Jan Kujala

The paper examines how vibroacoustic stimulation before music listening modulates brain oscillatory dynamics, showing changes in power and spatial distribution of neural activity.

This study examines biobehavioral synchrony in human-canine dyads, finding coordinated dynamics in motion, heart rate, and heart rate variability influenced by individual and relational factors.

This study shows that neural activity changes with breathing phases and techniques, with alpha power fluctuating during inspiration and expiration and increasing during deep breathing.

The paper investigates how heart rate variability modulates brain oscillatory activity and links heart-brain coupling to individual trait characteristics.

This paper introduces a new analysis scheme to improve the robustness of non-invasive functional connectivity assessment by addressing signal leakage and baseline assumptions.

This study investigates the heritability of sensorimotor beta rhythm and aperiodic signal in human MEG data, finding high heritability for beta spectral power and aperiodic 1/f signal.

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 investigates neural connectivity during naturalistic reading using magnetoencephalography, finding increased beta- and gamma-band coherence and directed interactions during reading compared to visual scanning.

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 examines cross-modal attentional control in adolescents using MEG, finding differences in visual and auditory cortex engagement linked to physical activity levels.

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.