The relationship between the visual evoked potential and the gamma band investigated by blind and semi-blind methods

Frequency-domain analysis of transient visual evoked potentials in schizophrenia

PURPOSE

Frequency-domain measures were applied to characterize neural deficits in individuals with schizophrenia using transient visual evoked potentials (tVEP). These measures were compared with conventional time-domain measures to elucidate underlying neurophysiological mechanisms and examine the value of frequency analysis.

METHODS

Four frequency bands of activity identified in previous work were explored with respect to magnitude (spectral power), timing (phase), a combined measure, magnitude-squared coherence (MSC), and compared to amplitudes and times of prominent deflections in the response.

RESULTS

Band 2 power/MSC (14-28 Hz) captured the major deflections in the waveform and its power predicted N75-P100 amplitude for patients and controls. Band 3 power/MSC (30-40 Hz) correlated highly with the earliest deflection (P60-N75), reflecting input to primary visual cortex (V1) and produced the largest magnitude effect. Phase of the 24th harmonic component predicted P100 peak time for patients and controls and yielded the largest group difference. Cluster analyses including time- and frequency-domain measures identified subgroups of patients with differential neurophysiological effects. A small but significant difference in visual acuity was found between groups that appears to be neurally based: Acuity (range 0.63-1.6) was not correlated with any tVEP measures in controls nor with input timing to V1 (P60 peak time) in patients, but was correlated with later tVEP measures in patients. All but two of the patients were on antipsychotic medication: Medication level (chlorpromazine equivalents) was correlated negatively with tVEP time measures and positively with certain magnitude measures yielding responses similar to controls at high levels.

CONCLUSIONS

Overall, frequency-domain measures were shown to be objective and recommended as an alternative to conventional, subjective time-domain measures for analyzing tVEPs and in distinguishing between groups (patients vs. controls and patient subgroups). The findings implicated a loss of excitatory input to V1 in schizophrenia. Acuity as measured in the current study reflected disease status, and medication level was associated with improved tVEP responses. These novel tVEP techniques may be useful in revealing neurophysiological processes affected in schizophrenia and as a clinical tool.

Dynamics of the "Cognitive" Brain Wave P3b at Rest for Alzheimer Dementia Prediction in Mild Cognitive Impairment

Alzheimer's disease (AD) is the most common cause of dementia that involves a progressive and irrevocable decline in cognitive abilities and social behavior, thus annihilating the patient's autonomy. The theoretical assumption that disease-modifying drugs are most effective in the early stages hopefully in the prodromal stage called mild cognitive impairment (MCI) urgently pushes toward the identification of robust and individualized markers of cognitive decline to establish an early pharmacological intervention. This requires the combination of well-established neural mechanisms and the development of increasingly sensitive methodologies. Among the neurophysiological markers of attention and cognition, one of the sub-components of the 'cognitive brain wave' P300 recordable in an odd-ball paradigm -namely the P3b- is extensively regarded as a sensitive indicator of cognitive performance. Several studies have reliably shown that changes in the amplitude and latency of the P3b are strongly related to cognitive decline and aging both healthy and pathological. Here, we used a P3b spatial filter to enhance the electroencephalographic (EEG) characteristics underlying 175 subjects divided into 135 MCI subjects, 20 elderly controls (EC), and 20 young volunteers (Y). The Y group served to extract the P3b spatial filter from EEG data, which was later applied to the other groups during resting conditions with eyes open and without being asked to perform any task. The group of 135 MCI subjects could be divided into two subgroups at the end of a month follow-up: 75 with stable MCI (MCI-S, not converted to AD), 60 converted to AD (MCI-C). The P3b spatial filter was built by means of a signal processing method called Functional Source Separation (FSS), which increases signal-to-noise ratio by using a weighted sum of all EEG recording channels rather than relying on a single, or a small sub-set, of channels. A clear difference was observed for the P3b dynamics at rest between groups. Moreover, a machine learning approach showed that P3b at rest could correctly distinguish MCI from EC (80.6% accuracy) and MCI-S from MCI-C (74.1% accuracy), with an accuracy as high as 93.8% in discriminating between MCI-C and EC. Finally, a comparison of the Bayes factor revealed that the group differences among MCI-S and MCI-C were 138 times more likely to be detected using the P3b dynamics compared with the best performing single electrode (Pz) approach. In conclusion, we propose that P3b as measured through spatial filters can be safely regarded as a simple and sensitive marker to predict the conversion from an MCI to AD status eventually combined with other non-neurophysiological biomarkers for a more precise definition of dementia having neuropathological Alzheimer characteristics.

Does learning to read affect naming skills? Insights from ERPs during letter and picture naming tasks

Numerous studies report that poor readers display low performance in naming tasks. However, very few studies have investigated the development of naming skills along with the development of reading fluency and its variability in typically developing children. In this study, we used electro-encephalographic (EEG) recordings acquired during letter and picture naming tasks to investigate how naming skills develop and, possibly, interact with age and reading level variations. Ninety-three children aged 7-12 years named letters and pictures under an EEG recording, and their reading performance was assessed. ERP results on amplitudes show that age and reading level have similar effects on the entire letter naming time-course. By contrast, age and reading level have different effects on the picture naming time-course, with a specific effect of reading level on the N1 time-interval, associated with visuo-conceptual processing and an effect of both age and reading on later time-windows. On the microstate analysis, age remains the only predictor of the variance in global electric field at scalp for both letter and picture naming indicating that reading skill is not related to a modulation of the mental processes underlying naming.

Role of the Ipsilateral Primary Motor Cortex in the Visuo-Motor Network During Fine Contractions and Accurate Performance

It is widely recognized that continuous sensory feedback plays a crucial role in accurate motor control in everyday life. Feedback information is used to adapt force output and to correct errors. While primary motor cortex contralateral to the movement (cM1) plays a dominant role in this control, converging evidence supports the idea that ipsilateral primary motor cortex (iM1) also directly contributes to hand and finger movements. Similarly, when visual feedback is available, primary visual cortex (V1) and its interactions with the motor network also become important for accurate motor performance. To elucidate this issue, we performed and integrated behavioral and electroencephalography (EEG) measurements during isometric compression of a compliant rubber bulb, at 10% and 30% of maximum voluntary contraction, both with and without visual feedback. We used a semi-blind approach (functional source separation (FSS)) to identify separate functional sources of mu-frequency (8-13[Formula: see text]Hz) EEG responses in cM1, iM1 and V1. Here for the first time, we have used orthogonal FSS to extract multiple sources, by using the same functional constraint, providing the ability to extract different sources that oscillate in the same frequency range but that have different topographic distributions. We analyzed the single-trial timecourses of mu power event-related desynchronization (ERD) in these sources and linked them with force measurements to understand which aspects are most important for good task performance. Whilst the amplitude of mu power was not related to contraction force in any of the sources, it was able to provide information on performance quality. We observed stronger ERDs in both contralateral and ipsilateral motor sources during trials where contraction force was most consistently maintained. This effect was most prominent in the ipsilateral source, suggesting the importance of iM1 to accurate performance. This ERD effect was sustained throughout the duration of visual feedback trials, but only present at the start of no feedback trials, consistent with more variable performance in the absence of feedback. Overall, we found that the behavior of the ERD in iM1 was the most informative aspect concerning the accuracy of the contraction performance, and the ability to maintain a steady level of contraction. This new approach of using FSS to extract multiple orthogonal sources provides the ability to investigate both contralateral and ipsilateral nodes of the motor network without the need for additional information (e.g. electromyography). The enhanced signal-to-noise ratio provided by FSS opens the possibility of extracting complex EEG features on an individual trial basis, which is crucial for a more nuanced understanding of fine motor performance, as well as for applications in brain-computer interfacing.

Hybrid Deep Learning (hDL)-Based Brain-Computer Interface (BCI) Systems: A Systematic Review

BACKGROUND

Brain-Computer Interface (BCI) is becoming more reliable, thanks to the advantages of Artificial Intelligence (AI). Recently, hybrid Deep Learning (hDL), which combines different DL algorithms, has gained momentum over the past five years. In this work, we proposed a review on hDL-based BCI starting from the seminal studies in 2015.

OBJECTIVES

We have reviewed 47 papers that apply hDL to the BCI system published between 2015 and 2020 extracting trends and highlighting relevant aspects to the topic.

METHODS

We have queried four scientific search engines (Google Scholar, PubMed, IEEE Xplore and Elsevier Science Direct) and different data items were extracted from each paper such as the database used, kind of application, online/offline training, tasks used for the BCI, pre-processing methodology adopted, type of normalization used, which kind of features were extracted, type of DL architecture used, number of layers implemented and which optimization approach were used as well. All these items were then investigated one by one to uncover trends.

RESULTS

Our investigation reveals that Electroencephalography (EEG) has been the most used technique. Interestingly, despite the lower Signal-to-Noise Ratio (SNR) of the EEG data that makes pre-processing of that data mandatory, we have found that the pre-processing has only been used in 21.28% of the cases by showing that hDL seems to be able to overcome this intrinsic drawback of the EEG data. Temporal-features seem to be the most effective with 93.94% accuracy, while spatial-temporal features are the most used with 33.33% of the cases investigated. The most used architecture has been Convolutional Neural Network-Recurrent Neural Network CNN-RNN with 47% of the cases. Moreover, half of the studies have used a low number of layers to achieve a good compromise between the complexity of the network and computational efficiency.

SIGNIFICANCE

To give useful information to the scientific community, we make our summary table of hDL-based BCI papers available and invite the community to published work to contribute to it directly. We have indicated a list of open challenges, emphasizing the need to use neuroimaging techniques other than EEG, such as functional Near-Infrared Spectroscopy (fNIRS), deeper investigate the advantages and disadvantages of using pre-processing and the relationship with the accuracy obtained. To implement new combinations of architectures, such as RNN-based and Deep Belief Network DBN-based, it is necessary to better explore the frequency and temporal-frequency features of the data at hand.

A functional source separation algorithm to enhance error-related potentials monitoring in noninvasive brain-computer interface

BACKGROUND AND OBJECTIVES

An Error related Potential (ErrP) can be noninvasively and directly measured from the scalp through electroencephalography (EEG), as response, when a person realizes they are making an error during a task (as a consequence of a cognitive error performed from the user). It has been shown that ErrPs can be automatically detected with time-discrete feedback tasks, which are widely applied in the Brain-Computer Interface (BCI) field for error correction or adaptation. In this work, a semi-supervised algorithm, namely the Functional Source Separation (FSS), is proposed to estimate a spatial filter for learning the ErrPs and to enhance the evoked potentials.

METHODS

EEG data recorded on six subjects were used to evaluate the proposed method based on FFS algorithm in comparison with the xDAWN algorithm. FSS- and xDAWN-based methods were compared also to the Cz and FCz single channel. Single-trial classification was considered to evaluate the performances of the approaches. (Both the approaches were evaluated on single-trial classification of EEGs.) RESULTS: The results presented using the Bayesian Linear Discriminant Analysis (BLDA) classifier, show that FSS (accuracy 0.92, sensitivity 0.95, specificity 0.81, F1-score 0.95) overcomes the other methods (Cz - accuracy 0.72, sensitivity 0.74, specificity 0.63, F1-score 0.74; FCz - accuracy 0.72, sensitivity 0.75, specificity 0.61, F1-score 0.75; xDAWN - accuracy 0.75, sensitivity 0.79, specificity 0.61, F1-score 0.79) in terms of single-trial classification.

CONCLUSIONS

The proposed FSS-based method increases the single-trial detection accuracy of ErrPs with respect to both single channel (Cz, FCz) and xDAWN spatial filter.

Natural scene representations in the gamma band are prototypical across subjects

Prototypical brain responses describe similarity in neural representations between subjects in response to a natural stimulus. During natural movie viewing, for example, inter-subject correlation (ISC) measured by fMRI is high in visual areas (Hasson et al., 2004). But the electrophysiological basis for this fMRI ISC has been controversial. Previous reports have only found ISC in low frequency bands-below 12 ​Hz (Chang et al., 2015). These findings stand in contrast to reports that gamma band oscillations-30 to 90 ​Hz-are highly stimulus-driven in visual cortex (Perry et al., 2015). To resolve this discrepancy, we carried out both ISC estimation and a novel inter-subject representational correlation analysis across six frequency bands extracted from MEG data of 24 subjects who each viewed four 5-min clips of an underwater documentary. Region-of-interest-based and vertex-based temporal ISC estimates confirmed that low-frequency bands are significantly synchronized in visual areas and that gamma band has low temporal correlation. We also found the representational geometry of movie scenes were related to structural statistics from the stimuli. Crucially, our results show that the gamma band oscillations also reflect prototypical brain response in scene representations formed in response to naturalistic stimuli as revealed by inter-subject representational correlation.

Functional Source Separation for EEG-fMRI Fusion: Application to Steady-State Visual Evoked Potentials

Neurorobotics is one of the most ambitious fields in robotics, driving integration of interdisciplinary data and knowledge. One of the most productive areas of interdisciplinary research in this area has been the implementation of biologically-inspired mechanisms in the development of autonomous systems. Specifically, enabling such systems to display adaptive behavior such as learning from good and bad outcomes, has been achieved by quantifying and understanding the neural mechanisms of the brain networks mediating adaptive behaviors in humans and animals. For example, associative learning from aversive or dangerous outcomes is crucial for an autonomous system, to avoid dangerous situations in the future. A body of neuroscience research has suggested that the neurocomputations in the human brain during associative learning involve re-shaping of sensory responses. The nature of these adaptive changes in sensory processing during learning however are not yet well enough understood to be readily implemented into on-board algorithms for robotics application. Toward this overall goal, we record the simultaneous electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI), characterizing one candidate mechanism, i.e., large-scale brain oscillations. The present report examines the use of Functional Source Separation (FSS) as an optimization step in EEG-fMRI fusion that harnesses timing information to constrain the solutions that satisfy physiological assumptions. We applied this approach to the voxel-wise correlation of steady-state visual evoked potential (ssVEP) amplitude and blood oxygen level-dependent imaging (BOLD), across both time series. The results showed the benefit of FSS for the extraction of robust ssVEP signals during simultaneous EEG-fMRI recordings. Applied to data from a 3-phase aversive conditioning paradigm, the correlation maps across the three phases (habituation, acquisition, extinction) show converging results, notably major overlapping areas in both primary and extended visual cortical regions, including calcarine sulcus, lingual cortex, and cuneus. In addition, during the acquisition phase when aversive learning occurs, we observed additional correlations between ssVEP and BOLD in the anterior cingulate cortex (ACC) as well as the precuneus and superior temporal gyrus.

Early and Late Effects of Semantic Distractors on Electroencephalographic Responses During Overt Picture Naming

This study investigated the nature of the interference effect of semantically related distractors in the picture-word interference paradigm, which has been claimed to be caused by either competition between lexical representations of target and distractor or by a late response exclusion mechanism that removes the distractor from a response buffer. EEG was recorded while participants overtly named pictures accompanied by categorically related versus unrelated written distractor words. In contrast to previous studies, stimuli were presented for only 250 ms to avoid any re-processing. ERP effects of relatedness were found around 290, 470, 540, and 660 ms post stimulus onset. In addition, related distractors led to an increase in midfrontal theta power, especially from about 440 to 540 ms, as well as to decreased high beta power between 40 and 110 ms and increased high beta power between 275 and 340 ms post stimulus onset. Response-locked analyses showed no differences in ERPs, however increased low and high beta power for related distractors in various time windows, most importantly a high beta power increase between -175 and -155 ms before speech onset. These results suggest that the semantic distractor effect is a combination of various effects and that the lexical competition account and the response exclusion account each capture a part, but not all aspects of the effect.

Electrophysiological Characteristics of the Migraine Brain: Current Knowledge and Perspectives

BACKGROUND

Despite pain being its most prominent feature, migraine is primarily a disorder of sensory processing. Electrophysiology-based research in the field has consistently developed over the last fifty years.

OBJECTIVE

To summarize the current knowledge on the electrophysiological characteristics of the migraine brain, and discuss perspectives.

METHODS

We critically reviewed the literature on the topic to present and discuss articles selected on the basis of their significance and/or novelty.

RESULTS

Physiologic fluctuations within time, between-subject differences, and methodological issues account as major limitations of electrophysiological research in migraine. Nonetheless, several abnormalities revealed through different approaches have been described in the literature. Altogether, these results are compatible with an abnormal state of sensory processing.

PERSPECTIVES

The greatest contribution of electrophysiological testing in the future will most probably be the characterization of sub-groups of migraine patients sharing specific electrophysiological traits. This should serve as strategy towards personalized migraine treatment. Incorporation of novel methods of analysis would be worthwhile.

Brain Correlates of Single Trial Visual Evoked Potentials in Migraine: More Than Meets the Eye

Background: Using conventional visual evoked potentials (VEPs), migraine patients were found to be hyperresponsive to visual stimulus. Considering that a significant portion of neuronal activity is lost for analysis in the averaging process of conventional VEPs, in this study we investigated visual evoked responses of migraine patients and healthy volunteers using a different approach: single trial analysis. This method permits to preserve all stimulus-induced neuronal activations, whether they are synchronized or not. In addition, we used MRI voxel-based morphometry to search for cortical regions where gray matter volume correlated with single trial (st) VEP amplitude. Finally, using resting-state functional MRI, we explored the connectivity between these regions. Results: stVEP amplitude was greater in episodic migraine patients than in healthy volunteers. Moreover, in migraine patients it correlated positively with gray matter volume of several brain areas likely involved in visual processing, mostly belonging to the ventral attention network. Finally, resting state functional connectivity corroborated the existence of functional interactions between these areas and helped delineating their directions. Conclusions: st-VEPs appear to be a reliable measure of cerebral responsiveness to visual stimuli. Mean st-VEP amplitude is higher in episodic migraine patients compared to controls. Visual hyper-responsiveness in migraine involves several functionally-interconnected brain regions, suggesting that it is the result of a complex multi-regional process coupled to stimulus driven attention systems rather than a localized alteration.

Functional Semi-Blind Source Separation Identifies Primary Motor Area Without Active Motor Execution

High time resolution techniques are crucial for investigating the brain in action. Here, we propose a method to identify a section of the upper-limb motor area representation (FS_M1) by means of electroencephalographic (EEG) signals recorded during a completely passive condition (FS_M1bySS). We delivered a galvanic stimulation to the median nerve and we applied to EEG the semi-Blind Source Separation (s-BSS) algorithm named Functional Source Separation (FSS). In order to prove that FS_M1bySS is part of FS_M1, we also collected EEG in a motor condition, i.e. during a voluntary movement task (isometric handgrip) and in a rest condition, i.e. at rest with eyes open and closed. In motor condition, we show that the cortico-muscular coherence (CMC) of FS_M1bySS does not differ from FS_ M1 CMC (0.04 for both sources). Moreover, we show that the FS_M1bySS’s ongoing whole band activity during Motor and both rest conditions displays high mutual information and time correlation with FS_M1 (above 0.900 and 0.800, respectively) whereas much smaller ones with the primary somatosensory cortex [Formula: see text] (about 0.300 and 0.500, [Formula: see text]). FS_M1bySS as a marker of the upper-limb FS_M1 representation obtainable without the execution of an active motor task is a great achievement of the FSS algorithm, relevant in most experimental, neurological and psychiatric protocols.

Information and the Origin of Qualia

This article argues that qualia are a likely outcome of the processing of information in local cortical networks. It uses an information-based approach and makes a distinction between information structures (the physical embodiment of information in the brain, primarily patterns of action potentials), and information messages (the meaning of those structures to the brain, and the basis of qualia). It develops formal relationships between these two kinds of information, showing how information structures can represent messages, and how information messages can be identified from structures. The article applies this perspective to basic processing in cortical networks or ensembles, showing how networks can transform between the two kinds of information. The article argues that an input pattern of firing is identified by a network as an information message, and that the output pattern of firing generated is a representation of that message. If a network is encouraged to develop an attractor state through attention or other re-entrant processes, then the message identified each time physical information is cycled through the network becomes “representation of the previous message”. Using an example of olfactory perception, it is shown how this piggy-backing of messages on top of previous messages could lead to olfactory qualia. The message identified on each pass of information could evolve from inner identity, to inner form, to inner likeness or image. The outcome is an olfactory quale. It is shown that the same outcome could result from information cycled through a hierarchy of networks in a resonant state. The argument for qualia generation is applied to other sensory modalities, showing how, through a process of brain-wide constraint satisfaction, a particular state of consciousness could develop at any given moment. Evidence for some of the key predictions of the theory is presented, using ECoG data and studies of gamma oscillations and attractors, together with an outline of what further evidence is needed to provide support for the theory.

Rapid and Objective Assessment of Neural Function in Autism Spectrum Disorder Using Transient Visual Evoked Potentials

OBJECTIVE

There is a critical need to identify biomarkers and objective outcome measures that can be used to understand underlying neural mechanisms in autism spectrum disorder (ASD). Visual evoked potentials (VEPs) offer a noninvasive technique to evaluate the functional integrity of neural mechanisms, specifically visual pathways, while probing for disease pathophysiology.

METHODS

Transient VEPs (tVEPs) were obtained from 96 unmedicated children, including 37 children with ASD, 36 typically developing (TD) children, and 23 unaffected siblings (SIBS). A conventional contrast-reversing checkerboard condition was compared to a novel short-duration condition, which was developed to enable objective data collection from severely affected populations who are often excluded from electroencephalographic (EEG) studies.

RESULTS

Children with ASD showed significantly smaller amplitudes compared to TD children at two of the earliest critical VEP components, P60-N75 and N75-P100. SIBS showed intermediate responses relative to ASD and TD groups. There were no group differences in response latency. Frequency band analyses indicated significantly weaker responses for the ASD group in bands encompassing gamma-wave activity. Ninety-two percent of children with ASD were able to complete the short-duration condition compared to 68% for the standard condition.

CONCLUSIONS

The current study establishes the utility of a short-duration tVEP test for use in children at varying levels of functioning and describes neural abnormalities in children with idiopathic ASD. Implications for excitatory/inhibitory balance as well as the potential application of VEP for use in clinical trials are discussed.

Puzzle task ERP response: time-frequency and source localization analysis

Perceptual decision making depends on the choices available for the presented task. Most event-related potential (ERP) experiments are designed with two options, such as YES or NO. In some cases, however, subjects may become confused about the presented task in such a way that they cannot provide a behavioral response. This study aims to put subjects into such a puzzled state in order to address the following questions: How does the brain respond during puzzling moments? And what is the brain’s response to a non-answerable task? To address these questions, ERP were acquired from the brain during a scintillation grid illusion task. The subjects were required to count the number of illusory dots, a task that was impossible to perform. The results showed the presence of N130 over the parietal area during the puzzling task. Coherency among the brain hemispheres was enhanced with the complexity of the task. The neural generators’ source localizations were projected to a multimodal complex covering the left postcentral gyrus, supramarginal gyrus, and angular gyrus. This study concludes that the brain component N130 is strongly related to perception in a puzzling task network but not the visual processing network.

Prestimulus amplitudes modulate P1 latencies and evoked traveling alpha waves

Traveling waves have been well documented in the ongoing, and more recently also in the evoked EEG. In the present study we investigate what kind of physiological process might be responsible for inducing an evoked traveling wave. We used a semantic judgment task which already proved useful to study evoked traveling alpha waves that coincide with the appearance of the P1 component. We found that the P1 latency of the leading electrode is significantly correlated with prestimulus amplitude size and that this event is associated with a transient change in alpha frequency. We assume that cortical background excitability, as reflected by an increase in prestimulus amplitude, is responsible for the observed change in alpha frequency and the initiation of an evoked traveling trajectory.

Single-Trial Visual Evoked Potential Extraction Using Partial Least-Squares-Based Approach

A single-trial extraction of a visual evoked potential (VEP) signal based on the partial least-squares (PLS) regression method has been proposed in this paper. This paper has focused on the extraction and estimation of the latencies of P100, P200, P300, N75, and N135 in the artificial electroencephalograph (EEG) signal. The real EEG signal obtained from the hospital was only concentrated on the P100. The performance of the PLS has been evaluated mainly on the basis of latency error rate of the peaks for the artificial EEG signal, and the mean peak detection and standard deviation for the real EEG signal. The simulation results show that the proposed PLS algorithm is capable of reconstructing the EEG signal into its desired shape of the ideal VEP. For P100, the proposed PLS algorithm is able to provide comparable results to the generalized eigenvalue decomposition (GEVD) algorithm, which alters (prewhitens) the EEG input signal using the prestimulation EEG signal. It has also shown better performance for later peaks (P200 and P300). The PLS outperformed not only in positive peaks but also in N75. In P100, the PLS was comparable with the GEVD although N135 was better estimated by GEVD. The proposed PLS algorithm is comparable to GEVD given that PLS does not alter the EEG input signal. The PLS algorithm gives the best estimate to multitrial ensemble averaging. This research offers benefits such as avoiding patient's fatigue during VEP test measurement in the hospital, in BCI applications and in EEG-fMRI integration.

A constrained ICA approach for real-time cardiac artifact rejection in magnetoencephalography

Recently, magnetoencephalography (MEG)-based real-time brain computing interfaces (BCI) have been developed to enable novel and promising methods of neuroscience research and therapy. Artifact rejection prior to source localization largely enhances the localization accuracy. However, many BCI approaches neglect real-time artifact removal due to its time consuming processing. With cardiac artifact rejection for real-time analysis (CARTA), we introduce a novel algorithm capable of real-time cardiac artifact (CA) rejection. The method is based on constrained independent component analysis (ICA), where a priori information of the underlying source signal is used to optimize and accelerate signal decomposition. In CARTA, this is performed by estimating the subject's individual density distribution of the cardiac activity, which leads to a subject-specific signal decomposition algorithm. We show that the new method is capable of effectively reducing CAs within one iteration and a time delay of 1 ms. In contrast, Infomax and Extended Infomax ICA converged not until seven iterations, while FastICA needs at least ten iterations. CARTA was tested and applied to data from three different but most common MEG systems (4-D-Neuroimaging, VSM MedTech Inc., and Elekta Neuromag). Therefore, the new method contributes to reliable signal analysis utilizing BCI approaches.

Changes in saccadic eye movement (SEM) and quantitative EEG parameter in bipolar patients

BACKGROUND

There is increasing evidence that neurocognitive dysfunction is associated with the different states in Bipolar Disorder. Gamma coherence is strongly related to cognitive processes and cortico-cortical communication. This paper aims at shedding light on the relationship between cortical gamma coherence within bipolar patients and a control group during a prosaccadic attention task. We hypothesized that gamma coherence oscillations act as a main neural mechanism underlying information processing which changes in bipolar patients.

METHOD

Thirty-two (12 healthy controls and 20 bipolar patients) subjects were enrolled in this study. The subjects performed a prosaccadic attention task while their brain activity pattern was recorded using quantitative electroencephalography (20 channels).

RESULTS

We observed that the maniac group presented lower saccade latency when compared to depression and control groups. The main finding was a greater gamma coherence for control group in the right hemisphere of both frontal and motor cortices caused by the execution of a prosaccadic attention task.

LIMITATIONS

The findings need to be confirmed in larger samples and in bipolar patients before start the pharmacological treatment.

CONCLUSIONS

Our findings suggest a disrupted connection of the brain's entire functioning of maniac patients and represent a deregulation in cortical inhibitory mechanism. Thus, our results reinforce our hypothesis that greater gamma coherence in the right and left frontal cortices for the maniac group produces a "noise" during information processing and highlights that gamma coherence might be a biomarker for cognitive dysfunction during the manic state.

The spatiospectral characterization of brain networks: fusing concurrent EEG spectra and fMRI maps

Different imaging modalities capture different aspects of brain activity. Functional magnetic resonance imaging (fMRI) reveals intrinsic networks whose BOLD signals have periods from 100 s (0.01 Hz) to about 10s (0.1 Hz). Electroencephalographic (EEG) recordings, in contrast, commonly reflect cortical electrical fluctuations with periods up to 20 ms (50 Hz) or above. We examined the correspondence between intrinsic fMRI and EEG network activity at rest in order to characterize brain networks both spatially (with fMRI) and spectrally (with EEG). Brain networks were separately identified within the concurrently recorded fMRI and EEG at the aggregate group level with group independent component analysis and the association between spatial fMRI and frequency by spatial EEG sources was examined by deconvolving their component time courses. The two modalities are considered linked if the estimated impulse response function (IRF) is significantly non-zero at biologically plausible delays. We found that negative associations were primarily present within two of five alpha components, which highlights the importance of considering multiple alpha sources in EEG-fMRI. Positive associations were primarily present within the lower (e.g. delta and theta) and higher (e.g. upper beta and lower gamma) spectral regions, sometimes within the same fMRI components. Collectively, the results demonstrate a promising approach to characterize brain networks spatially and spectrally, and reveal that positive and negative associations appear within partially distinct regions of the EEG spectrum.