Yin Tian

Lp Norm Iterative Sparse Solution for EEG Source Localization

How to localize the neural electric activities effectively and precisely from the scalp EEG recordings is a critical issue for clinical neurology and cognitive neuroscience. In this paper, based on the spatial sparse assumption of brain activities, proposed is a novel iterative EEG source imaging algorithm, Lp norm iterative sparse solution (LPISS). In LPISS, the lp(ples1) norm constraint for sparse solution is integrated into the iterative weighted minimum norm solution of the underdetermined EEG inverse problem, and it is the constraint and the iteratively renewed weight that forces the inverse problem to converge to a sparse solution effectively. The conducted simulation studies with comparison to LORETA and FOCUSS for various dipoles configurations confirmed the validation of LPISS for sparse EEG source localization. Finally, LPISS was applied to a real evoked potential collected in a study of inhibition of return (IOR), and the result was consistent with the previously suggested activated areas involved in an IOR process

Why do we need to use a zero reference? Reference influences on the ERPs of audiovisual effects.

Using ERPs in the audiovisual stimulus, the current study is the first to investigate the influence of the reference on experimental effects (between two conditions). Three references, the average reference (AR), the mean mastoid (MM), and a new infinity zero reference (IR), were comparatively investigated via ERPs, statistical parametric scalp mappings (SPSM), and LORETA. Specifically, for the N1 (170-190u2009ms), the SPSM results showed an anterior distribution for MM, a posterior distribution for IR, and both anterior and posterior distributions for AR. However, the circumstantial evidence provided by LORETA is consistent with SPSM of IR. These results indicated that the newly developed IR could provide increased accuracy; thus, we recommend IR for future ERP studies.

Neuroelectric source imaging using 3SCO: A space coding algorithm based on particle swarm optimization and l0 norm constraint

The electroencephalogram (EEG) neuroelectric sources inverse problem is usually underdetermined and lacks a unique solution, which is due to both the electromagnetism Helmholtz theorem and the fact that there are fewer observations than the unknown variables. One potential choice to tackle this issue is to solve the underdetermined system for a sparse solution. Aiming to the sparse solution, a novel algorithm termed 3SCO (Solution Space Sparse Coding Optimization) is presented in this paper. In 3SCO, after the solution space is coded with some particles, the particle-coded space is compressed by the evolution of particle swarm optimization algorithm, where an l0 constrained fitness function is introduced to guarantee the selection of a suitable sparse solution for the underdetermined system. 3SCO was first tested by localizing simulated EEG sources with different configurations on a realistic head model, and the comparisons with minimum norm (MN), LORETA (low resolution electromagnetic tomography), l1 norm solution and FOCUSS (focal underdetermined system solver) confirmed that a good sparse solution for EEG source imaging could be achieved with 3SCO. Finally, 3SCO was applied to localize the neuroelectric sources in a visual stimuli related experiment and the localized areas were basically consistent with those reported in previous studies.

Relationships between the resting-state network and the P3: Evidence from a scalp EEG study.

The P3 is an important event-related potential that can be used to identify neural activity related to the cognitive processes of the human brain. However, the relationships, especially the functional correlations, between resting-state brain activity and the P3 have not been well established. In this study, we investigated the relationships between P3 properties (i.e., amplitude and latency) and resting-state brain networks. The results indicated that P3 amplitude was significantly correlated with resting-state network topology, and in general, larger P3 amplitudes could be evoked when the resting-state brain network was more efficient. However, no significant relationships were found for the corresponding P3 latency. Additionally, the long-range connections between the prefrontal/frontal and parietal/occipital brain regions, which represent the synchronous activity of these areas, were functionally related to the P3 parameters, especially P3 amplitude. The findings of the current study may help us better understand inter-subject variation in the P3, which may be instructive for clinical diagnosis, cognitive neuroscience studies, and potential subject selection for brain-computer interface applications.

Cortical network properties revealed by SSVEP in anesthetized rats.

Steady state visual evoked potentials (SSVEP) are assumed to be regulated by multiple brain areas, yet the underlying mechanisms are not well understood. In this study, we utilized multi-channel intracranial recordings together with network analysis to investigate the underlying relationships between SSVEP and brain networks in anesthetized rat. We examined the relationship between SSVEP amplitude and the network topological properties for different stimulation frequencies, the synergetic dynamic changes of the amplitude and topological properties in each rat, the network properties of the control state, and the individual difference of SSVEP network attributes existing among rats. All these aspects consistently indicate that SSVEP response is closely correlated with network properties, the reorganization of the background network plays a crucial role in SSVEP production, and the background network may provide a physiological marker for evaluating the potential of SSVEP generation.

Robust removal of ocular artifacts by combining Independent Component Analysis and system identification

Abstract Eye activity is one of the main sources of artifacts in electroencephalogram (EEG) recordings, however, the ocular artifact can seriously distort the EEG recordings. It is an open issue to remove the ocular artifact as completely as possible without losing the useful EEG information. Independent Component Analysis (ICA) has been one of the correction approaches to correct the ocular artifact in practice. However, ICA based approach may overly or less remove the artifacts when the EEG sources and ocular sources cannot be represented in different independent components (ICs). In this paper, a new approach combining ICA and Auto-Regressive eXogenous (ARX) (ICA-ARX) is proposed for a more robust removal of ocular artifact. In the proposed approach, to lower the negative effect induced by ICA, ARX is used to build the multi-models based on the ICA corrected signals and the reference EEG selected before contamination period for each channel, and then the optimal model will be selected for further artifact removal. The results applied to both the simulated signals and actual EEG recordings demonstrate the effectiveness of the proposed approach for ocular artifact removal, and its potential to be used in the EEG related studies.

Equivalent Charge Source Model Based Iterative Maximum Neighbor Weight for Sparse EEG Source Localization

How to localize the neural electric activities within brain effectively and precisely from the scalp electroencephalogram (EEG) recordings is a critical issue for current study in clinical neurology and cognitive neuroscience. In this paper, based on the charge source model and the iterative re-weighted strategy, proposed is a new maximum neighbor weight based iterative sparse source imaging method, termed as CMOSS (Charge source model based Maximum neighbOr weight Sparse Solution). Different from the weight used in focal underdetermined system solver (FOCUSS) where the weight for each point in the discrete solution space is independently updated in iterations, the new designed weight for each point in each iteration is determined by the source solution of the last iteration at both the point and its neighbors. Using such a new weight, the next iteration may have a bigger chance to rectify the local source location bias existed in the previous iteration solution. The simulation studies with comparison to FOCUSS and LORETA for various source configurations were conducted on a realistic 3-shell head model, and the results confirmed the validation of CMOSS for sparse EEG source localization. Finally, CMOSS was applied to localize sources elicited in a visual stimuli experiment, and the result was consistent with those source areas involved in visual processing reported in previous studies.

Recognizing mild cognitive impairment based on network connectivity analysis of resting EEG with zero reference

The diagnosis of mild cognitive impairment (MCI) is very helpful for early therapeutic interventions of Alzheimers disease (AD). MCI has been proven to be correlated with disorders in multiple brain areas. In this paper, we used information from resting brain networks at different EEG frequency bands to reliably recognize MCI. Because EEG network analysis is influenced by the reference that is used, we also evaluate the effect of the reference choices on the resting scalp EEG network-based MCI differentiation. The conducted study reveals two aspects: (1) the network-based MCI differentiation is superior to the previously reported classification that uses coherence in the EEG; and (2) the used EEG reference influences the differentiation performance, and the zero approximation technique (reference electrode standardization technique, REST) can construct a more accurate scalp EEG network, which results in a higher differentiation accuracy for MCI. This study indicates that the resting scalp EEG-based network analysis could be valuable for MCI recognition in the future.

Brain oscillations and electroencephalography scalp networks during tempo perception

In the current study we used electroencephalography (EEG) to investigate the relation between musical tempo perception and the oscillatory activity in specific brain regions, and the scalp EEG networks in the theta, alpha, and beta bands. The results showed that the theta power at the frontal midline decreased with increased arousal level related to tempo. The alpha power induced by original music at the bilateral occipital-parietal regions was stronger than that by tempo-transformed music. The beta power did not change with tempo. At the network level, the original music-related alpha network had high global efficiency and the optimal path length. This study was the first to use EEG to investigate multi-oscillatory activities and the data support the tempo-specific timing hypothesis.

Attentional orienting and response inhibition: insights from spatial-temporal neuroimaging

Attentional orienting and response inhibition have largely been studied separately. Each has yielded important findings, but controversy remains concerning whether they share any neurocognitive processes. These conflicting findings may originate from two issues: (1) at the cognitive level, attentional orienting and response inhibition are typically studied in isolation; and (2) at the technological level, a single neuroimaging method is typically used to study these processes. This article reviews recent achievements in both spatial and temporal neuroimaging, emphasizing the relationship between attentional orienting and response inhibition. We suggest that coordinated engagement, both top-down and bottom-up, serves as a common neural mechanism underlying these two cognitive processes. In addition, the right ventrolateral prefrontal cortex may play a major role in their harmonious operation.