Independent EEG Sources Are Dipolar

Independent component analysis (ICA) and blind source separation (BSS) methods are increasingly used to separate individual brain and non-brain source signals mixed by volume conduction in electroencephalographic (EEG) and other electrophysiological recordings. We compared results of decomposing thirteen 71-channel human scalp EEG datasets by 22 ICA and BSS algorithms, assessing the pairwise mutual information (PMI) in scalp channel pairs, the remaining PMI in component pairs, the overall mutual information reduction (MIR) effected by each decomposition, and decomposition ‘dipolarity’ defined as the number of component scalp maps matching the projection of a single equivalent dipole with less than a given residual variance. The least well-performing algorithm was principal component analysis (PCA); best performing were AMICA and other likelihood/mutual information based ICA methods. Though these and other commonly-used decomposition methods returned many similar components, across 18 ICA/BSS algorithms mean dipolarity varied linearly with both MIR and with PMI remaining between the resulting component time courses, a result compatible with an interpretation of many maximally independent EEG components as being volume-conducted projections of partially-synchronous local cortical field activity within single compact cortical domains. To encourage further method comparisons, the data and software used to prepare the results have been made available (http://sccn.ucsd.edu/wiki/BSSComparison)

Spatiotemporal linear decoding of brain state: application to performance augmentation in high-throughput tasks

This review summarizes linear spatiotemporal signal analysis methods that derive their power from careful consideration of spatial and temporal features of skull surface potentials. BCIs offer tremendous potential for improving the quality of life for those with severe neurological disabilities. At the same time, it is now possible to use noninvasive systems to improve performance for time-demanding tasks. Signal processing and machine learning are playing a fundamental role in enabling applications of BCI and in many respects, advances in signal processing and computation have helped to lead the way to real utility of noninvasive BCI

Attentional priorities and access to short-term memory: parietal interactions.

The intraparietal sulcus (IPS) has been implicated in selective attention as well as visual short-term memory (VSTM). To contrast mechanisms of target selection, distracter filtering, and access to VSTM, we combined behavioral testing, computational modeling and functional magnetic resonance imaging. Sixteen healthy subjects participated in a change detection task in which we manipulated both target and distracter set sizes. We directly compared the IPS response as a function of the number of targets and distracters in the display and in VSTM. When distracters were not present, the posterior and middle segments of IPS showed the predicted asymptotic activity increase with an increasing target set size. When distracters were added to a single target, activity also increased as predicted. However, the addition of distracters to multiple targets suppressed both middle and posterior IPS activities, thereby displaying a significant interaction between the two factors. The interaction between target and distracter set size in IPS could not be accounted for by a simple explanation in terms of number of items accessing VSTM. Instead, it led us to a model where items accessing VSTM receive differential weights depending on their behavioral relevance, and secondly, a suppressive effect originates during the selection phase when multiple targets and multiple distracters are simultaneously present. The reverse interaction between target and distracter set size was significant in the right temporoparietal junction (TPJ), where activity was highest for a single target compared to any other condition. Our study reconciles the role of middle IPS in attentional selection and biased competition with its role in VSTM access

Neural correlates of age-related decline and compensation in visual attention capacity

We identified neural correlates of declined and preserved basic visual attention functions in aging individuals based on Bundesen’s ‘Theory of Visual Attention’ (TVA). In an inter-individual difference approach, we contrasted electrophysiology of higher- and lower-performing younger and older participants. In both age groups, the same distinct components indexed performance levels of TVA parameters visual processing speed C and visual short-term memory (vSTM) storage capacity K: The posterior N1 marked inter-individual differences in C and the contralateral delay activity (CDA) marked inter-individual differences in K. Moreover, both parameters were selectively related to two further ERP waves in older age: The anterior N1 was reduced for older participants with lower processing speed, indicating that age-related loss of attentional resources slows encoding. An enhanced right-central positivity (RCP) was found only for older participants with high storage capacity, suggesting compensatory recruitment for retaining vSTM performance. Together, our results demonstrate that attentional capacity in older age depends on both preservation and successful reorganization of the underlying brain circuits

Striatal involvement in visual encoding

Klin Neurophysiol 2014; 45 - V16DOI: 10.1055/s-0034-1371195Striatal involvement in visual encodingK Moos 1, 2, R Weidner 1, S Vossel 1, E Zimmermann 1, M Dyrholm 3, GR Fink 1, 2 1Forschungszentrum Jülich, Cognitive Neuroscience, Jülich, Deutschland 2Uniklinik Köln, Klinik und Poliklinik für Neurologie, Köln, Deutschland 3Universität Kopenhagen, Institut für Psychologie, Kopenhagen, Dänemark KongressbeitragA capacity limited system, such as the human brain, needs effective strategies to deal with large amounts of incoming information. The theory of visual attention (TVA) allows for formal quantification of specific processes related to selection and recognition of visual information. In the present model-based fMRI study, parameters derived from the TVA-framework were used to determine trial-by-trial changes in the distribution of attention and to relate these to specific cortical attentional networks. Trial-by-trial changes in the single trial likelihood (STL), an inferred variable representing an element's probability of being encoded into the visual short term memory (VSTM) and thus consciously perceived, were accompanied by changes in activity in the bilateral putamen and a right-lateralized network involving the inferior parietal lobule. Moreover, attentional networks were activated by additional display elements: With higher competition between relevant display elements (i.e., when two targets rather than one target and a distractor were presented), a bilateral dorsal network comprising the frontal eye fields and superior parietal regions was more active. Bilateral temporo-parietal junction and superior frontal regions showed higher activity in the reversed contrast, reflecting distractor-related filtering processes.We demonstrate that basal ganglia, i.e., the bilateral striatum, are critically involved in the encoding of visual information into the VSTM to a perceptual level of processing. Moreover, we specify the function of the dorsal attention network in resolving competition between equally important display elements, whereas a bilateral ventral network processes imbalances in the distribution of attentional resources