pub134611

cited authors

  • Tang, AC; Sutherland, MT; McKinney, CJ; Liu, JY; Wang, Y; Parra, LC; Gerson, AD; Sajda, P

abstract

  • Event-related potentials (ERPs) recorded at the scalp are indicators of brain activity associated with eventrelated 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 backpropagation 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. © 2006 IEEE.

Digital Object Identifier (DOI)

start page

  • 1376

end page

  • 1383