Sara Korkealaakso

This study uses MEG data to guide TMS timing and location for individualized speech cortical mapping, improving accuracy by aligning TMS with MEG-defined activation patterns.

This paper introduces a Hidden Markov Model (HMM) framework to automatically segment beta activity from MEG data, showing good test-retest reliability and multisite transferability.

This study identifies two distinct beta bands in brain activity using MEG and a Hidden Markov Model, showing different roles in movement initiation, maintenance, and termination.

This paper introduces an automated method to remove speech artefacts from MEG data using EMG and mutual information, improving the analysis of speech-related neural activity.