Low Resolution Electrical Tomography Research Articles

Recent trends and advances in solving the inverse problem for EEG source localization

ABSTRACTThis paper addresses the recent advancements and trends in the field of electroencephalography (EEG) using inverse problem solutions. Using the EEG data of the brain to gather the information regarding the neuronal current source distribution has been a persisting challenge. Since the EEG inverse problem is ill-posed in nature; therefore, it does not offer a unique result. A trivial and precise solution yields a detailed insight regarding the electrical activity as well as the damaged tissue in the brain. Ordinarily, this problem is solved using the regularization techniques, such as minimum norm estimates, mixed-norm estimate, low-resolution electrical tomography, artificial neural networks, and their modified variants. In this paper, the latest algorithmic developments in solving the EEG inverse problem are reviewed. The optimization rendered by these techniques in accurately solving the neural source localization problem is also discussed. The comparative performance analysis of the recent techniques has been presented. Furthermore, a number of future enhancements have also been proposed to further improve the performance of these state-of-the-art techniques.

The inverse problem in electroencephalography using the bidomain model of electrical activity

BackgroundAcquiring information about the distribution of electrical sources in the brain from electroencephalography (EEG) data remains a significant challenge. An accurate solution would provide an understanding of the inner mechanisms of the electrical activity in the brain and information about damaged tissue. New MethodIn this paper, we present a methodology for reconstructing brain electrical activity from EEG data by using the bidomain formulation. The bidomain model considers continuous active neural tissue coupled with a nonlinear cell model. Using this technique, we aim to find the brain sources that give rise to the scalp potential recorded by EEG measurements taking into account a non-static reconstruction. Comparison with Existing MethodsWe simulate electrical sources in the brain volume and compare the reconstruction to the minimum norm estimates (MNEs) and low resolution electrical tomography (LORETA) results. Then, with the EEG dataset from the EEG Motor Movement/Imagery Database of the Physiobank, we identify the reaction to visual stimuli by calculating the time between stimulus presentation and the spike in electrical activity. Finally, we compare the activation in the brain with the registered activation using the LinkRbrain platform. Results/ConclusionOur methodology shows an improved reconstruction of the electrical activity and source localization in comparison with MNE and LORETA. For the Motor Movement/Imagery Database, the reconstruction is consistent with the expected position and time delay generated by the stimuli. Thus, this methodology is a suitable option for continuously reconstructing brain potentials.

The Competitive Influences of Perceptual Load and Working Memory Guidance on Selective Attention.

The perceptual load theory in selective attention literature proposes that the interference from task-irrelevant distractor is eliminated when perceptual capacity is fully consumed by task-relevant information. However, the biased competition model suggests that the contents of working memory (WM) can guide attentional selection automatically, even when this guidance is detrimental to visual search. An intriguing but unsolved question is what will happen when selective attention is influenced by both perceptual load and WM guidance. To study this issue, behavioral performances and event-related potentials (ERPs) were recorded when participants were presented with a cue to either identify or hold in memory and had to perform a visual search task subsequently, under conditions of low or high perceptual load. Behavioural data showed that high perceptual load eliminated the attentional capture by WM. The ERP results revealed an obvious WM guidance effect in P1 component with invalid trials eliciting larger P1 than neutral trials, regardless of the level of perceptual load. The interaction between perceptual load and WM guidance was significant for the posterior N1 component. The memory guidance effect on N1 was eliminated by high perceptual load. Standardized Low Resolution Electrical Tomography Analysis (sLORETA) showed that the WM guidance effect and the perceptual load effect on attention can be localized into the occipital area and parietal lobe, respectively. Merely identifying the cue produced no effect on the P1 or N1 component. These results suggest that in selective attention, the information held in WM could capture attention at the early stage of visual processing in the occipital cortex. Interestingly, this initial capture of attention by WM could be modulated by the level of perceptual load and the parietal lobe mediates target selection at the discrimination stage.

The temporal dynamics of visual working memory guidance of selective attention.

The biased competition model proposes that there is top-down directing of attention to a stimulus matching the contents of working memory (WM), even when the maintenance of a WM representation is detrimental to target relevant performance. Despite many studies elucidating that spatial WM guidance can be present early in the visual processing system, whether visual WM guidance also influences perceptual selection remains poorly understood. Here, we investigated the electrophysiological correlates of early guidance of attention by WM in humans. Participants were required to perform a visual search task while concurrently maintaining object representations in their visual WM. Behavioral results showed that response times (RTs) were longer when the distractor in the visual search task was held in WM. The earliest WM guidance effect was observed in the P1 component (90–130 ms), with match trials eliciting larger P1 amplitude than mismatch trials. A similar result was also found in the N1 component (160–200 ms). These P1 and N1 effects could not be attributed to bottom-up perceptual priming from the presentation of a memory cue, because there was no significant difference in early event-related potential (ERP) component when the cue was merely perceptually identified but not actively held in WM. Standardized Low Resolution Electrical Tomography Analysis (sLORETA) showed that the early WM guidance occurred in the occipital lobe and the N1-related activation occurred in the parietal gyrus. Time-frequency data suggested that alpha-band event-related spectral perturbation (ERSP) magnitudes increased under the match condition compared with the mismatch condition only when the cue was held in WM. In conclusion, the present study suggests that the reappearance of a stimulus held in WM enhanced activity in the occipital area. Subsequently, this initial capture of attention by WM could be inhibited by competing visual inputs through attention re-orientation, reflecting by the alpha-band rhythm.

Mapping brain activity during loss of situation awareness: an EEG investigation of a basis for top-down influence on perception.

The objective was to map brain activity during early intervals in loss of situation awareness (SA) to examine any co-activity in visual and high-order regions, reflecting grounds for top-down influences on Level I SA. Behavioral and neuroscience evidence indicates that high-order brain areas can engage before perception is complete. Inappropriate top-down messages may distort perception during loss of SA. Evidence of co-activity of perceptual and high-order regions would not confirm such influence but may reflect a basis for it. SA and bias were measured using Quantitative Analysis of Situation Awareness and brain activity recorded with 128-channel EEG (electroencephalography) during loss of SA. One task (15 participants) required identification of a target pattern, and another task (10 participants) identification of "threat" in urban scenes. In both, the target was changed without warning, enforcing loss of SA. Key regions of brain activity were identified using source localization with standardized low-resolution electrical tomography (sLORETA) 150 to 160 ms post-stimulus onset in both tasks and also 100 to 110 ms in the second task. In both tasks, there was significant loss of SA and bias shift (p < .02), associated at both 150- and 100-ms intervals with co-activity of visual regions and prefrontal, anterior cingulate and parietal regions linked to cognition under uncertainty. There was early co-activity in high- order and visual perception regions that may provide a basis for top-down influence on perception. Co-activity in high- and low-order brain regions may explain either beneficial or disruptive top-down influence on perception affecting Level I SA in real-world operations.

The dynamic audio–motor system in pianists

This paper reports a preliminary study based on the theoretical assumption that continuous closed-loop audio-motor control could be disadvantageous for pianists. It is argued that the functional relationship between the intracerebral electrical activations in the auditory and premotor cortex should be rhythmically decreased and increased. To test this hypothesis, intracerebral electrical activations for the auditory and premotor cortex were estimated using scalp EEG and standardized low-resolution electrical tomography (sLORETA). The extracted times series were subjected to a Granger causality analysis, revealing a causal relationship from the auditory cortex to the premotor cortex that was considerably stronger during piano playing and weaker during rest. Importantly, this relationship varied rhythmically during the course of piano playing, with lags (obtained with cross-correlations) between 666 ms and 820 milliseconds. This study thus delivers evidence that the functional coupling between the auditory and premotor cortex varies during piano playing.

A strong parietal hub in the small‐world network of coloured‐hearing synaesthetes during resting state EEG

We investigated whether functional brain networks are different in coloured-hearing synaesthetes compared with non-synaesthetes. Based on resting state electroencephalographic (EEG) activity, graph-theoretical analysis was applied to functional connectivity data obtained from different frequency bands (theta, alpha1, alpha2, and beta) of 12 coloured-hearing synaesthetes and 13 non-synaesthetes. The analysis of functional connectivity was based on estimated intra-cerebral sources of brain activation using standardized low-resolution electrical tomography. These intra-cerebral sources of brain activity were subjected to graph-theoretical analysis yielding measures representing small-world network characteristics (cluster coefficients and path length). In addition, brain regions with strong interconnections were identified (so-called hubs), and the interconnectedness of these hubs were quantified using degree as a measure of connectedness. Our analysis was guided by the two-stage model proposed by Hubbard and Ramachandran (2005). In this model, the parietal lobe is thought to play a pivotal role in binding together the synaesthetic perceptions (hyperbinding). In addition, we hypothesized that the auditory cortex and the fusiform gyrus would qualify as strong hubs in synaesthetes. Although synaesthetes and non-synaesthetes demonstrated a similar small-world network topology, the parietal lobe turned out to be a stronger hub in synaesthetes than in non-synaesthetes supporting the two-stage model. The auditory cortex was also identified as a strong hub in these coloured-hearing synaesthetes (for the alpha2 band). Thus, our a priori hypotheses receive strong support. Several additional hubs (for which no a priori hypothesis has been formulated) were found to be different in terms of the degree measure in synaesthetes, with synaesthetes demonstrating stronger degree measures indicating stronger interconnectedness. These hubs were found in brain areas known to be involved in controlling memory processes (alpha1: hippocampus and retrosplenial area), executive functions (alpha1 and alpha2: ventrolateral prefrontal cortex; theta: inferior frontal cortex), and the generation of perceptions (theta: extrastriate cortex; beta: subcentral area). Taken together this graph-theoretical analysis of the resting state EEG supports the two-stage model in demonstrating that the left-sided parietal lobe is a strong hub region, which is stronger functionally interconnected in synaesthetes than in non-synaesthetes. The right-sided auditory cortex is also a strong hub supporting the idea that coloured-hearing synaesthetes demonstrate a specific auditory cortex. A further important point is that these hub regions are even differently operating at rest supporting the idea that these hub characteristics are predetermining factors of coloured-hearing synaesthesia.

Sentence integration processes: An ERP study of Chinese sentence comprehension with relative clauses

In an event-related potentials (ERPs) study, we examined the comprehension of different types of Chinese (Mandarin) relative clauses (object vs. subject-extracted) to test the universality and language specificity of sentence comprehension processes. Because Chinese lacks morphosyntactic cues to sentence constituent relations, it allows a test of the possibility that semantic-contextual processes dominate the extraction of clausal relations, in contrast to the structure-dependent processing in English and many other languages. ERP results at the RC embedded verbs showed a P600 effect for the subject-extraction type, reflecting a processing of phrasal reconfiguration, and an N400 effect for the object-extraction type, reflecting a processing of meaning reinterpretation. A central–frontal sustained negativity was produced by the RC head noun of object-extraction, suggesting a combined effect of meaning derivation and referents establishment. LORETA (Low Resolution Electrical Tomography) source localization showed activation of posterior dominance (e.g., BA 22/39/19/41/42) supporting the integration of structure mapping (P600) and meaning derivation (N400) in a developing sentential representation, consistent with the memory unification and control model (Hagoort, 2005). More left-lateralized anterior regions of a frontal–temporal network (e.g., BA 47/38) became active later in the sentence (a sustained central–frontal negativity), when the thematic-role specification for multiple referents may have required additional cognitive and memory resources. Our findings suggest that Chinese sentence reading recruits a neural network that is sensitive to the sequential/hierarchical organization of linguistic inputs in a manner that resembles to the structure-dependent cognitive processes in other languages, reflecting a universal property of language processing. The ERP data shows that early lexical processes are important in the integration process, but also challenges the view that Chinese text reading depends primarily on semantic-contextual processing in the derivation of meaning representation.

Brain activation during fast driving in a driving simulator: the role of the lateral prefrontal cortex

Little is currently known about the neural underpinnings of the cognitive control of driving behavior in realistic situations and of the driver's speeding behavior in particular. In this study, participants drove in realistic scenarios presented in a high-end driving simulator. Scalp-recorded EEG oscillations in the alpha-band (8-13 Hz) with a 30-electrode montage were recorded while the participants drove under different conditions: (i) excessively fast (Fast), (ii) in a controlled manner at a safe speed (Correct), and (iii) impatiently in the context of testing traffic conditions (Impatient). Intracerebral sources of alpha-band activation were estimated using low resolution electrical tomography. Given that previous studies have shown a strong negative correlation between the Bold response in the frontal cortex and the alpha-band power, we used alpha-band-related activity as an estimation of frontal activation. Statistical analysis revealed more alpha-band-related activity (i.e. less neuronal activation) in the right lateral prefrontal cortex, including the dorsolateral prefrontal cortex, during fast driving. Those participants who speeded most and exhibited greater risk-taking behavior demonstrated stronger alpha-related activity (i.e. less neuronal activation) in the left anterior lateral prefrontal cortex. These findings are discussed in the context of current theories about the role of the lateral prefrontal cortex in controlling risk-taking behavior, task switching, and multitasking.

A theoretical formulation of the electrophysiological inverse problem on the sphere

The construction of three-dimensional images of the primary current density (PCD) produced by neuronal activity is a problem of great current interest in the neuroimaging community, though being initially formulated in the 1970s. There exist even now enthusiastic debates about the authenticity of most of the inverse solutions proposed in the literature, in which low resolution electrical tomography (LORETA) is a focus of attention. However, in our opinion, the capabilities and limitations of the electro and magneto encephalographic techniques to determine PCD configurations have not been extensively explored from a theoretical framework, even for simple volume conductor models of the head. In this paper, the electrophysiological inverse problem for the spherical head model is cast in terms of reproducing kernel Hilbert spaces (RKHS) formalism, which allows us to identify the null spaces of the implicated linear integral operators and also to define their representers. The PCD are described in terms of a continuous basis for the RKHS, which explicitly separates the harmonic and non-harmonic components. The RKHS concept permits us to bring LORETA into the scope of the general smoothing splines theory. A particular way of calculating the general smoothing splines is illustrated, avoiding a brute force discretization prematurely. The Bayes information criterion is used to handle dissimilarities in the signal/noise ratios and physical dimensions of the measurement modalities, which could affect the estimation of the amount of smoothness required for that class of inverse solution to be well specified. In order to validate the proposed method, we have estimated the 3D spherical smoothing splines from two data sets: electric potentials obtained from a skull phantom and magnetic fields recorded from subjects performing an experiment of human faces recognition.

Low-resolution electrical tomography of the brain during psychometrically matched verbal and spatial cognitive tasks.

EEGs were recorded from 75 normal, young, female subjects during psychometrically matched verbal (WF) and spatial (DL) cognitive tasks to elicit the differences in the electrical source distribution inside the brain. Recordings were obtained using 43 EEG and 3 guard electrodes then visually edited and spatially filtered to remove extracerebral artifacts. Twenty 1-sec artifact-free epochs were obtained and analyzed from 42 and 60 subjects during WF and DL respectively. Of these subjects, 20 were placed in a training set and the remainder into a test set. The baseline for the comparison of the two tasks was established by factoring the average cross-spectral matrices of the training-set EEGs, computed in the theta, alpha, and beta frequency bands into spatial patterns common to the two tasks. Only those spatial patterns that contributed to the correct classification of subjects in the test set were included in the source analysis. The source-current density distributions were obtained using the LORETA-KEY algorithm. The results show that the source-current density distribution is related to the putative functional activity in the brain in all three frequency bands. The electrical effects of the tasks are both most highly localized and lateralized in the theta band. The effects in the alpha and beta bands are much more generalized and are strongly lateralized only during one and the other of the tasks respectively. The conclusion is that WF is mainly a left central and bilateral frontal cerebral process while DL is mainly a right central and bilateral posterior cerebral process.