Lauri Parkkonen
Department of Neuroscience and Biomedical Engineering

Lauri Parkkonen

Professor (Associate Professor)

Contact information

Postal address
Otakaari 3
Mobile phone
+358405089712
Full researcher profile
https://research.aalto.fi/...

Description

The main research area of Parkkonen is non-invasive neuroimaging, mainly instrumentation and data analysis methods related to MEG as well as human neuroscience of sensory modalities and cognition. He has contributed to MEG instrumentation and signal processing methods that are currently in active use in over 100 laboratories world-wide. Currently he pursues the use of optically-pumped magnetometers for MEG, exploits real-time feedback of measurements of brain activity to study cognition and plasticity, both for basic neuroscientific research and for clinical applications.

He has expanded beyond MEG by contributing to ultra-low-field MRI research and to invasive monitoring of local cortical activity. In cognitive neuroscience, Parkkonen has shed light on the neural mechanisms of sensory processing, particularly conscious visual perception. Recently he has pioneered the emerging field of social neuroscience to uncover brain mechanisms supporting social interaction.

Areas of expertise

Neuroscience Neurotechnology Human brain imaging Medical Technology Brain Research Unit Aalto Brain Centre (ABC) O.V. Lounasmaa Laboratory Department of Biomedical Engineering and Computational Science Signal processing Magnetic resonance imaging Magnetoencephalography Bioelectromagnetism Inverse problems Modeling the brain

Honors and awards

Award or honor granted for a specific work
Department of Neuroscience and Biomedical Engineering
Jan 2002

Young investigator award at the 13th Int’l Conference on Biomagnetism (Biomag2002), Jena, Germany

Award or honor granted for academic career
Department of Neuroscience and Biomedical Engineering
Jan 2015

European Research Council (ERC) Starting Grant

Publications

Department of Neuroscience and Biomedical Engineering

Adaptive neural network classifier for decoding MEG signals

Publishing year: 2019 NeuroImage
Department of Neuroscience and Biomedical Engineering

On-scalp MEG system utilizing an actively shielded array of optically-pumped magnetometers

Publishing year: 2019 NeuroImage
Department of Neuroscience and Biomedical Engineering

Non-Linear Dynamical Analysis of Resting Tremor for Demand-Driven Deep Brain Stimulation

Publishing year: 2019 Sensors (Basel, Switzerland)
Department of Neuroscience and Biomedical Engineering

The impact of improved MEG–MRI co-registration on MEG connectivity analysis

Publishing year: 2019 NeuroImage
Department of Neuroscience and Biomedical Engineering

Optical Co-registration of MRI and On-scalp MEG

Publishing year: 2019 Scientific Reports
Department of Neuroscience and Biomedical Engineering

Decoding attentional states for neurofeedback

Publishing year: 2019 NeuroImage
Department of Neuroscience and Biomedical Engineering

Across-subject offline decoding of motor imagery from MEG and EEG

Publishing year: 2018 Scientific Reports
Department of Neuroscience and Biomedical Engineering

Requirements for Coregistration Accuracy in On-Scalp MEG

Publishing year: 2018 Brain Topography
Department of Neuroscience and Biomedical Engineering

Evidence for a general performance-monitoring system in the human brain

Publishing year: 2018 Human Brain Mapping
Department of Electrical Engineering and Automation, Department of Neuroscience and Biomedical Engineering, Sensor Informatics and Medical Technology

Tracking of dynamic functional connectivity from MEG data with Kalman filtering

Publishing year: 2018 Proceedings of the 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018