ARTICLE IN PRESS Neuroscience and Biobehavioral Reviews 30 (2006) 808–822 www.elsevier.com/locate/neubiorev Review Imaging human EEG dynamics using independent component analysis Julie Ontona, Marissa Westerfieldb, Jeanne Townsendb, Scott Makeiga,Ã aSwartz Center for Computational Neuroscience, Institute for Neural Computation, University of California San Diego, La Jolla, CA 92093-0961, USA bDepartment of Neurosciences, University of California San Diego, La Jolla, CA 92093, USA Abstract This review discusses the theory and practical application of independent component analysis (ICA) to multi-channel EEG data. We use examples from an audiovisual attention-shifting task performed by young and old subjects to illustrate the power of ICA to resolve subtle differences between evoked responses in the two age groups. Preliminary analysis of these data using ICA suggests a loss of task specificity in independent component (IC) processes in frontal and somatomotor cortex during post-response periods in older as compared to younger subjects, trends not detected during examination of scalp-channel event-related potential (ERP) averages. We discuss possible approaches to component clustering across subjects and new ways to visualize mean and trial-by-trial variations in the data, including ERP-image plots of dynamics within and across trials as well as plots of event-related spectral perturbations in component power, phase locking, and coherence. We believe that widespread application of these and related analysis methods should bring EEG once again to the forefront of brain imaging, merging its high time and frequency resolution with enhanced cm-scale spatial resolution of its cortical sources. r 2006 Published by Elsevier Ltd. Keywords: EEG; ERP; ICA; Independent component analysis; P300; P3; Aging; Mu; Review Contents 1. Imaging human brain dynamics from multi-channel scalp electroencephalographic (EEG) recordings . 809 2. EEG sources and source independence . 810 3. Independent component analysis. 810 3.1. ICA history . 810 3.2. ICA model assumptions . 811 3.3. The ICA model . 811 3.4. Component source modeling . 812 3.5. Practical considerations . 813 3.6. ICA versus PCA . 813 3.7. Two classes of EEG artifacts . 814 4. Sample application to the study of normal aging . 814 4.1. Audiovisual attention-shifting . 814 4.2. Target-evoked response differences in younger and older adults . 814 4.3. ICA decomposition . 815 4.4. Component clustering . 816 4.5. ERPs and component clusters . 817 4.6. Event-related rhythmicity. 819 4.7. Statistics on component clusters . 819 ÃCorresponding author. E-mail address: [email protected] (S. Makeig). 0149-7634/$ - see front matter r 2006 Published by Elsevier Ltd. doi:10.1016/j.neubiorev.2006.06.007 ARTICLE IN PRESS J. Onton et al. / Neuroscience and Biobehavioral Reviews 30 (2006) 808–822 809 5. Further challenges . 819 5.1. Component clustering methods . 819 5.2. Time/frequency modeling. 820 5.3. Trial-to-trial variability . 820 5.4. Component statistics . 820 6. Functional electromagnetic brain imaging . 821 Acknowledgments . 821 References . 821 1. Imaging human brain dynamics from multi-channel scalp whole data. While the spatial resolution of EEG imaging electroencephalographic (EEG) recordings has in the past been considered to be poor in all three of these aspects, we believe that new techniques for EEG Even a brief glance at multi-channel EEG data shows analysis including those discussed in this review signifi- that nearby scalp channels record highly correlated signals. cantly improve its spatial resolution by all definirions of the Why? Because EEG signals are not produced in the scalp term. or the brain directly under the recording electrodes. The recovery of the exact cortical distribution of an EEG Rather, they are generated by partial synchrony of local source region is limited by the undercompleteness of the field potentials in many distinct cortical domains—each inverse source localization problem. For example, far-field domain being, in the simplest case, a patch of cortex of potentials from two synchronously active but physically unknown extent. The radial orientation of pyramidal cells opposing cortical source areas—e.g., source areas facing relative to the cortical surface within such a domain allows each other on opposite sides of a cortical sulcus—may summation of temporally synchronous extra-neuronal cancel, and their joint activity will have no effect on the potentials whose summed ‘far-field’ potentials project to scalp data. If a third area is coherently active, there will be the scalp electrodes near instantly through passive volume no way to determine from scalp recordings whether the conduction. In the absence of such local area synchrony observed activity arises within the third area alone, within and near parallel orientations of neighboring pyramidal all three areas synchronously, or in any other combination neurons, local field activities would partially or completely of partially self-canceling source areas whose summed cancel each other out, thus preventing far-field potentials activity at the scalp also matches or closely resembles that of sufficient strength to be detected at scalp electrodes. By of the third area alone. the basic laws of electrical conductance, far-field potentials The inverse source localization problem may be greatly generated within all cortical (and non-brain) domains simplified by relying on the well-accepted assumptions that project to and sum linearly at nearly every scalp electrode. EEG signals arise from cortical pyramidal cells oriented Thus, EEG data recorded at a single electrode are a simple perpendicular to the cortical surface and (usually) located sum (or more technical, a weighted linear mixture) of within a single contiguous and therefore highly intercon- underlying cortical source signals. The weights of each nected cortical domain. It is not easy, however, to recorded mixture are determined by the distance of the separately record an EEG scalp distribution generated in cortical source domains or patches from the electrode pair only one cortical domain, since many EEG source domains (‘active’ and ‘reference’), the orientation of the cortical contribute to each recorded EEG signal at nearly all time patch relative to the electrode pair locations, and the points. The common method of response averaging, electrical properties of intervening tissues (cortex, cerebral- producing event-related potential (ERP) average time spinal fluid, skull, and skin). courses time-locked to a set of similar stimulus onsets or This spatial mixing of EEG source signals by volume other events, was originally thought to produce EEG scalp conduction produces the strong correlations observed distributions in which only a few source areas—hopefully between EEG recordings at nearby electrodes and is the no more than one—were active at a time. However, in reason why EEG, the first developed and still the most practice such hopes were not realized, since very soon after sensitive and dynamic non-invasive brain imaging mod- the earliest sensory signals reach the cortex, sensory ality, has long been denigrated as having ‘poor spatial information begins to reach and perturb ongoing field resolution.’ The term ‘spatial resolution’ has several potential activities within many brain areas (Hupe et al., meanings, however, and the actual degree of spatial 2001; Klopp et al., 2000). A more ideal goal for EEG resolution of EEG depends on the intended sense of the analysis should be to detect and separate activities in term ‘resolution’. For any signal modality, three separable multiple concurrently active EEG source areas, regardless meanings of the term ‘spatial resolution’ are the degree to of their relative strengths at different moments. which the exact location of a single source may be Recently, a new approach to finding EEG source accurately determined; the spatial separation between two activities has been developed (Makeig et al., 1996) based sources that is necessary to separate their signals; and the on a simple physiological assumption that across sufficient number of such sources that can be separated from the time, the EEG signals arising in different cortical source ARTICLE IN PRESS 810 J. Onton et al. / Neuroscience and Biobehavioral Reviews 30 (2006) 808–822 domains are near temporally independent of each other. effective EEG source. In either case, EEG scalp signals This means that measuring the scalp EEG activity may be modeled as the sum of distinct, phase-independent, produced in some of the source domains at a given and spatially stationary signals from cortical patches (or moment allows no inferences about EEG activities in the coupled patch pairs). A third major category of EEG signal other source domains at the same instant. As we shall see, sources are non-brain artifact sources including the eyes, this assumption is sufficient to separate signals from both scalp muscles, defective or poorly attached electrodes, and physically distant and adjacent EEG source areas—if their ambient line noise, whose volume-conducted activities are contributions to the scalp EEG are largely independent also summed in EEG recordings. over time. This insight and the resulting algorithms for While sufficiently dense multi-scale recordings of macro- signal separation that have emerged in the last decade have scopic field activity in cortex are still lacking, the created a new field within signal processing in general— physiological plausibility and heuristic accuracy, at least,