Directed Transfer Function Research Articles

Effective Connectivity of Beta Oscillations in Endometriosis-Related Chronic Pain During rest and Pain-Related Mental Imagery

Using the EEG recordings of patients with endometriosis-related chronic pelvic pain, we have examined the effective connectivity within the cortical pain-related network during rest and during pain-related imagery. During rest, an altered connectivity was hypothesized between cortical somatosensory pain areas and regions involved in emotional and cognitive modulation of pain. During pain-related imagery, alterations in prefrontal-temporal connectivity were expected. The effective connectivity was estimated using the Directed Transfer Function method. Differences between endometriosis patients and controls were found in the beta band (14–25 Hz). During rest, endometriosis was associated with an increased connectivity from the left dorsolateral prefrontal cortex to the left somatosensory cortex and also from the left somatosensory cortex to the orbitofrontal cortex and the right temporal cortex. These results might be related to sustained activation of the somatosensory pain system caused by the ongoing pain. During pain-related imagery, endometriosis patients showed an increased connectivity from the left dorsolateral prefrontal cortex to the right temporal cortex. This finding might point to impaired emotional regulation when processing pain-related stimuli, or it might be related to altered memorization of pain experiences. Results of this study open up new directions in chronic pain research aimed at exploring the beta band connectivity alterations. PerspectiveThis study examined the pain system's dynamics in endometriosis patients with chronic pelvic pain during resting-state and pain-related mental imagery. The results could contribute to the development of new therapies using guided mental imagery.

Causal Coupling Between Electrophysiological Signals, Cerebral Hemodynamics and Systemic Blood Supply Oscillations in Mayer Wave Frequency Range.

The aim of the study was to assess causal coupling between neuronal activity, microvascular hemodynamics and blood supply oscillations in the Mayer wave frequency range. An electroencephalogram, cerebral blood oxygenation changes, an electrocardiogram and blood pressure were recorded during rest and during a movement task. Causal coupling between them was evaluated using directed transfer function, a measure based on the Granger causality principle. The multivariate autoregressive model was fitted to all the signals simultaneously, which made it possible to construct a complete scheme of interactions between the considered signals. The obtained pattern of interactions in the resting state estimated in the 0.05-0.15 Hz band revealed a predominant influence of blood pressure oscillations on all the other variables. Reciprocal connections between blood pressure and heart rate variability time series indicated the presence of feedback loops between these signals. During movement, the pattern of connections did not change dramatically. The number of connections decreased, but the couplings between blood pressure and heart rate variability signal were not significantly changed, and the strong influence of the decreased blood hemoglobin concentration on the oxygenated hemoglobin concentration persisted. For the first time our results provided a comprehensive scheme of interactions between electrical and hemodynamic brain signals, heart rate and blood pressure oscillations. Persistent reciprocal connections between blood pressure and heart rate variability time series suggest possible feedforward and feedback coupling of cardiovascular variables which may lead to the observed oscillations in Mayer wave range.

P27-T Common pattern of information transfer in the brain for different modalities during a reasoning task

Background Information processing in the brain involves the synchronization of brain structures realized by the transmission of rhythmic activity. The aim of our work was to find connectivity patterns between brain structures during the same mental activity performed during tasks with different stimuli modality. Material and methods The task: the paradigm of linear syllogism was performed with visual (12 subjects) and auditory stimuli (29 subjects). The EEG activity transmission was analyzed by means of the short-time Directed Transfer Function (sDTF), a frequency dependent estimator of directed coupling between time series based on the Granger causality principle. Results and conclusions The obtained patterns showed similar coupling for both modalities involving EEG propagation mainly between the frontal and posterior areas which communicated intermittently. Our results indicate that the fronto-parietal network operated mainly in the theta frequency range as well as in other frequency ranges, especially gamma. By means of the assortative mixing approach, the strengths of coupling between the regions of interests (ROIs) were determined. For both modalities, the results showed stronger coupling within the ROIs than between them in agreement with the theories considering information transfer efficiency and metabolic energy savings. The patterns differences were minor and concerned stronger propagation from the posterior electrodes towards the frontal ones during the visual task and from the temporal sites to the frontal ones during the auditory task, which can be explained as bottom-up communication from specific sensory sites. Our results support a modality-free process of information retrieval and integration in a reasoning task.

Dynamics of impaired humour processing in schizophrenia – An EEG effective connectivity study

Specific language and communication abilities, such as humour comprehension, are commonly impaired in schizophrenia. The present study investigates the dynamics of the humour-related neural network underlying this deficit. Specifically, we focused on the abnormalities of information flow in schizophrenia within the fronto-temporo-parietal circuit. We estimated the direction and strength of cortical information flow in the time course of humour processing by the EEG Directed Transfer Function. The study included 40 schizophrenia outpatients and 40 healthy controls (age-sex-education matched) assessed with an EEG punchline-based humour comprehension task (written and cartoon jokes). The linear mixed models procedure was used to test group effects across three processes: 1. incongruity detection, 2. incongruity resolution and elaboration, 3. complete humour processing. Conjunction maps for both types of jokes were created to investigate fundamental between-group differences, beyond the context of modality. Clinical subjects indicated a lower level of understanding of the funny punchlines, indicated absurd punchlines as more understandable and gave higher funniness ratings to both absurd and neutral punchlines. The EEG effective connectivity results revealed that humour processing in schizophrenia engages alternative circuits, exhibiting a pronounced abnormal leftward shifted lateralization related to diminished activity of the right hemisphere in fronto-temporo-parietal regions. In conclusion, the present paper presents the dynamics of cortical propagation of information in the humour-related circuit as a neural substrate of humour impairment in schizophrenia.

Prediction of epilepsy seizure from multi-channel electroencephalogram by effective connectivity analysis using Granger causality and directed transfer function methods.

Epilepsy is a chronic disorder, which causes strange perceptions, muscle spasms, sometimes seizures, and loss of awareness, associated with abnormal neuronal activity in the brain. The goal of this study is to investigate how effective connectivity (EC) changes effect on unexpected seizures prediction, as this will authorize the patients to play it safe and avoid risk. We approve the hypothesis that EC variables near seizure change significantly so seizure can be predicted in accordance with this variation. We introduce two time-variant coefficients based on standard deviation of EC on Freiburg EEG dataset by using directed transfer function and Granger causality methods and compare index changes over the course of time in five different frequency bands. Comparison of the multivariate and bivariate analysis of factors is implemented in this investigation. The performance based on the suggested methods shows the seizure occurrence period is approximately 50min that is expected onset stated in, the maximum value of sensitivity approaching ~ 80%, and 0.33 FP/h is the false prediction rate. The findings revealed that greater accuracy and sensitivity are obtained by the designed system in comparison with the results of other works in the same condition. Even though these results still are not sufficient for clinical applications. Based on the conclusions, it can generally be observed that the greater results by DTF method are in the gamma and beta frequency bands.

Brain functional connectivity changes in long-term mental stress

Long-term psychological stress can highly influence brain structure and functions. However, there are only a few studies using electroencephalogram (EEG) that have examined this fact. The current study demonstrates a brain-computer interface (BCI) to classify EEG correlates of long-term mental stress in various mental states. The study was performed on 26 healthy right-handed university students and the examination period was considered as a long-term mental stressor. Two groups of subjects were selected based on their stress levels evaluated by the perceived stress scale (PSS-14). The subjects' EEG data were collected during eyes-open resting state and while they exposed to positive and negative emotional stimuli scored by self-assessment manikin questionnaire (SAM). Several types of features were extracted from EEG data including power spectrum density (PSD), laterality index (LI), correlation coefficient (CC), Canonical correlation analysis (CCA), magnitude square coherence estimation (MSCE), mutual information (MI), phase-slope index (PSI), Granger causality (GC) and directed transfer function (DTF). Subsequently, the extracted features were discriminated using several types of classifiers including knearest neighbor (KNN), support vector machine (SVM) and naive Bayesian (NB) classifiers. The proposed BCI was validated by one leave out method and investigation was done in different time windows using low and high-frequency resolutions, 7 and 36 frequency bands respectively. The results showed that the proposed system can accurately recognize the subjects' stress level in various mental states. Moreover, the MI as a functional and DTF as effective connectivity methods yield the highest classification accuracy compared to other feature extraction methods.

Application of causality analysis on nuclear reactor systems

Causality analysis is a substantial tool for identifying cause-and-effect links between different components of a system and has been extensively used in various areas of science such as neuroscience, climatology, and econometrics. This analysis is carried out in terms of the renormalized partial directed coherence and the directed transfer function connectivity measures. Applying such analysis in the nuclear reactor field is of paramount importance since it can help in inferring cause-and-effect relationships between highly coupled processes, and consequently, it can assist on the safe and reliable operation of a nuclear power plant during the occurrence of possible disturbances or malfunctions. The effectiveness of the connectivity analysis is demonstrated through several simulated and measured test cases. Results show that the connectivity analysis is able to identify accurately the importance and central role of the activation signal when it is applied on a simple analytical model and a simulated nuclear reactor system. In addition, the application on more realistic and complex measured data sets of a Swiss boiling water reactor illustrates the capability of this analysis to indicate possible causes behind the observed anomalies or trends observed at certain conditions and, more importantly, allows a better understanding of the underlying interactions among different neutronic and thermal-hydraulic processes.

Coupling Between Brain Structures During Visual and Auditory Working Memory Tasks.

Transmission of EEG activity during a visual and auditory version of the working memory task based on the paradigm of linear syllogism was investigated. Our aim was to find possible similarities and differences in the synchronization patterns between brain structures during the same mental activity performed on different modality stimuli. The EEG activity transmission was evaluated by means of full frequency Directed Transfer Function (ffDTF) and short-time Directed Transfer Function (SDTF). SDTF provided information on dynamical propagation of EEG activity. The assortative mixing approach was applied to quantify coupling between regions of interest encompassing frontal, central and two posterior modules. The results showed similar schemes of coupling for both modalities with stronger coupling within the regions of interests than between them, which is concordant with the theories concerning efficient wiring and metabolic energy saving. The patterns of transmission showed main sources of activity in the anterior and posterior regions communicating intermittently in a broad frequency range. The differences between the patterns of transmission between the visual and auditory versions of working memory tasks were subtle and involved bigger propagation from the posterior electrodes towards the frontal ones during the visual task as well as from the temporal sites to the frontal ones during the auditory task.

Effective connectivity analysis of iEEG and accurate localization of the epileptogenic focus at the onset of operculo-insular seizures

Recognition of insular epilepsy may sometimes be challenging due to the rapid speed at which insular seizures can spread throughout the cortex via extensive connections to surrounding cortices. The spectrum weighted adaptive directed transfer function, a multivariate causality-based effective connectivity measure, was applied to intracranial electroencephalography recordings to identify generators of seizure activity. A non-parametric test based on surrogate data testing was used to validate statistical significance of causal relations. Outflow and inflow of seizure activity were extracted from the computed transfer matrix. Recorded data of 21 seizures from seven patients were analyzed including five who were rendered seizure-free after operculo-insular resection. Effective connectivity analysis of 7 s following electrical onset confirmed an operculo-insular seizure origin in 5 patients with a good post-operative seizure outcome, and for whom the resected region was sampled by intracranial electroencephalography contacts. Different or additional seizure foci were identified in 2 patients with a bad post-operative seizure outcome. Findings highlight the feasibility of accurate operculo-insular seizure foci localization based on quantitative approaches.

Transient seizure onset network for localization of epileptogenic zone: effective connectivity and graph theory-based analyses of ECoG data in temporal lobe epilepsy.

Abnormal and dynamic epileptogenic networks cause difficulties for clinical epileptologists in the localization of the seizure onset zone (SOZ) and the epileptogenic zone (EZ) in preoperative assessments of patients with refractory epilepsy. The aim of this study is to investigate the characteristics of time-varying effective connectivity networks in various non-seizure and seizure periods, and to propose a quantitative approach for accurate localization of SOZ and EZ. We used electrocorticogram recordings in the temporal lobe and hippocampus from seven patients with temporal lobe epilepsy to characterize the effective connectivity dynamics at a high temporal resolution using the full-frequency adaptive directed transfer function (ffADTF) measure and five graph metrics, i.e., the out-degree (OD), closeness centrality (CC), betweenness centrality (BC), clustering coefficient (C), and local efficiency (LE). The ffADTF effective connectivity network was calculated and described in five frequency bands (δ, θ, α, β, and γ) and five seizure periods (pre-seizure, early seizure, mid-seizure, late seizure, and post-seizure). The cortical areas with high values of graph metrics in the transient seizure onset network were compared with the SOZ and EZ identified by clinical epileptologists and the results of epilepsy resection surgeries. Origination and propagation of epileptic activity were observed in the high time resolution ffADTF effective connectivity network throughout the entire seizure period. The seizure-specific transient seizure onset ffADTF network that emerged at seizure onset time remained for approximately 20-50ms with strong connections generated from both SOZ and EZ. The values of graph metrics in the SOZ and EZ were significantly larger than that in the other cortical areas. More cortical areas with the highest mean of graph metrics were the same as the clinically determined SOZ in the low-frequency δ and θ bands and in Engel Class I patients than in higher frequency α, β, and γ bands and in Engel Class II and III patients. The OD and C were more likely to localize the SOZ and EZ than CC, BC, and LE in the transient seizure onset network. The high temporal resolution ffADTF effective connectivity analysis combined with the graph theoretical analysis helps us to understand how epileptic activity is generated and propagated during the seizure period. The newly discovered seizure-specific transient seizure onset network could be an important biomarker and a promising tool for more precise localization of the SOZ and EZ in preoperative evaluations.

Repetitive transcranial magnetic stimulation (rTMS) modulates time-varying electroencephalography (EEG) network in primary insomnia patients: a TMS-EEG study

IntroductionRepetitive transcranial magnetic stimulation (rTMS) is a noninvasive method, and its application in the treatment of primary insomnia (PI) is poorly researched. PI is considered to be a functional disorder with abnormal brain network connections. TMS combined electroencephalography (EEG) is a valuable method for directly probing excitability and dynamic connectivity of different regions in the brain. We aimed to investigate the therapeutic efficacy of rTMS and its impact on brain network connections in PI patients. MethodsTwenty patients with PI were recruited in this study and received 1 Hz rTMS treatment for 14 consecutive days. The severity of symptoms was evaluated using the Pittsburgh Sleep Quality Index (PSQI), Insomnia Severity Index (ISI) and the Epworth Sleepiness Scale (ESS). Twenty minutes TMS-EEG were performed before and immediately after treatment. EEG data was collected by 10-20 EEG system with the sample rate of 1024 Hz. Time-varying EEG network was analyzed using the adaptive directed transfer function. The EEG network results of patients were compared with those of 20 healthy subjects. ResultsIn the present study, we found that PSQI, ISI, and ESS ratings of the patients decreased significantly (P＜0.05) after rTMS treatment, and the improvement in ratings lasted one month. Compared with healthy subjects, single-pulse TMS targeting right posterior parietal cortex of PI patients before therapy showed excessive information outflow from the left occipital region, the frontal mid-line region and the right posterior temporal region, in succession. Meanwhile, inadequate information outflow was seen in the right central region, right parietal region, as well as in the right temporal region. Compared with prior treatment, PI patients showed increased information outflow in the left temporal region and decreased information outflow in the frontal mid-line region. ConclusionLow frequency rTMS targeting the right posterior parietal cortex has significant positive effects on the treatment of PI that can last for at least one month and can reverse abnormal changes of time-varying EEG networks.

Identification of Early Vascular Dementia Patients With EEG Signal

Effort has been made to find biomarkers for vascular dementia (VaD). Nevertheless, the current findings are typically obtained through statistical tests of group level differences. In clinical practice, however, it is more common to perform individual level inferences, e.g., to determine if a subject is suffering from VaD, which cannot be resolved with statistical analysis. The goal of this study is to develop a method to effectively discriminate early VaD patients from normal controls by combining EEG features with machine learning methods. The EEG signals were recorded from a total of 15 VaD patients and 21 controls during a visual oddball task. Interregional directed connectivity was derived from directed transfer function (DTF) analysis and used as features in classification. Three machine learning methods, including linear discriminant analysis (LDA), error back-propagation (BP) neural network, and support vector machine (SVM) were used as classifiers, and their classification performance was compared. It was found that VaD patients can be effectively identified using the BP and SVM classifiers with high accuracy. In particular, when the SVM classifier was combined with feature selection by Fisher score, it reached an accuracy 86.11%, sensitivity 86.67%, and specificity 85.71%. The area under the curve (AUC, 0.854) indicates a good identification of VaD patients from the normal controls. Since the EEG is noninvasive, inexpensive, and widely available to use, the current study presents a novel clinical application of machine learning methods and could facilitate automatic screening and diagnosis of the VaD at an early stage in future.

Analysis of speech imagery using brain connectivity estimators on consonant-vowel-consonant words

Speech imagery refers to the perceptual experience of uttering speech to oneself without any articulation. In this paper, the neural correlations between brain regions associated with articulated and imagined speech processes of consonant-vowel-consonant (CVC) words are analysed using brain connectivity estimators. EEG coherence, a synchronisation parameter establishes the correlation between several cortical areas. To analyse the causal dependence, partial directed coherence (PDC) and directed transfer function (DTF) estimators are derived from multi-channel EEG data. From inter and intra hemispheric coherences it has been observed that theta, beta and gamma frequencies were dominant and words with same vowel having one consonant common have similar coherence values. Results inferred from intra-hemispheric PDC and DTF parameters show that the frontal and temporal regions of the left hemisphere are more activated for all the given speech imagery tasks. Thus, the analysis provides a significant step in understanding the neural interactions of the brain while thinking and articulating processes.

BOLD-fMRI activity informed by network variation of scalp EEG in juvenile myoclonic epilepsy.

Epilepsy is marked by hypersynchronous bursts of neuronal activity, and seizures can propagate variably to any and all areas, leading to brain network dynamic organization. However, the relationship between the network characteristics of scalp EEG and blood oxygenation level-dependent (BOLD) responses in epilepsy patients is still not well known. In this study, simultaneous EEG and fMRI data were acquired in 18 juvenile myoclonic epilepsy (JME) patients. Then, the adapted directed transfer function (ADTF) values between EEG electrodes were calculated to define the time-varying network. The variation of network information flow within sliding windows was used as a temporal regressor in fMRI analysis to predict the BOLD response. To investigate the EEG-dependent functional coupling among the responding regions, modulatory interactions were analyzed for network variation of scalp EEG and BOLD time courses. The results showed that BOLD activations associated with high network variation were mainly located in the thalamus, cerebellum, precuneus, inferior temporal lobe and sensorimotor-related areas, including the middle cingulate cortex (MCC), supplemental motor area (SMA), and paracentral lobule. BOLD deactivations associated with medium network variation were found in the frontal, parietal, and occipital areas. In addition, modulatory interaction analysis demonstrated predominantly directional negative modulation effects among the thalamus, cerebellum, frontal and sensorimotor-related areas. This study described a novel method to link BOLD response with simultaneous functional network organization of scalp EEG. These findings suggested the validity of predicting epileptic activity using functional connectivity variation between electrodes. The functional coupling among the thalamus, frontal regions, cerebellum and sensorimotor-related regions may be characteristically involved in epilepsy generation and propagation, which provides new insight into the pathophysiological mechanisms and intervene targets for JME.

Altered neural oscillations within and between sensorimotor cortex and parietal cortex in chronic jaw pain.

Pain perception is associated with priming of the motor system and the orienting of attention in healthy adults. These processes correspond with decreases in alpha and beta power in the sensorimotor and parietal cortices. The goal of the present study was to determine whether these findings extend to individuals with chronic pain. Individuals with chronic jaw pain and pain-free controls anticipated and experienced a low pain or a moderate pain-eliciting heat stimulus. Although stimuli were calibrated for each subject, stimulus temperature was not different between groups. High-density EEG data were collected during the anticipation and heat stimulation periods and were analyzed using independent component analyses, EEG source localization, and measure projection analyses. Direct directed transfer function was also estimated to identify frequency specific effective connectivity between regions. Between group differences were most evident during the heat stimulation period. We report three novel findings. First, the chronic jaw pain group had a relative increase in alpha and beta power and a relative decrease in theta and gamma power in sensorimotor cortex. Second, the chronic jaw pain group had a relative increase in power in the alpha and beta bands in parietal cortex. Third, the chronic jaw pain group had less connectivity strength in the beta and gamma bands between sensorimotor cortex and parietal cortex. Our findings show that the effect of chronic pain attenuates rather than magnifies neural responses to heat stimuli. We interpret these findings in the context of system-level changes in intrinsic sensorimotor and attentional circuits in chronic pain.

Improvement of neural activity in brain EEG network using somatosensory electrical stimulation

Objective To investigate the characteristics of brain network based on brain electrical activity induced by somatosensory electrical stimulation, and to provide a theoretical basis for further understanding the mechanism of brain neural plasticity induced by somatosensory electrical stimulation. Methods Ten healthy subjects were selected and a somatosensory electrical stimulation experiment was constructed based on the directed transfer function (DTF). In the experiment, the DTF causal connection matrixes of the 32-channel EEG data of Delta, Theta, Alpha and Beta bands were obtained under the somatosensory electrical target and non-target stimulation, and the differences of clustering coefficient and global efficiency between two stimulation states were contrasted based on graph theory. Results Under the target stimulation and non-target stimulation states, the regions with stronger DTA causal connections were mainly concentrated in FCz, Cz, CPz and Pz channels. The causal connection intensity under target stimulation state was greater than that of non-target stimulation. Also, in the Delta, Theta, and Alpha bands, the clustering coefficient under the target stimulation state was significantly higher than that in the non-target stimulation state (P<0.05). In the Delta and Theta bands, the global efficiency of the target stimulation state was significantly higher than that of the non-target stimulation state (P<0.05). Conclusions Somatosensory electrical stimulation can activate and induce EEG brain networks. In the target stimulation state, the role of the parietal lobe in the EEG causal network is enhanced, which helps to induce attention to specific brain region plasticity, and thus realizing the nerve rehabilitation in the brain regions of interest. While in the non-target stimulation state, the synergistic interactions between brain regions were enhanced, which helps to activate and induce a wide range of associations in the whole brain network, so as to promote the global neural activity in the brain. Key words: Somatosensory electrical stimulation; Target stimulation; Non-target stimulation; Directed transform function; Brain network

Long term power frequency electromagnetic fields exposure influences the causal network connection pattern of local field potentials during working memory

The possible influence of electromagnetic field (EMF) on the function of neural systems has been widely concerned. In this article, we intend to investigate the effects of long term power frequency EMF exposure on brain cognitive functions and it's mechanism. The Sprague-Dawley (SD) rats were randomly divided into 3 groups: the rats in EMF Ⅰ group were placed in the 2 mT power frequency EMF for 24 days. The rats in EMF Ⅱ group were placed in the 2 mT power frequency EMF for 48 days. The rats in control group were not exposed to the EMF. Then, the 16 channel local field potentials (LFPs) were recorded from rats' prefrontal cortex (PFC) in each group during the working memory (WM) tasks. The causal networks of LFPs were also established by applying the directed transfer function (DTF). Based on that, the differences of behavior and the LFPs network connection patterns between different groups were compared in order to investigate the influence of long term power frequency EMF exposure on working memory. The results showed the rats in the EMF Ⅱ group needed more training to reach the task correction criterion (over 80%). Moreover, the causal network connection strength and the global efficiency of the rats in EMF Ⅰ and EMF Ⅱ groups were significantly lower than the corresponding values of the control group. Meanwhile, significant differences of causal density values were found between EMF Ⅱ group and the other two groups. These results indicate that long term exposure to 2 mT power frequency EMF will reduce the connection strength and the information transfer efficiency of the LFPs causal network in the PFC, as well as the behavior performance of the rats. These results may explain the effect of EMF exposure on working memory from the view of neural network connectivity and provide a support for further studies on the mechanism of the effect of EMF on cognition.

Interictal spike connectivity in human epileptic neocortex

ObjectiveInterictal spikes are a biomarker of epilepsy, yet their precise roles are poorly understood. Using long-term neocortical recordings from epileptic patients, we investigated the spatial-temporal propagation patterns of interictal spiking. MethodsInterictal spikes were detected in 10 epileptic patients. Short time direct directed transfer function was used to map the spatial-temporal patterns of interictal spike onset and propagation across different cortical topographies. ResultsEach patient had unique interictal spike propagation pattern that was highly consistent across times, regardless of the frequency band. High spiking brain regions were often not spike onset regions. We observed frequent spike propagations to shorter distances and that the central sulcus forms a strong barrier to spike propagation. Spike onset and seizure onset seemed to be distinct networks in most cases. ConclusionsPatients in epilepsy have distinct and unique network of causal propagation pattern which are very consistent revealing the underlying epileptic network. Although spike are epileptic biomarkers, spike origin and seizure onset seems to be distinct in most cases. SignificanceUnderstanding patterns of interictal spike propagation could lead to the identification patient-specific epileptic networks amenable to surgical or other treatments.

HD-EEG Based Classification of Motor-Imagery Related Activity in Patients With Spinal Cord Injury.

Brain computer interfaces (BCIs) are thought to revolutionize rehabilitation after SCI, e.g., by controlling neuroprostheses, exoskeletons, functional electrical stimulation, or a combination of these components. However, most BCI research was performed in healthy volunteers and it is unknown whether these results can be translated to patients with spinal cord injury because of neuroplasticity. We sought to examine whether high-density EEG (HD-EEG) could improve the performance of motor-imagery classification in patients with SCI. We recorded HD-EEG with 256 channels in 22 healthy controls and 7 patients with 14 recordings (4 patients had more than one recording) in an event related design. Participants were instructed acoustically to either imagine, execute, or observe foot and hand movements, or to rest. We calculated Fast Fourier Transform (FFT) and full frequency directed transfer function (ffDTF) for each condition and classified conditions pairwise with support vector machines when using only 2 channels over the sensorimotor area, full 10-20 montage, high-density montage of the sensorimotor cortex, and full HD-montage. Classification accuracies were comparable between patients and controls, with an advantage for controls for classifications that involved the foot movement condition. Full montages led to better results for both groups (p < 0.001), and classification accuracies were higher for FFT than for ffDTF (p < 0.001), for which the feature vector might be too long. However, full-montage 10–20 montage was comparable to high-density configurations. Motor-imagery driven control of neuroprostheses or BCI systems may perform as well in patients as in healthy volunteers with adequate technical configuration. We suggest the use of a whole-head montage and analysis of a broad frequency range.

Effects of disparity on visual discomfort caused by short-term stereoscopic viewing based on electroencephalograph analysis

BackgroundDiscomfort evoked by stereoscopic depth has been widely concerned. Previous studies have proposed a comfortable disparity range and considered that disparities exceed this range would cause visual discomfort. Brain activity recordings including Electroencephalograph (EEG) monitoring enable better understanding of perceptual and cognitive processes related to stereo depth-induced visual comfort.MethodsEEG data was collected using a stereo-visual evoked potential (VEP) test system by providing visual stimulus to subjects aged from 21 to 25 with normal stereoscopic vision. For each type of visual stimulus, data were processed using directed transfer function (DTF) and adaptive directed transfer function (ADTF) in combination with subjective feedbacks (comfort or discomfort). The topographies of information flow were constructed to compare responses stimulated by different stereoscopic depth, and to determine the difference in comfort and discomfort situations upon stimulation with same stereoscopic depth.ResultsBased on EEG analysis results, we found that the occipital P270 was moderately related to the disparity. Moreover, the ADTF of P270 showed that the information flows at frontal lobe and central-parietal lobe changed when stimulation with different stereoscopic depth applied. As to the stereo images with same stereoscopic depth, the DTF outflows at the temporal and temporal-parietal lobes in δ band, central and central-parietal lobes in α and θ bands, and the comparison of inflows in these three bands could be considered as discriminated indexes for matching the stereoscopic effect with viewers’ comfort or discomfort state impacted by disparity. The subjective feedbacks indicated that the comfort judgments remained as a result of cumulative effect.ConclusionsThis study proposed a short-term stereo-VEP experiment that shorted the duration of each stimulus in the experimental scheme to minimize the interference from other factors except the disparity. The occipital P270 had a mid-relevance to the disparity and its ADTF showed the affected areas when viewers are receiving stimulations with different disparities. DTF could be considered as discriminated indexes for matching the stereoscopic effect with viewers’ comfort or discomfort state induced by disparity. This study proposed a preferable experiment to observe the single effect of disparity and provided an intuitive and easy-to-read result in a more convenient manner.