The popularity of electroencephalography (EEG) research has been growing over the past years, especially as it relates to the understanding of human cognitive function. This growth has been in part due to recent methodological advances in the acquisition and analysis of time-resolved signals.

cuttingEEG was established to deal with the ever increasing demand for these methodological developments, that often require an advanced understanding of signal processing, machine learning and statistical pattern recognition.

cuttingEEG 2017 is the third Symposium of its kind and it brings together an impressive group of international experts to discuss and showcase the most up to date developments in the field. This four-day symposium will provide attendees with:
  • 9 different 3-hour tutorials that will introduce a wide range of cutting-edge techniques in time-resolved signal analysis
  • 12 talks by leading researchers in the field (spread over four thematic areas) who will introduce some of the most advanced methods for exploiting time-resolved signals
  • 4 short debates on the utility of these methods and the need for future developments (following the talks in each of the four thematic areas)
  • Attendees will also be offered the opportunity to present posters of their latest research during the duration of the event

June 19-22nd, 2017, Centre for Cognitive Neuroimaging, University of Glasgow
Sir Charles Wilson Building (Talks, Poster Sessions, Lunch)
Boyd Orr and James Watt South (Workshops)

Registration Fee
£90 (includes lunch on all days)
During registration, participants will also have the opportunity to indicate whether they would like to present a poster at the event.

Registration is now closed.

Click here for a campus map

Plenary Sessions (3 talks per session):
  • Dynamic Network Reconstruction (Matias Palva, Mark Woolrich, Virginie van Wassenhove)
  • EEG-informed multimodal neuroimaging (Paul Sajda, Petra Ritter, Markus Siegel)
  • iEEG and LFPs in cognition (Jean-Philippe Lachaux, Nikolai Axmacher, Josef Parvizi)
  • Model-based EEG and decoding (Marieke van Vugt, Valentin Wyart, Lucas Parra)
  • Information theoretic analysis of EEG signals (Robin Ince)
  • Multivariate encoding/decoding of EEG signals (Lucas Parra)
  • Time-frequency analysis of the EEG (Mike Cohen)
  • Connectivity and causal analysis of the EEG (Laura Astolfi)
  • Robust statistics and data visualization (Guillaume Rousselet)
  • General linear modeling analysis of EEG signals (Cyril Pernet)
  • Frequency tagging (steady state analysis) in EEG (Molly Henry)
  • EEG analysis in R (Matt Craddock, Dale Barr)
  • Biophysical modeling for interpreting EEG data (Stephanie Jones)
Click here for the detailed program


Matias Palva
University of Helsinki

Virginie van Wassenhove

Mark Woolrich
Oxford University

Petra Ritter
Charite Berlin

Paul Sajda
Columbia University

Markus Siegel
University of Tubingen

Josef Parvizi
Stanford University

Jean-Philippe Lachaux

Nikolai Axmacher
University of Bochum

Marieke van Vugt
University of Groningen

Lucas Parra

Valentin Wyart
ENS Paris

Mike X Cohen
Donders Institute

Robin Ince
University of Glasgow

Guillaume Rousselet
University of Glasgow

Cyril Pernet
University of Edinburgh

Laura Astolfi
Sapienza University of Rome

Molly Henry
University of Western Ontario

Stephanie Jones
Brown University

Dale Barr
University of Glasgow

Matt Craddock
University of Leeds