Error-related potentials: a novel approach to a real-time human-in-the-loop robotic stroke rehabilitation system
Creators: name: Kumar, Akshay
Publisher: name: RMIT University
Year: 2024
Subjects: classificationCode: 400309
subject: Neural engineering
classificationCode: 400310
subject: Rehabilitation engineering
classificationCode: 461104
subject: Neural networks

description: Stroke is not only the leading cause of death in the world but also the main cause of functional disability. Stroke causes various types of body functional impairments, such as cognitive impairment, motor impairment, vision impairment, and speech and language impairment, which restrict the day-to-day activities of millions of stroke patients. Motor impairment limits stroke survivors' ability to move their arms and legs, and to execute activities of daily living (ADL). Conventionally, in hospitals, a rehabilitation therapist helps and guide the patient through the rehabilitation procedures. The main principle of stroke rehabilitation is to stimulate the plasticity of the brain through task-oriented rehabilitation exercises to recover the damaged function to a certain extent. However, due to a rapid increase in the number of stroke patients across the world and an inadequate number of rehabilitation therapists, stroke patients are often ineffectively guided in rehabilitation procedures. Assist-as-needed (AAN) robot therapy is gradually becoming a promising alternative to conventional rehabilitation training methods and can potentially help in meeting the rehabilitation demand. The main advantage of AAN robot therapy is its capability to assist patients only when required, which encourages patients to perform the rehabilitation exercises actively and repetitively and which has been linked with a higher rate of recovery from motor impairment. However, until now, state-of-the-art AAN robotic rehabilitation systems have failed to show any significant improvement in the recovery of upper-limb functional abilities in comparison to standard stroke rehabilitation treatment. A major shortcoming in the current AAN robotic procedures is the lack of advanced methods to find out the optimal time to modulate/trigger the external assistance in performing the rehabilitation exercise; which makes the AAN robot therapy less effective. 

Error-related potential (ErrP) signals, which are elicited in the human brain following the perception of an error, have been increasingly utilized in the development of a diverse range of rehabilitation and assistive applications including robotic devices for motor-impaired people. Nevertheless, regaining upper-limb movement through post-stroke rehabilitation is the highest priority of motor-impaired people, as it directly affects their ability to execute the activities of daily living. ErrP signals can form the optimal basis of modulating/triggering the assistance in an AAN robot therapy. However, in the context of AAN methods, the existence of ErrP is unexplored. Therefore, the primary objective of this thesis is to explore the possibility of using ErrP signals in developing an AAN robotic stroke rehabilitation system.

The thesis study was initiated by a comprehensive literature review on ErrP-based brain-computer interfaces (BCIs) for motor-impaired people. The review also included a discussion on potential issues and challenges being faced by the current state-of-the-art ErrP-based BCIs as well as critical future pathways and research directions, including the development of an ErrP-based AAN robotic stroke rehabilitation system. Following this, fifteen stroke patients' EEG and EMG data recorded during a rehabilitation experiment were gathered from the Jiaxing 2nd Hospital, Zhejiang province, China. The collected data were then analysed using various signal processing, statistical and machine learning techniques to achieve three primary objectives: (i) to determine whether an ErrP signal is evoked when a patient is trying to complete a physical rehabilitation exercise in a given time; (ii) to assess the feasibility of detecting the ErrP signals evoked in the novel task situation in real time and a single trial; and (iii) to assess how the states-of-mind of stroke patients can affect the amplitude of their error-related negativity signals. 

Finally, a conceptual design of the ErrP-based AAN robotic stroke rehabilitation system was developed and presented. The outcome of these investigations demonstrates the feasibility of using ErrP signals as a measure to trigger/modulate robotic assistance in a real-time AAN robot therapy. It is expected that, with the integration of ErrP signals into the feedback loop of AAN robot-therapy designs, robotic stroke rehabilitation programs can be made more engaging and minimally assistive; which thereby can improve state-of-the-art rehabilitation methods and, ultimately, the stroke patients' rate of recovery from motor impairments.