title: Analysis and asynchronous detection of gradually unfolding errors during monitoring tasks.
abstract: Human studies on cognitive control processes rely on tasks involving sudden-onset stimuli, which allow the analysis of these neural imprints to be time-locked and relative to the stimuli onset. Human perceptual decisions, however, comprise continuous processes where evidence accumulates until reaching a boundary. Surpassing the boundary leads to a decision where measured brain responses are associated to an internal, unknown onset. The lack of this onset for gradual stimuli hinders both the analyses of brain activity and the training of detectors. This paper studies electroencephalographic (EEG)-measurable signatures of human processing for sudden and gradual cognitive processes represented as a trajectory mismatch under a monitoring task. Time-locked potentials and brain-source analysis of the EEG of sudden mismatches revealed the typical components of event-related potentials and the involvement of brain structures related to cognitive control processing. For gradual mismatch events, time-locked analyses did not show any discernible EEG scalp pattern, despite related brain areas being, to a lesser extent, activated. However, and thanks to the use of non-linear pattern recognition algorithms, it is possible to train an asynchronous detector on sudden events and use it to detect gradual mismatches, as well as obtaining an estimate of their unknown onset. Post-hoc time-locked scalp and brain-source analyses revealed that the EEG patterns of detected gradual mismatches originated in brain areas related to cognitive control processing. This indicates that gradual events induce latency in the evaluation process but that similar brain mechanisms are present in sudden and gradual mismatch events. Furthermore, the proposed asynchronous detection model widens the scope of applications of brain-machine interfaces to other gradual processes.
authors.affiliation: Instituto de Investigación en Ingeniería de Aragón (I3A), Edificio I+D+i, Mariano Esquillor, E-50018 Zaragoza, Spain. Departamento de Informática e Ingeniería de Sistemas (DIIS), University of Zaragoza, Maria de Luna 1, E-50018 Zaragoza, Spain.
authors.forename: Jason
authors.initials: J
authors.lastname: Omedes
authors.forename: Iñaki
authors.initials: I
authors.lastname: Iturrate
authors.forename: Javier
authors.initials: J
authors.lastname: Minguez
authors.forename: Luis
authors.initials: L
authors.lastname: Montesano
mesh_terms: D000328:Adult; D001288:Attention / Q000502:physiology; D001921:Brain / Q000502:physiology*; D003071:Cognition / Q000502:physiology*; D004569:Electroencephalography / Q000379:methods*; D006801:Humans; D000069550:Machine Learning; D008297:Male; D009039:Motion Perception / Q000502:physiology*; D010363:Pattern Recognition, Automated / Q000379:methods; D011930:Reaction Time / Q000502:physiology*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013647:Task Performance and Analysis