Tagged: Scalp

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.

Classifying single-trial ERPs from visual and frontal cortex during free viewing

Event-related potentials (ERPs) recorded at the scalp are indicators of brain activity associated with event-related information processing; hence they may be suitable for the assessment of changes in cognitive processing load. While the measurement of ERPs in a laboratory setting and classifying those ERPs is trivial, such a task presents major challenges in a “real world” setting where the EEG signals are recorded when subjects freely move their eyes and the sensory inputs are continuously, as opposed to discretely presented. Here we demonstrate that with the aid of second-order blind identification (SOBI), a blind source separation (BSS) algorithm: (1) we can extract ERPs from such challenging data sets; (2) we were able to obtain meaningful single-trial ERPs in addition to averaged ERPs; and (3) we were able to estimate the spatial origins of these ERPs. Finally, using back-propagation neural networks as classifiers, we show that these single-trial ERPs from specific brain regions can be used to determine moment-to-moment changes in cognitive processing load during a complex “real world” task.