title: Let's face it, from trial to trial: comparing procedures for N170 single-trial estimation.
abstract: The estimation of event-related single trial EEG activity is notoriously difficult but is of growing interest in various areas of cognitive neuroscience, such as multimodal neuroimaging and EEG-based brain computer interfaces. However, an objective evaluation of different approaches is lacking. The present study therefore compared four frequently-used single-trial data filtering procedures: raw sensor amplitudes, regression-based estimation, bandpass filtering, and independent component analysis (ICA). High-density EEG data were recorded from 20 healthy participants in a face recognition task and were analyzed with a focus on the face-selective N170 single-trial event-related potential. Linear discriminant analysis revealed significantly better single-trial estimation for ICA compared to raw sensor amplitudes, whereas the other two approaches did not improve classification accuracy. Further analyses suggested that ICA enabled extraction of a face-sensitive independent component in each participant, which led to the superior performance in single trial estimation. Additionally, we show that the face-sensitive component does not directly represent activity from a neuronal population exclusively involved in face-processing, but rather the activity of a network involved in general visual processing. We conclude that ICA effectively facilitates the separation of physiological trial-by-trial fluctuations from measurement noise, in particular when the process of interest is reliably reflected in components representing the neural signature of interest.
authors.affiliation: Neuropsychology Lab, Department of Psychology, University of Oldenburg, Oldenburg, Germany. maarten.de.vos@uni-oldenburg.de
authors.forename: Maarten
authors.initials: M
authors.lastname: De Vos
authors.forename: Jeremy D
authors.initials: JD
authors.lastname: Thorne
authors.forename: Galit
authors.initials: G
authors.lastname: Yovel
authors.forename: Stefan
authors.initials: S
authors.lastname: Debener
mesh_terms: D000328:Adult; D001921:Brain / Q000502:physiology*; D004569:Electroencephalography / Q000379:methods*; D005071:Evoked Potentials / Q000502:physiology*; D006801:Humans; D010364:Pattern Recognition, Visual / Q000502:physiology; D010775:Photic Stimulation; D012815:Signal Processing, Computer-Assisted; D055815:Young Adult