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.

We applied second-order blind identification (SOBI), an independent component analysis method, to MEG data collected during cognitive tasks. We explored SOBI's ability to help isolate underlying neuronal sources with relatively poor signal-to-noise ratios, allowing their identification and localization. We compare localization of the SOBI-separated components to localization from unprocessed sensor signals, using an equivalent current dipole modeling method. For visual and somatosensory modalities, SOBI preprocessing resulted in components that can be localized to physiologically and anatomically meaningful locations. Furthermore, this preprocessing allowed the detection of neuronal source activations that were otherwise undetectable. This increased probability of neuronal source detection and localization can be particularly beneficial for MEG studies of higher-level cognitive functions, which often have greater signal variability and degraded signal-to-noise ratios than sensory activation tasks.