Real-time machine learning of MEG: Decoding signatures of selective attention
Perustieteiden korkeakoulu | Master's thesis
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Machine Learning and Data Mining
Master’s Programme in Machine Learning and Data Mining (Macadamia)
AbstractBrain--computer interfaces (BCIs) provide disabled patients with access to communication tools and control of prosthetic devices. Most BCIs employ a machine-learning algorithm which analyzes brain data in real time and provides users with feedback. Magnetoencephalography (MEG) is a non-invasive method which records neuromagnetic signals from the brain at a high temporal resolution. This makes it particularly suitable for real-time analysis and machine learning. Developing tools that allow such analysis will have long-term benefits in using MEG for BCI approaches and exploring new experimental paradigms. In this thesis, a real-time analysis pipeline for machine learning in MEG was developed with the goal to enable BCI in MEG systems. The implementation details of the pipeline were described in the thesis along with performance details. Additionally, pilot measurements to decode auditory attention were conducted. The spatio-temporal dynamics of the offline experiment were used to optimize the preprocessing steps required for the BCI application. In particular, the frequency range of 1.0--1.5 Hz was found to be particularly discriminative. Finally, simulating this pipeline in pseudo real-time mode demonstrated that a BCI to decode auditory attention is feasible in MEG.
Thesis advisorParkkonen, Lauri
machine learning, selective attention, brain–computer interface, magnetoencephalography, real-time analysis