Mia Liljeström

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 examines age-related changes in frequency-specific resting-state functional connectivity across the adult lifespan using MEG data and finds associations with sensorimotor function.

The paper uses MEG to show that older adults maintain lexical-semantic proficiency through neural reorganization, preserving sensitivity to semantic congruency despite altered activation patterns.

This study uses Bayesian reduced-rank regression to extract stable neural fingerprints from EEG data of children during nREM sleep, showing increased fingerprint stability with age.

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.

This study examines the reliability of dynamic functional connectivity in MEG data related to language processing and identifies properties associated with reliable connections.

This study assesses the test-retest reliability of sensorimotor beta events and 1/f response using MEG in healthy controls, finding good stability for most features.

This study assesses the test-retest reliability of MEG recordings during picture naming and semantic judgment tasks, showing consistent cortical activation patterns in healthy individuals.

The paper provides a Python-based pipeline for analyzing functional connectivity and oscillatory power in MEG data, using DICS to estimate group-level brain networks from raw data to visualization.