Learning EEG components for discriminating multi-class perceptual decisions

Logistic regression has been used as a supervised method for extracting EEG components predictive of binary perceptual decisions. However, often perceptual decisions require a choice between more than just two alternatives. In this paper we present results using multinomial logistic regression (MLR) for learning EEG components in a 3-way visual discrimination task. Subjects were required to decide between three object classes (faces, houses, and cars) for images which were embedded with varying amounts of noise. We recorded the subjects´ EEG while they were performing the task and then used MLR to predict the stimulus category, on a single-trial basis, for correct behavioral responses. We found an early component (at 170ms) that was consistent across all subjects and with previous binary discrimination paradigms. However a later component (at 300-400ms), previously reported in the binary discrimination paradigms, was more variable across subjects in this three-way discrimination task. We also computed forward models for the EEG components, with these showing a difference in the spatial distribution of component activity for the different categorical decisions. In summary, we find that logistic regression, generalized to the arbitrary N-class case, can be a useful approach for learning and analyzing EEG components underlying multi-class perceptual decisions.