https://www.mdu.se/

Stroke is a major cause of long-term disability, often resulting in severe motor impairments. Motor imagery has been proposed as a technique to engage motor networks and promote neuroplasticity, even in the absence of physical movement. Brain–computer interfaces (BCIs) can decode neural activity and provide feedback in a closed loop, supporting reinforcement-based motor learning. BCIs have shown modest but promising results, and their efficiency and usability can be improved by understanding how design choices influence human performance and clinical outcomes. Key challenges include achieving high signal precision through high-dimensional data, delivering real-time feedback under strict latency constraints, and managing non-stationary brain signals within and across sessions. This thesis presents an iterative and exploratory investigation of these challenges. Methods were first developed and validated in healthy participants and later implemented in a clinical intervention with stroke patients. Contributions include classification of motor imagery tasks involving one hand, characterization of motor signals in high-dimensional data, evaluation of system latency for real-time feedback, application of multivariate pattern analysis to capture temporal dynamics of the motor signal, introduction of a continuous metric for motor signal strength linked to visual representations, implementation of a method to set and maintain difficulty level during BCI training, evaluation of adaptive classification methods to maintain performance across sessions, and validation of these methods in a stroke case study. The findings advance understanding of how BCI design parameters affect usability and clinical relevance. By integrating neuroscience, machine learning, and principles of motor learning and neuroplasticity, this work contributes to the development of more effective and personalized BCI systems for stroke rehabilitation.

Loss of motor function after stroke substantially impacts the day-to-day life. In addition, the rehabilitation process is demanding and can require difficult physical exercises. Motor Imagery (MI), a technique where the user imagines the sensation of performing a movement, has been shown to activate similar brain activity as physically performing the movement. Brain Computer Interfaces (BCIs) with MI-based neurofeedback is a promising approach for rehabilitating loss of motor function without the use of physical exercise. For this technology to be used in rehabilitation, it is crucial that the neurofeedback reflects underlying neural activity that is functionally relevant for motor recovery. To this end, I believe personalisation is key. Therefore, in this thesis I have investigated important components for personalisation of a MI-BCI system for post-stroke rehabilitation. Specifically, I have 1) explored a distance-to-bound approach for adapting the BCI task difficulty, 2) investigated the impact of continual visual feedback on MI-related cortical activity, and 3) analysed the feasibility of extracting a novel motor-related feature.

I suggest that adjusting the difficulty of an MI-BCI, using distance-to-bound, targets stronger Event Related Desynchronisation (ERD) during neurofeedback training. However, strong ERD does not directly correlate with improved motor function, highlighting the importance for personalisation. I further provide evidence, in a group of stroke patients,  that continual visual feedback does not interfere with the MI-related cortical activity, nor does it lead to stronger activity. Finally, I show that transient heterogenous features in the beta band, so called beta bursts can be extracted from both the lesioned and healthy hemispheres of stroke patients. These results contribute both methodologically and scientifically in building a stronger foundation of knowledge for the development of MI-BCI rehabilitation after stroke.

However, there is still need for more research to fully understand the complexity of neurofeedback stroke rehabilitation. I believe this thesis sheds light on important considerations when developing the different components of a BCI designed to promote motor recovery after stroke.