliability, and usability of BCI-controlled neuroprosthetics. Innovations in high-resolution signal acquisition, noise reduction, and real-time processing are critical to improving system performance. Integrating sensory feedback mechanisms, such as haptic feedback, is a promising area of development, aimed at providing users with real-time information about the prosthetic's interaction with the environment. This feedback is crucial for tasks requiring fine motor control and can significantly enhance the user experience by making the control of the prosthetic arm more natural and intuitive. Personalization and adaptability of BCI systems are also key research areas, with adaptive algorithms that learn and adjust to the user’s unique neural patterns over time. The development of wireless and wearable BCI devices aims to increase mobility and ease of use, making these systems more practical for everyday applications. Additionally, interdisciplinary collaboration among neuroscientists, engineers, and clinicians is essential to advance the field, along with addressing ethical and regulatory considerations related to data privacy, user consent, and long-term impacts. This Thesis presents a 2-loop architecture of brain-actuated position control system of a robot arm, realized with a Takagi-Sugeno type fuzzy speed modulator placed in the outer loop and a traditional speed-controller placed in the inner loop. Chapter 1 provides a detailed review on the Brain-Computer Interface (BCI) technology. It begins with a classification BCI-based controllers, and gradually explores different types of standard BCI system along with their control policies. The different modules of a closed-loop BCI are discussed in brief with their current state-of-the-art approaches. It also provides an outline of the electroencephalography (EEG)-based brain signals commonly used in closed-loop BCI-based robot control tasks. Chapter 2 provides a 2-loop position control scheme is proposed here with an outer loop for position control of the motor and the inner loop for speed-setting of a robotic limb. Three distinct models of brain-actuated speed-setting with possible control policies and stability analysis are presented. The analysis reveals that Takagi–Sugeno type fuzzy control yields the best results. Additionally, cognitive load is also reduced significantly as the user needs to generate error-related potential once only to identify the position of the targeted object he needs to translate. Traditional BCI-based position control schemes utilize acquired motor imagery signals of the subject for motor actuation and error-related potential signals to detect positional overshoots. The positional overshoot results in steady-state positional error. Current BCI research attempts to reduce steady-state errors at the cost of high cognitive load of the subjects. The motivation here is to reduce the steady-state positional error without enhancing cognitive load of the subjects. Chapter 3 provides the designing of Brain-Actuated Speed Modulator along with the corresponding Simulink Models and plots. Finally, Chapter 4 ends with a brief conclusion along with future scope and innovations. The future scope of BCI-based neuroprosthetic control is vast, with the potential to significantly enhance the quality of life for individuals with disabilities. By continuing to refine the technology and addressing current challenges, BCIs can offer more precise, intuitive, and accessible solutions for neuroprosthetic control, paving the way for broader adoption and improved outcomes in clinical and everyday settings.