EEG error-related potentials encode magnitude of errors and individual perceptual thresholds
Fumiaki Iwane,
Aleksander Sobolewski,
Ricardo Chavarriaga,
José del R. Millán
Affiliations
Fumiaki Iwane
Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX 78712, USA; Department of Neurology, The University of Texas at Austin, Austin, TX 78712, USA; Learning Algorithms and Systems Laboratory (LASA), École Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland
Aleksander Sobolewski
Wyss Center for Bio and Neuroengineering, Campus Biotech, 1202 Genève, Switzerland; École Polytechnique Fédérale de Lausanne (EPFL), Campus Biotech, 1202 Genève, Switzerland
Ricardo Chavarriaga
École Polytechnique Fédérale de Lausanne (EPFL), Campus Biotech, 1202 Genève, Switzerland; Centre for Artificial Intelligence, Zurich University of Applied Sciences (ZHAW), 8401 Winterthur, Switzerland
José del R. Millán
Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX 78712, USA; Department of Neurology, The University of Texas at Austin, Austin, TX 78712, USA; École Polytechnique Fédérale de Lausanne (EPFL), Campus Biotech, 1202 Genève, Switzerland; Mulva Clinic for the Neurosciences, The University of Texas at Austin, Austin, TX 78712, USA; Corresponding author
Summary: Error-related potentials (ErrPs) are a prominent electroencephalogram (EEG) correlate of performance monitoring, and so crucial for learning and adapting our behavior. It is poorly understood whether ErrPs encode further information beyond error awareness. We report an experiment with sixteen participants over three sessions in which occasional visual rotations of varying magnitude occurred during a cursor reaching task. We designed a brain-computer interface (BCI) to detect ErrPs that provided real-time feedback. The individual ErrP-BCI decoders exhibited good transfer across sessions and scalability over the magnitude of errors. A non-linear relationship between the ErrP-BCI output and the magnitude of errors predicts individual perceptual thresholds to detect errors. We also reveal theta-gamma oscillatory coupling that co-varied with the magnitude of the required adjustment. Our findings open new avenues to probe and extend current theories of performance monitoring by incorporating continuous human interaction tasks and analysis of the ErrP complex rather than individual peaks.
