Pairs Of EEG Channels Research Articles

Automatic detection of obsessive-compulsive disorder from EEG signals based on Hilbert-Huang transform and sparse coding classification

Obsessive-compulsive disorder (OCD) is a chronic disease and psychosocial disorder that significantly reduces the quality of life of patients and affects their personal and social relationships. Therefore, early diagnosis of this disorder is of particular importance and has attracted the attention of researchers. In this research, new statistical differential features are used, which are suitable for EEG signals and have little computational load. Hilbert-Huang transform was applied to EEGs recorded from 26 OCD patients and 30 healthy subjects to extract instant amplitude and phase. Then, modified mean, variance, median, kurtosis and skewness were calculated from amplitude and phase data. Next, the difference of these statistical features between various pairs of EEG channels was calculated. Finally, different scenarios of feature classification were examined using the sparse nonnegative least squares classifier. The results showed that the modified mean feature calculated from the amplitude and phase of the interhemispheric channel pairs produces a high accuracy of 95.37%. The frontal lobe of the brain also created the most distinction between the two groups among other brain lobes by producing 90.52% accuracy. In addition, the features extracted from the frontal-parietal network produced the best classification accuracy (93.42%) compared to the other brain networks examined. The method proposed in this paper dramatically improves the accuracy of EEG classification of OCD patients from healthy individuals and produces much better results compared to previous machine learning techniques.

Minimal EEG channel selection for depression detection with connectivity features during sleep

Background and objectivesSleeping cortical electroencephalogram (EEG) has the potential for depression detection, for different sleep structure and cortical connection have been proved in depressed patients. However, the operation of multi-channel sleep EEG recording is cumbersome and requires laboratory equipment and professional sleep technician. Here, we focus on the depression detection using minimal sleep EEG channels. MethodsSixteen channels of EEG data of 30 patients with depression and 30 age-matched normal controls were recorded during sleep. Power spectral density of each single EEG channel was calculated, followed by measuring the symbolic transfer entropy (STE) and weighed phase lag index (WPLI) between EEG channel pairs in various frequency bands. Thereafter, these features were evaluated by F-score in the two-way classification (depression vs. control) of 30-s sleep EEG segments. Based on the F-score, entropy method was introduced to calculate the weight which could further assess the classification ability of various EEG channels or channel pairs. Finally, machine learning was implemented to verify the important EEG channels or channel pairs in depression diagnosis. ResultsThe features characterizing the inter-hemispheric connectivity in the posterior lobe, especially in the temporal lobe, showed high classification capacity. The classification accuracy of using two and four EEG channels in the temporal lobe were 97.96% and 99.61%, respectively. ConclusionsThis study showed the possibility of using only a few sleep EEG channels for depression screening, which may greatly facilitate the diagnosis of depression outside the hospital.

Design of an EEG analytical methodology for the analysis and interpretation of cerebral connectivity signals

The objective of this study is to design an Electroencephalographic (EEG) analytic methodology that allows to develop a variety of analysis and interpretations of brain signals. The initial phase considers the acquisition and filtering of EEG signals, the division into bands in data ranges, and the storage of EEG signals in a cloud data base. Then, an analytical phase considering descriptive, predictive and prescriptive analysis is accomplished. A sequence of analytic intermediate processing steps is done in order to render a graphic visualization of significant correlations between pairs of EEG channels. Pearson correlation is utilized to detect synchronic connectivity through the brain areas. Time series in nearly instantaneous time lapses are treated by using Hilbert Huang Transform. An experimental design by submitting a set of students to an abbreviated version Raven visual test is made providing results in correlation maps of cerebral connectivity.

Electroencephalography in Evaluating Mental Workload of Gaming.

The feasibility of electroencephalography (EEG) analysis in evaluating mental workload of gaming was studied by carrying out a proof-of-concept type experiment on a set of EEG recordings, with a bespoke tool developed for the purpose. The EEG recordings (20 recordings in total) that were used in the experiment had been acquired by groups of students and staff of Tampere University during n-back gaming sessions, as part of course projects. The ratio of theta and alpha power, calculated over the EEG signal segments that were time-locked to game events, was selected as EEG metrics for mental load evaluation. Also, Phase Locking Value (PLV) was calculated for all pairs of EEG channels to assess the change in phase synchronization with the increasing difficulty level of the game. Wilcoxon rank-sum test was used to compare the metrics between the levels of the game (from 1-back to 4-back). The rank-sum test results revealed that the theta-alpha power ratio calculated from the frontal derivations Fp1 and Fp2 performed as a confident indicator for the evaluation and comparison of mental load. Also, phase locking between EEG derivations was found to become stronger with the increasing difficulty level of the game, especially in the case of channel pairs where the electrodes were located at opposite hemispheres.

Effects of Mental Fatigue on Small-World Brain Functional Network Organization.

Brain functional network has been widely applied to investigate brain function changes among different conditions and proved to be a small-world-like network. But seldom researches explore the effects of mental fatigue on the small-world brain functional network organization. In the present study, 20 healthy individuals were included to do a consecutive mental arithmetic task to induce mental fatigue, and scalp electroencephalogram (EEG) signals were recorded before and after the task. Correlations between all pairs of EEG channels were determined by mutual information (MI). The resulting adjacency matrices were converted into brain functional networks by applying a threshold, and then, the clustering coefficient (C), characteristic path length (L), and corresponding small-world feature were calculated. Through performing analysis of variance (ANOVA) on the mean MI for every EEG rhythm, only the data of α1 rhythm during the task state were emerged for the further explorations of mental fatigue. For a wide range of thresholds, C increased and L and small-world feature decreased with the deepening mental fatigue. The pattern of the small-world characteristic still existed when computed with a constant degree. Our present findings indicated that more functional connectivities were activated at the mental fatigue stage for efficient information transmission and processing, and mental fatigue can be characterized by a reduced small-world network characteristic. Our results provide a new perspective to understand the neural mechanisms of mental fatigue based on complex network theories.

Directed Connectivity in Large-scale Brain Networks for Precision Grip Force Control.

Precision grip requires accurate but dynamic control of the magnitude and direction of fingertip forces. It is still not well known whether directed information flow across activated cortical regions could change with different force outputs during precision grip. This study aimed to investigate the directed connectivity in large-scale brain networks for precision grip force control. Eight healthy volunteers participated in the experiment. Totally 32 channel electroencephalography (EEG) signals were recorded during a precision grip task that requires accurate force control following a dynamically changed visual target. The force target changed between 10% and 1% Maximal Voluntary Contraction (MVC). Horizontal visibility graph transfer entropy (HVG-TE) was used to measure the directed connectivity between pairs of EEG channels. In addition, the relative HVG-TE (rHVG-TE) was applied to evaluate the activation of each EEG channel. Results showed relatively higher rHVG-TE values in the posterior brain region but lower rHVG-TE values in the anterior brain region within the whole spectrum (4-70Hz), indicating a posterior-to-anterior information flow. This study revealed that the activation of posterior region is higher than the anterior region. There is a directed functional connectivity of cortex from posterior to anterior region for precision grip force control. This study shed light on how to quantify activation and large-scale connectivity of cortical regions, and reveal the in-depth central mechanisms for peripheral fine motor control.

Intra and inter-individual variability in the chaotic component and functional connectivity of the EEG signal in basal eyes closed condition

Individuality has to do with our differences and similitudes, our phenotype and genotype characteristics that make us unique in some ways, and common in others. In this study we applied linear and non-linear quantitative description of the EEG signal of 8 subjects in closed eyes, basal conditions, three times at a day (at 10:00, 13:00, and 16:00 hrs. UTC -04:00 Santiago), to provide evidence that allows us to narrow the boundaries of intra and inter-individual differences in the behavior of the oscillatory electrical signal of the brain. We applied two versions of Hurst exponent estimator of persistence/anti-persistence of the brain signal, and a Spearman R inter-correlation analysis between pairs of EEG channels to quantify and allocate the magnitude of highly correlated pairs of electrodes as an indicator of enhanced brain synchrony. Results showed differences and patterns in laterality/symmetry associated with magnitude, self-organized long-memory processes; and brain synchrony.

Wavlet phase-locking based binary classification of hand movement directions from EEG

Objective. Brain signals can be used to extract relevant features to decode various limb movement parameters such as the direction of upper limb movements. Amplitude based feature extraction techniques have been used to study such motor activity of upper limbs whereas phase synchrony, used to estimate functional relationship between signals, has rarely been used to study single hand movements in different directions. Approach. In this paper, a novel phase-locking-based feature extraction method, called wavelet phase-locking value (W-PLV) is proposed to analyse synchronous EEG channel-pairs and classify hand movement directions. EEG data collected from seven subjects performing right hand movements in four orthogonal directions in the horizontal plane is used for this analysis. Main results. Our proposed W-PLV based method achieves a mean binary classification accuracy of 76.85% over seven subjects using wavelet levels corresponding to ⩽12 Hz EEG. The results also show direction-dependent information in various wavelet levels and indicate the presence of relevant information in slow cortical potentials (<1 Hz) as well as higher wavelet levels (⩽12 Hz). Significance. This study presents a thorough analysis of the phase-locking patterns extracted from EEG corresponding to hand movements in different directions using W-PLV across various wavelet levels and verifies their discriminative ability in the single trial binary classification of hand movement directions.

Altered Small-World Networks in First-Episode Schizophrenia Patients during Cool Executive Function Task.

At present, little is known about brain functional connectivity and its small-world topologic properties in first-episode schizophrenia (SZ) patients during cool executive function task. In this paper, the Trail Making Test-B (TMT-B) task was used to evaluate the cool executive function of first-episode SZ patients and electroencephalography (EEG) data were recorded from 14 first-episode SZ patients and 14 healthy controls during this cool executive function task. Brain functional connectivity between all pairs of EEG channels was constructed based on mutual information (MI) analysis. The constructed brain functional networks were filtered by three thresholding schemes: absolute threshold, mean degree, and a novel data-driven scheme based on orthogonal minimal spanning trees (OMST), and graph theory was then used to study the topographical characteristics of the filtered brain graphs. Results indicated that the graph theoretical measures of the theta band showed obvious difference between SZ patients and healthy controls. In the theta band, the characteristic path length was significantly longer and the cluster coefficient was significantly smaller in the SZ patients for a wide range of absolute threshold T. However, the cluster coefficient showed no significant changes, and the characteristic path length was still significantly longer in SZ patients when calculated as a function of mean degree K. Interestingly, we also found that only the characteristic path length was significantly longer in SZ patients compared with healthy controls after using the OMST scheme. Pearson correlation analysis showed that the characteristic path length was positively correlated with executive time of TMT-B for the combined SZ patients and healthy controls (r = 0.507, P = 0.006), but not for SZ patients alone (r = 0.072, P = 0.612). The above results suggested a less optimal organization of the brain network and could be useful for understanding the pathophysiologic mechanisms underlying cool executive dysfunction in first-episode SZ patients.

Cambios en la variabilidad temporal de la conectividad funcional electroencefalográfica durante el envejecimiento

Analizamos las alteraciones durante el envejecimiento en la variabilidad temporal de la conectividad funcional (CF) electroencefalográfica (EEG). La variabilidad en la CF se ha relacionado con el rendimiento cognitivo. El estudio se realizó en dos grupos de sujetos sanos: uno de 10 adultos entre 50-65 años y otro de 15 entre 66-85 años. Se realizaron registros EEG monopolares de 16 canales EEG en reposo: con ojos cerrados (OC) y bajo hiperventilación (HV). La CF cortical entre pares de canales EEG se estimó a partir de un índice (L) de sincronización no lineal generalizada. Para cada sujeto y condición, se calculó respectivamente, la conectividad global (CG) de cada área/canal con el resto de las mismas y la variabilidad temporal de dicha conectividad (VTC) a partir del promedio y desviación estándar de sucesivas medidas de CF. Los cambios con la edad/condición de CG y VTC de las áreas corticales se estimaron mediante MANOVA. Encontramos que la CG no cambia con la edad durante OC ni HV, pero la VTC del conjunto de áreas corticales es significativamente mayor (p&lt;0.01) en los sujetos menores de 65 años y fundamentalmente para las áreas centrales, temporales y parietales (p&lt;0.01). Concluimos que la disminución de la VTC con el envejecimiento podría estar relacionada con déficits en el rendimiento cognitivo.

Mid-Task Break Improves Global Integration of Functional Connectivity in Lower Alpha Band.

Numerous efforts have been devoted to revealing neurophysiological mechanisms of mental fatigue, aiming to find an effective way to reduce the undesirable fatigue-related outcomes. Until recently, mental fatigue is thought to be related to functional dysconnectivity among brain regions. However, the topological representation of brain functional connectivity altered by mental fatigue is only beginning to be revealed. In the current study, we applied a graph theoretical approach to analyse such topological alterations in the lower alpha band (8~10 Hz) of EEG data from 20 subjects undergoing a two-session experiment, in which one session includes four successive blocks with visual oddball tasks (session 1) whereas a mid-task break was introduced in the middle of four task blocks in the other session (session 2). Phase lag index (PLI) was then employed to measure functional connectivity strengths for all pairs of EEG channels. Behavior and connectivity maps were compared between the first and last task blocks in both sessions. Inverse efficiency scores (IES = reaction time/response accuracy) were significantly increased in the last task block, showing a clear effect of time-on-task in participants. Furthermore, a significant block-by-session interaction was revealed in the IES, suggesting the effectiveness of the mid-task break on maintaining task performance. More importantly, a significant session-independent deficit of global integration and an increase of local segregation were found in the last task block across both sessions, providing further support for the presence of a reshaped topology in functional brain connectivity networks under fatigue state. Moreover, a significant block-by-session interaction was revealed in the characteristic path length, small-worldness, and global efficiency, attributing to the significantly disrupted network topology in session 1 in comparison of the maintained network structure in session 2. Specifically, we found increased nodal betweenness centrality in several channels resided in frontal regions in session 1, resembling the observations of more segregated global architecture under fatigue state. Taken together, our findings provide insights into the substrates of brain functional dysconnectivity patterns for mental fatigue and reiterate the effectiveness of the mid-task break on maintaining brain network efficiency.

Design of a Decision-making Tool to Identify Cross-correlation Brain Dominances

The objective of this study is to design a decision-making tool to carry out a sequence of intermediate processing analytic steps in order to render a graphic visualization of significant correlations between pairs of EEG channels. We submitted a set of students to an abbreviated version of a visual intelligent test while we recorded EEG activity covering frontal, temporal, parietal and occipital areas of the brain cortex through the scalp. The graphs generated show correlations between specific ranges of R Spearman values and pairs of electrodes relevant for the particular study needed. We used this tool to make a descriptive classification of subjects according to the inter-channels correlation maps generated by our decision-making tool. We called correlation “dominances” to the areas defined for a high concentration of significant correlations. This term has been used previously as a way to classify styles of people's thinking-acting in relation to four descriptive referential brain areas. We contrasted our correlation dominances results with this psychological-questionnaire tool in search of consistencies. To design the brain connectivity module, we used Rapid Application Development (RAD) methodology. The tool has been developed in Matlab to analyze signal data ranges delta, theta, alpha, beta, and gamma; all these obtained from the EEG cleaned off artifacts, pre-processed and separated into bands using EEGLAB. Pearson correlation is utilized to detect synchronic connectivity dominances (correlations) through the brain areas. The results are presented in terms of cross-correlation maps, or correlation matrices, between the 14 channels of EEG signal for different EEG frequency bands.

EEG slow-wave mediates the fragmentation and coupling of cortical networks in propofol-induced general anesthesia

Electroencephalogram (EEG) recorded from propofol-induced general anesthesia is characterized by large amplitude slow-waves (0.1--1.5 Hz). Clinically, these lowest frequency components of the EEG signal become dominant over other higher frequency components during and after loss of consciousness [1]. However, it remains unclear how these slow oscillations are produced and to what extent they reflect changes in cortical network connectivity. Modeling anesthesia as a moderate reduction in interneuronal gap-junction coupling, a recent theoretical work by Steyn-Ross et al [2] predicts emergence of anesthetic slow-waves with chaotic dynamics. In the modeled anesthesia state, the weakened gap-junction coupling supports a codimension-2 bifurcation point where competing Turing (space) and Hopf (time) dynamics coexist, signifying spontaneous symmetry-breaking instabilities in the firing behavior of cortical neurons. Further, these chaotic slow-waves are found to perturb the neuronal coupling across the cortex, leading to a dramatic drop in global phase-coherence compared to its high level during consciousness. In this study, we analyze clinically-recorded EEG data to examine the model prediction for changes in phase-coherence between pairs of EEG channels in the sub-delta band during propofol anesthetic induction. Our study finds a coherence decrease in the frontal and occipital regions (see left panel of Figure ​Figure1),1), as well in the connection between them. Concomitantly, more strongly coupled neuronal activities are disclosed in the temporal-frontal, temporal-occipital and left-right temporal regions (right panel). Our clinical observation of reduced EEG coherence is consonant with cortical model predictions. However, our EEG study indicates that the coherence alternation is regional in nature, while the cortical model describes a spatially-uniform trend. Moreover, we did not find any theoretical prediction for the left- and right-temporal increased-coherence patterns. As the cortical model by Steyn-Ross et al. is spatially homogenous, i.e., there are no explicit front-to-back or right-to-left directionality, it is unable to produce regional coherence changes. It appears that the Steyn-Ross cortical model best represents the cortical dynamics in the frontal region. Figure 1 Correlation representations showing electrode pairs with significantly reduced (left, blue) or increased (right, pink) phase-coherence for sub-delta band (0.05-1.5 Hz) EEG induced by propofol anesthesia. Only electrode pairs (from 128 scalp electrodes) ...

Changes in the temporal variability of the EEG functional connectivity during aging

This work is aimed at investigating changes in the temporal variability of EEG functional connectivity (FC) during agin. The variability in the FC has been linked to cognitive performance. The study was carried out in two groups of healthy subjects: one of 10 adults between 50-65 years old and another of 15 subjects ranging 66-85 years. EEG recordings were made at rest using 16 monopolar channels: under eyes closed (EC) and under hyperventilation (HV). The cortical FC between all EEG channel pairs was estimated from an index (L) of nonlinear generalized synchronization. For each subject and condition, the global connectivity (GC) of each area/channel with the rest of them was calculated, then the GC average and the GC temporal variability (TVC) of such connectivities was computed from the GC mean and standard deviation of successive EEG recordings respectively. The changes with age and condition of GC and TVC of each cortical area were estimated via a MANOVA. We found that the GC does not change with age during OC or HV, but the TVC of all cortical areas is significantly higher (p&lt;0.01) for subjects under 65 and mainly for the central, temporal and parietal areas (p&lt;0.01). It is concluded that TVC decreasing with aging may be related to cognitive performance deficits.

Changes in the temporal variability of the EEG functional connectivity during aging

This work is aimed at investigating changes in the temporal variability of EEG functional connectivity (FC) during agin. The variability in the FC has been linked to cognitive performance. The study was carried out in two groups of healthy subjects: one of 10 adults between 50-65 years old and another of 15 subjects ranging 66-85 years. EEG recordings were made at rest using 16 monopolar channels: under eyes closed (EC) and under hyperventilation (HV). The cortical FC between all EEG channel pairs was estimated from an index (L) of nonlinear generalized synchronization. For each subject and condition, the global connectivity (GC) of each area/channel with the rest of them was calculated, then the GC average and the GC temporal variability (TVC) of such connectivities was computed from the GC mean and standard deviation of successive EEG recordings respectively. The changes with age and condition of GC and TVC of each cortical area were estimated via a MANOVA. We found that the GC does not change with age during OC or HV, but the TVC of all cortical areas is significantly higher (p&lt;0.01) for subjects under 65 and mainly for the central, temporal and parietal areas (p&lt;0.01). It is concluded that TVC decreasing with aging may be related to cognitive performance deficits.

Altered characteristic of brain networks in mild cognitive impairment during a selective attention task: An EEG study

The present study evaluated the topological properties of whole brain networks using graph theoretical concepts and investigated the time-evolution characteristic of brain network in mild cognitive impairment patients during a selective attention task. Electroencephalography (EEG) activities were recorded in 10 MCI patients and 17 healthy subjects when they performed a color match task. We calculated the phase synchrony index between each possible pairs of EEG channels in alpha and beta frequency bands and analyzed the local interconnectedness, overall connectedness and small-world characteristic of brain network in different degree for two groups. Relative to healthy normal controls, the properties of cortical networks in MCI patients tend to be a shift of randomization. Lower σ of MCI had suggested that patients had a further loss of small-world attribute both during active and resting states. Our results provide evidence for the functional disconnection of brain regions in MCI. Furthermore, we found the properties of cortical networks could reflect the processing of conflict information in the selective attention task. The human brain tends to be a more regular and efficient neural architecture in the late stage of information processing. In addition, the processing of conflict information needs stronger information integration and transfer between cortical areas.

Single-trial EEG classification using logistic regression based on ensemble synchronization.

In this paper, we propose an ensemble synchronization measure across all EEG channel pairs of a cluster based on Frobenius norm of the phase synchronization matrix, in a 0-1 scale enabling a direct comparison between clusters with different number of channels. Using this metric, we studied the intrahemispheric EEG synchronization in the lower gamma band (30-40 Hz) during 1229 single trials of an audio-visual integration cross modal task (CMT) recorded from five patients with schizophrenia and five healthy control subjects. Using ensemble synchronization measure and response latency of single trials recorded during the CMT as features for logistic regression, we could classify each single trial of EEG as belonging to a patient with schizophrenia or a healthy control subject with 73% accuracy, with an area under receiver operating characteristics curve of 0.83. We also propose a likelihood rating to denote the possibility of a subject belonging to the schizophrenia group.

Analysis of brain connectivity changes after propofol injection by generalized partial directed coherence

In this paper we present a method for the analysis of multichannel EEG by using Generalized Partial Directed Coherence (gPDC) to extract cortical connectivity changes under anesthesia. 15 channel EEG data were recorded from female subjects undergoing general anesthesia for gynecological surgery. Multivariate Autoregressive (MAR) modeling was applied to multichannel, bipolar EEG segments of awake and anesthetized states. gPDCs were calculated using the derived MAR model and averaged through EEG α frequency band (8–14 Hz) and through a number of data segments. The gPDC calculation was performed for three different sets of bipolar EEG channel pairs each of which mainly represent a specific scalp area. To derive consistency levels of gPDC values, surrogate analysis is also performed. Using paired t-test for 12 patients, we extracted significant gPDC changes between awake and anesthetized stages for each set. Analysis revealed that during transition from awake to anesthetized stage, gPDCs of central to parietal directions were suppressed whereas gPDCs of parietal to central directions were increased. The results indicate that the proposed gPDC analysis method can be used to track the changes in brain connectivity and hence to estimate the depth of anesthesia.

Disturbed resting state EEG synchronization in bipolar disorder: A graph-theoretic analysis

Disruption of functional connectivity may be a key feature of bipolar disorder (BD) which reflects disturbances of synchronization and oscillations within brain networks. We investigated whether the resting electroencephalogram (EEG) in patients with BD showed altered synchronization or network properties. Resting-state EEG was recorded in 57 BD type-I patients and 87 healthy control subjects. Functional connectivity between pairs of EEG channels was measured using synchronization likelihood (SL) for 5 frequency bands (δ, θ, α, β, and γ). Graph-theoretic analysis was applied to SL over the electrode array to assess network properties. BD patients showed a decrease of mean synchronization in the alpha band, and the decreases were greatest in fronto-central and centro-parietal connections. In addition, the clustering coefficient and global efficiency were decreased in BD patients, whereas the characteristic path length increased. We also found that the normalized characteristic path length and small-worldness were significantly correlated with depression scores in BD patients. These results suggest that BD patients show impaired neural synchronization at rest and a disruption of resting-state functional connectivity.

Graph theoretical analysis of EEG functional connectivity during music perception

The present study evaluated the effect of music on large-scale structure of functional brain networks using graph theoretical concepts. While most studies on music perception used Western music as an acoustic stimulus, Guqin music, representative of Eastern music, was selected for this experiment to increase our knowledge of music perception. Electroencephalography (EEG) was recorded from non-musician volunteers in three conditions: Guqin music, noise and silence backgrounds. Phase coherence was calculated in the alpha band and between all pairs of EEG channels to construct correlation matrices. Each resulting matrix was converted into a weighted graph using a threshold, and two network measures: the clustering coefficient and characteristic path length were calculated. Music perception was found to display a higher level mean phase coherence. Over the whole range of thresholds, the clustering coefficient was larger while listening to music, whereas the path length was smaller. Networks in music background still had a shorter characteristic path length even after the correction for differences in mean synchronization level among background conditions. This topological change indicated a more optimal structure under music perception. Thus, prominent small-world properties are confirmed in functional brain networks. Furthermore, music perception shows an increase of functional connectivity and an enhancement of small-world network organizations.