Public defence in Biomedical Engineering, Lic.Sc. (Tech) Hanna-Leena Halme
Title of the doctoral thesis: Methods for brain-computer interfaces utilizing MEG and motor imagery
Opponent: Professor Nathalie George, Institut du Cerveau - Paris Brain Institute (ICM), France
Custos: professori Lauri Parkkonen, Aalto University School of Science, Department of Neuroscience and Biomedical Engineering
The defence will be organized on campus and via Zoom: https://aalto.zoom.us/j/61933224381
The doctoral thesis will be publicly displayed 10 days before the defence in the publication archive of Aalto University.
Public defence announcement:
Brain–computer interfaces (BCI) are systems that measure the user’s brain activity, analyze it in real time and utilize it to define the stimuli given to the user. The users can thus control the presented feedback by modulating their brain activity. This kind of closed-loop system can be used e.g. to assist stroke rehabilitation. In this doctoral thesis, methods for brain–computer interfaces utilizing magnetoencephalography (MEG) were developed. In the experimental setup, healthy BCI users imagined either left- or right-hand movements during MEG measurement, according to given instructions. Motor imagery -related MEG responses were analyzed in real time and used to control visual and proprioceptive feedback delivered to the users. The first and second study of the thesis involved a comparison of machine learning methods for discriminating between MEG signals related to left- and right-hand imagery. These signals could be classified with a good accuracy when each subject’s own MEG signals were used as training data for the machine learning classifiers. The classification was more difficult when the training data were collected from other subjects, but even in that case left- and right- hand imagery could be discriminated with a reasonable accuracy. However, poor classification results were obtained when MEG responses for passive hand movements were used as training data. The third study investigated how the users’ brain activity changes during a single measurement session while using a BCI. In the subjects receiving proprioceptive feedback, the amplitude on sensorimotor rhythms increased linearly over the motor cortical regions, while similar effect was not observed in subjects receiving purely visual feedback. Proprioceptive feedback should thus be preferred over visual feedback in BCIs aiming at motor cortex recovery. The results of the thesis can be utilized in development of BCIs aiming at motor cortex rehabilitation. The classification results can be used as a benchmark for other algorithms decoding motor imagery -related MEG signals.
Contact details of the doctoral student: [email protected]