he central active challenge we are constantly addressing in daily life is to correctly assess the intent of others ('What is she trying to do? ...') and the import of sensory events ('What - good or bad - may happen now? ...') based on active perception ('It looks to me like she is trying to ...') and retrieved associations (''And she was the one who ...'). The corresponding problem for cognitive neuroscience is to identify, ideally from non-invasive brain activity recordings, those patterns of distributed brain activity that accompany and support active human cognition and behavior. This problem has two parts: First, -What patterns of distributed brain dynamics follow from, accompany, and predict specific world events and subject behavior? -To fully understand the experience and behavior of subjects in performing a given task, we must take into account both the import of each task event to the subject and the intent of each of behavioral event. These factors cannot be known directly, but they may be accurately guessed or inferred, in many cases, from detailed recordings of subject behavior and from the specific historical context in which each recorded environmental or behavioral event occurs. In the case of electroencephalographic (EEG) and/or magnetoencephalographic (MEG) signals recorded non-invasively from the human scalp, a second part of the problem remains -Which brain areas generate the identified signal patterns?'
The usual approach to analyzing electromagnetic scalp data has been to separate recorded events and behavior into a few simple categories, to average the recorded brain dynamics time locked to each event category, and then to apply physical inverse source estimation methods to scalp maps of peaks in the resulting averages. This project will explore using new machine learning methods, including advanced independent component analysis (ICA) and sparse Bayesian learning (SBL) methods, to jointly model the recorded task event, subject behavior, and brain dynamic data recorded in a complex learning task. The project has two goals: First, to identify patterns in unaveraged EEG and/or MEG data that reliably accompany subject behavior in specific contexts, and second to determine the exact areas of the subject's cortical mantle that locally synchonize their electromagnetic activities to produce the identified scalp patterns. If successful, the project will enhance the value of noninvasive electromagnetic brain imaging for identifying and measuring, with high temporal and spatial resolution, complex, distributed patterns of locally synchronous cortical activity that support active human cognition.
