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.

Improved Denoising of Structural Vibration Data Employing Bilateral Filtering.

With the continuous advancement of data acquisition and signal processing, sensors, and wireless communication, copious research work has been done using vibration response signals for structural damage detection. However, in actual projects, vibration signals are often subject to noise interference during acquisition and transmission, thereby reducing the accuracy of damage identification. In order to effectively remove the noise interference, bilateral filtering, a filtering method commonly used in the field of image processing for improving data signal-to-noise ratio was introduced. Based on the Gaussian filter, the method constructs a bilateral filtering kernel function by multiplying the spatial proximity Gaussian kernel function and the numerical similarity Gaussian kernel function and replaces the current data with the data obtained by weighting the neighborhood data, thereby implementing filtering. By processing the simulated data and experimental data, introducing a time-frequency analysis method and a method for calculating the time-frequency spectrum energy, the denoising abilities of median filtering, wavelet denoising and bilateral filtering were compared. The results show that the bilateral filtering method can better preserve the details of the effective signal while suppressing the noise interference and effectively improve the data quality for structural damage detection. The effectiveness and feasibility of the bilateral filtering method applied to the noise suppression of vibration signals is verified.

Automated detection of auditory response: applying sequential detection strategies with constant significance level to magnitude-squared coherence.

Abstracts Objective: The detection of the auditory steady-state responses is usually performed by an appropriate objective response detector applied to stimulus-related epochs of the raw electroencephalogram (EEG). In order to improve the detection time, sequential detection strategies are usually used. These multiple tests strategies increase the probability of mistakenly detecting a response. The aim of this study was to develop strategies to determine the critical values for the sequential detection strategies based on constant significance level tests. Design: Extensive Monte Carlo simulations were used to test these strategies for the magnitude-squared coherence (MSC) detector. The performances of these strategies were compared with previous works found in the literature. Study sample: All strategies were applied to synthetic and real EEG datasets. Results: The strategies ensure the desired significance level at the end of the sequential detection strategy. The simulated results are in accordance with the real data results. Conclusions: For the MSC detector, where the critical value depends on the number of epochs, the proposed sequential detection strategies obtain better performance regarding test time and detection rate, but worse overall detection rate compared to applying a test only once.

Quantification and statistical analysis of the transient visual evoked potential to a contrast-reversing pattern: A frequency-domain approach.

Visual function is often assessed by recording transient visual evoked potentials to contrast reversal of spatial patterns (tVEP-CR). This technique relies on measurements of amplitudes and peak times of a few points in the time-domain waveform, which require subjective selection of appropriate time points in a possibly noisy waveform and ignores much of the informational content in the response. Here, we introduce a set of frequency-domain measures that capture the full content of the response. Magnitude-squared coherence is used to determine the significance and reliability of magnitude measures; estimates of time delay are based on frequency-domain phase measures. In Study 1, extensive testing of a small number of observers revealed response details, and in Study 2, testing of a larger sample verified the novel frequency-domain measures and demonstrated the validity of a short-duration technique to produce reliable tVEP-CRs. In addition, Study 2 revealed adaptation effects present under prolonged stimulation conditions. Principal component analyses provided evidence for six distinct frequency mechanisms, and comparisons with time-domain measures indicated that power in high-frequency bands may be used as objective measures of excitatory input to visual cortex. A middle-frequency band captures the major peaks in the tVEP-CR waveform, and its power is highly correlated with the standard peak-to-trough amplitude measure. These novel frequency-domain indices may serve as more precise and powerful tools to assess visual function in healthy and diseased states.

A Robust Real Time Direction-of-Arrival Estimation Method for Sequential Movement Events of Vehicles.

Parameters estimation of sequential movement events of vehicles is facing the challenges of noise interferences and the demands of portable implementation. In this paper, we propose a robust direction-of-arrival (DOA) estimation method for the sequential movement events of vehicles based on a small Micro-Electro-Mechanical System (MEMS) microphone array system. Inspired by the incoherent signal-subspace method (ISM), the method that is proposed in this work employs multiple sub-bands, which are selected from the wideband signals with high magnitude-squared coherence to track moving vehicles in the presence of wind noise. The field test results demonstrate that the proposed method has a better performance in emulating the DOA of a moving vehicle even in the case of severe wind interference than the narrowband multiple signal classification (MUSIC) method, the sub-band DOA estimation method, and the classical two-sided correlation transformation (TCT) method.

Predictive value of N95 waveforms of pattern electroretinograms (PERGs) in children with optic nerve hypoplasia (ONH).

PURPOSE: As part of a long-term, prospective study of prenatal and clinical risk factors for optic nerve hypoplasia (ONH) at Children's Hospital Los Angeles, pattern ERGs (PERGs) were evaluated for prognostic value using an automated objective and robust analytical method. METHODS: Participants were 33 children with ophthalmoscopically diagnosed ONH [disc diameter-to-disc macula ratio (DD/DM) less than 0.35 in one or both eyes on fundus photographs]. Using cycloplegia and chloral hydrate sedation in one session before 26 months of age, we recorded PERGs to checkerboard reversal using five check sizes. Participants were followed with clinical and psychometric testing until 5 years of age. PERGs were analysed using automated robust statistics based on magnitude-squared coherence and bootstrapping optimized to objectively quantify PERG recovery in the challenging recordings encountered in young patients. PERG measures in the fixating or better-seeing eyes were compared with visual outcome data. RESULTS: PERG recording was complete to at least three check sizes in all eyes and to all five sizes in 79%. Probability of recording a PERG that is significantly different from noise varied with check size from 73% for the largest checks to 30% for the smallest checks (p = 0.002); smaller waveforms were associated with earlier implicit times. The presence of significant PERGs in infancy is associated with better visual outcomes; the strongest association with visual outcome was for the threshold check size with a significant N95 component (ρ = 0.398, p = 0.02). CONCLUSIONS: Automated statistically robust signal-processing techniques reliably and objectively detect PERGs in young children with ONH and show that congenital deficits of retinal ganglion cells are associated with diminished or non-detectable PERGs. The later negativity, N95, was the best indicator of visual prognosis and was most useful to identify those with good visual outcomes (≤0.4 LogMAR). Although PERGs reflect function of the inner layers of the central retina, they lack the specificity required to determine prognosis reliably in individual cases.

Objective detection of auditory steady-state evoked potentials based on mutual information.

OBJECTIVE: Recently, we developed a metric to objectively detect human auditory evoked potentials based on the mutual information (MI) between neural responses and stimulus spectrograms. Here, the MI algorithm is evaluated further for validity in testing the auditory steady-state response (ASSR), a sustained potential used in objective audiometry. DESIGN: MI was computed between spectrograms of ASSRs and their evoking stimuli to quantify the shared time-frequency information between neuroelectric activity and stimulus acoustics. MI was compared against two traditional ASSR detection metrics: F-test and magnitude-squared coherence (MSC). STUDY SAMPLE: Using an empirically derived threshold (⊖MI=1.45), MI was applied as a binary classifier to distinguish actual biological responses recorded in human participants (n=11) from sham recordings, containing only EEG noise (i.e., non-stimulus-control condition). RESULTS: MI achieved high overall accuracy (>90%) in identifying true ASSRs from sham recordings, with true positive/true negative rates of 82/100%. During online averaging, comparison with two other indices (F-test, MSC) indicated that MI could detect ASSRs in roughly half the number of trials (i.e., ∼400 sweeps) as the MSC and performed comparably to the F-test, but showed slightly better signal detection performance. CONCLUSIONS: MI provides an alternative, more flexible metric for efficient and automated ASSR detection.

Design of a Direction-of-Arrival Estimation Method Used for an Automatic Bearing Tracking System.

In this paper, we introduce a sub-band direction-of-arrival (DOA) estimation method suitable for employment within an automatic bearing tracking system. Inspired by the magnitude-squared coherence (MSC), we extend the MSC to the sub-band and propose the sub-band magnitude-squared coherence (SMSC) to measure the coherence between the frequency sub-bands of wideband signals. Then, we design a sub-band DOA estimation method which chooses a sub-band from the wideband signals by SMSC for the bearing tracking system. The simulations demonstrate that the sub-band method has a good tradeoff between the wideband methods and narrowband methods in terms of the estimation accuracy, spatial resolution, and computational cost. The proposed method was also tested in the field environment with the bearing tracking system, which also showed a good performance.

EEG spatial inter-channel connectivity analysis: A GCN-based dual stream approach to distinguish mental fatigue status.

Mental fatigue is defined as a decline in the ability and efficiency of mental activities. A lot of research suggests that the transition from alertness to fatigue is accompanied by alterations in correlation patterns among various brain regions. However, conventional methods for detecting mental fatigue seldom emphases inter-channel connectivity in the spatial domain. To fill this gap, this paper explores the spatial inter-channel connectivity in alertness and fatigue, employing spectral graph convolutional networks (GCN) for mental fatigue detection. We utilized Pearson correlation coefficients (PCC) to establish temporal connections and magnitude-squared coherence (MSC) for spectral connections. Topological features of the brain network were then analysed. To enhance the learning of spatial inter-channel connectivity, a dual-graph strategy transforms edge features into node features, serving as inputs to the spectral GCN. By simultaneously learning PCC and MSC features, the model results indicate significant differences in some brain network characteristics between alert and fatigue states. It confirms that the synchronicity of brain operations differs in the alert state compared to mental fatigue, and indicates that fatigue states can influence correlation patterns among different brain regions. Our approach is evaluated on a self-designed experimental dataset containing 7 subjects, demonstrating a classification accuracy of 89.59 % in group-level experiments and 95.24 % at the subject level. Additionally, on the public dataset SEED-VIG containing 23 subjects, our method achieves an accuracy of 86.58 %. In summary, this paper proposes a neural network approach based on a dynamic functional connectivity network. The network integrates both temporal and spectral connections with the goal of simultaneously learning spatial inter-channel connectivity in time and frequency domains. This effectively accomplishes fatigue state detection, highlighting that fatigue significantly influences correlations among different brain regions.

Inter-hemispheric somatosensory coherence and parental stress in hypersensitivity at 8 months old: An electroencephalography study.

OBJECTIVE: Infant hypersensitivity affects daily challenges and parental stress. Although the crucial role of tactile sensation in infants' brain function has been highlighted, hypersensitive infants and their families lack support. Electroencephalography may be useful for understanding hypersensitivity traits. We investigated the relationship between infant perceptual hypersensitivity and parental stress, somatosensory-evoked potential (SEP), and magnitude-squared coherence (MSC) in the general population. METHODS: Infants aged 8 months (n = 63) were evaluated for hypersensitivity and parental stress using a questionnaire and for cortical activity using electroencephalography. Vibration stimuli were applied to the infant's left foot. SEP components that peaked around 150 ms (N2) and at 200 ms (P2) after stimulus onset were evaluated by amplitude and latency at the midline electrode (Cz) and MSC between the midline electrodes (C3-C4). RESULTS: Parental stress was associated with infant hypersensitivity. The latency of Cz was delayed, and C3-C4 delta MSC was high in infants with hypersensitivity. CONCLUSIONS: Increasing inter-hemispheric MSC synchrony in the stimulated condition in infants with hypersensitivity suggested atypical somatosensory cortical function. SIGNIFICANCE: These findings contribute to identifying, understanding the mechanisms of, and developing effective coping strategies for early-stage hypersensitivity.

Stability of infraslow correlation structure in time-shifted intracranial EEG signals.

It is increasingly understood that the epilepsies are characterized by network pathology that can span multiple spatial and temporal scales. Recent work indicates that infraslow (<0.2 Hz) envelope correlations may form a basis for distant spatial coupling in the brain. We speculated that infraslow correlation structure may be preserved even with some time lag between signals. To this end, we studied intracranial EEG (icEEG) data collected from 22 medically refractory epilepsy patients. For each patient, we selected hour-long background, awake icEEG epochs before and after antiseizure medication (ASM) taper. For each epoch, we selected 5,000 random electrode contact pairs and estimated magnitude-squared coherence (MSC) below 0.15 Hz of band power time-series in the traditional EEG frequency bands. Using these same contact pairs, we shifted one signal of the pair by random durations in 15-s increments between 0 and 300 s. We aggregated these data across all patients to determine how infraslow MSC varies with duration of lag. We further examined the effect of ASM taper on infraslow correlation structure. We also used surrogate data to empirically characterize MSC estimator and to set optimal parameters for estimation specifically for the study of infraslow activity. Our empirical analysis of the MSC estimator showed that hour-long segments with MSC computed using 3-min windows with 50% overlap was sufficient to capture infraslow envelope correlations while minimizing estimator bias and variance. The mean MSC decreased monotonically with increasing time lag until 105 s of lag, then plateaued between 106 and 300 s. Significantly nonzero infraslow envelope MSC was preserved in all frequency bands until about 1 min of time lag, both pre- and post-ASM taper. We also saw a slight, but significant increase in infraslow MSC post-ASM taper, consistent with prior work. These results provide evidence for the feasibility of examining infraslow activity via its modulation of higher-frequency activity in the absence of DC-coupled recordings. The use of surrogate data also provides a general methodology for benchmarking measures used in network neuroscience studies. Finally, our study points to the clinical relevance of infraslow activity in assessing seizure risk.

Automated detection of auditory response: non-detection stopping criterion and repeatability studies for multichannel EEG.

An Auditory Steady-State Response (ASSR) is a valuable tool for determining auditory thresholds in individuals who are either unable or unwilling to cooperate with conventional behavioral testing methods. This study proposes a sequential test technique for automatic detection of ASSRs, incorporating a non-detection stopping criterion. The electrophysiological thresholds of a normal hearing volunteer were established using data collected from multichannel EEG signals. The detection probabilities and critical values were obtained via Monte Carlo simulations. Remarkably, application of the non-detection stopping criterion resulted in a 60% reduction in exam time in the absence of a response. These findings clearly demonstrate the significant potential of the sequential test in enhancing the performance of automatic audiometry.

Investigating the application of graph theory features in hand movement directions decoding using EEG signals.

In recent years, functional analysis of brain networks based on graph theory properties has attracted considerable attention. This approach has usually been exploited for structural and functional brain analysis, while its potential in motor decoding tasks has remained unexplored. This study aimed to investigate the feasibility of using graph-based features in hand direction decoding in movement execution and preparation intervals. Hence, EEG signals were recorded from nine healthy subjects while performing a four-target center-out reaching task. The functional brain network was calculated based on the magnitude-squared coherence (MSC) at six frequency bands. Then, the features based on eight graph theory metrics were extracted from brain networks. The classification was performed with a support vector machine classifier. The results revealed that in four-class direction discrimination, the mean accuracy of the graph-based method surpassed 63% and 53% on movement and pre-movement data, respectively. Additionally, a feature fusion approach that combines the graph theory features with power features was proposed. The fusion method raised the classification accuracy to 70.8% and 61.2% for movement and pre-movement intervals, respectively. This work has verified the feasibility of using graph theory properties and their superiority over band power features in a hand movement decoding task.

Connections between spatially distant primary language regions strengthen with age during infancy, as revealed by resting-state fNIRS.

Objective.Hearing is an important sensory function that plays a key role in how children learn to speak and develop language skills. Although previous neuroimaging studies have established that much of brain network maturation happens in early childhood, our understanding of the developmental trajectory of language areas is still very limited. We hypothesized that typical development trajectory of language areas in early childhood could be established by analyzing the changes of functional connectivity in normal hearing infants at different ages using functional near-infrared spectroscopy.Approach.Resting-state data were recorded from two bilateral temporal and prefrontal regions associated with language processing by measuring the relative changes of oxy-hemoglobin (HbO) and deoxy-hemoglobin (HbR) concentrations. Connectivity was calculated using magnitude-squared coherence of channel pairs located in (a) inter-hemispheric homologous and (b) intra-hemispheric brain regions to assess connectivity between homologous regions across hemispheres and two regions of interest in the same hemisphere, respectively.Main results.A linear regression model fitted to the age vs coherence of inter-hemispheric homologous test group revealed a significant coefficient of determination for both HbO (R2= 0.216,p= 0.0169) and HbR (R2= 0.206,p= 0.0198). A significant coefficient of determination was also found for intra-hemispheric test group for HbO (R2= 0.237,p= 0.0117) but not for HbR (R2= 0.111,p= 0.0956).Significance.The findings from HbO data suggest that both inter-hemispheric homologous and intra-hemispheric connectivity between primary language regions significantly strengthen with age in the first year of life. Mapping out the developmental trajectory of primary language areas of normal hearing infants as measured by functional connectivity could potentially allow us to better understand the altered connectivity and its effects on language delays in infants with hearing impairments.

A Feature Extraction Method for Seizure Detection Based on Multi-Site Synchronous Changes and Edge Detection Algorithm.

Automatic detection of epileptic seizures is important in epilepsy control and treatment, and specific feature extraction assists in accurate detection. We developed a feature extraction method for seizure detection based on multi-site synchronous changes and an edge detection algorithm. We investigated five chronic temporal lobe epilepsy rats with 8- and 12-channel detection sites in the hippocampus and limbic system. Multi-site synchronous changes were selected as a specific feature and implemented as a seizure detection method. For preprocessing, we used magnitude-squared coherence maps and Canny edge detection algorithm to find the frequency band with the most significant change in synchronization and the important channel pairs. In detection, we used the maximal cross-correlation coefficient as an indicator of synchronization and the correlation coefficient curves' average value and standard deviation as two detection features. The method achieved high performance, with an average 96.60% detection rate, 2.63/h false alarm rate, and 1.25 s detection delay. The experimental results show that synchronization is an appropriate feature for seizure detection. The magnitude-squared coherence map can assist in selecting a specific frequency band and channel pairs to enhance the detection result. We found that individuals have a specific frequency band that reflects the most significant synchronization changes, and our method can individually adjust parameters and has good detection performance.

Reducing false discoveries in resting-state functional connectivity using short channel correction: an fNIRS study.

Significance: Functional near-infrared spectroscopy (fNIRS) is a neuroimaging tool that can measure resting-state functional connectivity; however, non-neuronal components present in fNIRS signals introduce false discoveries in connectivity, which can impact interpretation of functional networks. Aim: We investigated the effect of short channel correction on resting-state connectivity by removing non-neuronal signals from fNIRS long channel data. We hypothesized that false discoveries in connectivity can be reduced, hence improving the discriminability of functional networks of known, different connectivity strengths. Approach: A principal component analysis-based short channel correction technique was applied to resting-state data of 10 healthy adult subjects. Connectivity was analyzed using magnitude-squared coherence of channel pairs in connectivity groups of homologous and control brain regions, which are known to differ in connectivity. Results: By removing non-neuronal components using short channel correction, significant reduction of coherence was observed for oxy-hemoglobin concentration changes in frequency bands associated with resting-state connectivity that overlap with the Mayer wave frequencies. The results showed that short channel correction reduced spurious correlations in connectivity measures and improved the discriminability between homologous and control groups. Conclusions: Resting-state functional connectivity analysis with short channel correction performs better than without correction in its ability to distinguish functional networks with distinct connectivity characteristics.

Continuous Decoding of Hand Movement From EEG Signals Using Phase-Based Connectivity Features.

The principal goal of the brain-computer interface (BCI) is to translate brain signals into meaningful commands to control external devices or neuroprostheses to restore lost functions of patients with severe motor disabilities. The invasive recording of brain signals involves numerous health issues. Therefore, BCIs based on non-invasive recording modalities such as electroencephalography (EEG) are safer and more comfortable for the patients. The BCI requires reconstructing continuous movement parameters such as position or velocity for practical application of neuroprostheses. The BCI studies in continuous decoding have extensively relied on extracting features from the amplitude of brain signals, whereas the brain connectivity features have rarely been explored. This study aims to investigate the feasibility of using phase-based connectivity features in decoding continuous hand movements from EEG signals. To this end, the EEG data were collected from seven healthy subjects performing a 2D center-out hand movement task in four orthogonal directions. The phase-locking value (PLV) and magnitude-squared coherence (MSC) are exploited as connectivity features along with multiple linear regression (MLR) for decoding hand positions. A brute-force search approach is employed to find the best channel pairs for extracting features related to hand movements. The results reveal that the regression models based on PLV and MSC features achieve the average Pearson correlations of 0.43 ± 0.03 and 0.42 ± 0.06, respectively, between predicted and actual trajectories over all subjects. The delta and alpha band features have the most contribution in regression analysis. The results also demonstrate that both PLV and MSC decoding models lead to superior results on our data compared to two recently proposed feature extraction methods solely based on the amplitude or phase of recording signals (p < 0.05). This study verifies the ability of PLV and MSC features in the continuous decoding of hand movements with linear regression. Thus, our findings suggest that extracting features based on brain connectivity can improve the accuracy of trajectory decoder BCIs.

Multichannel search strategy for improving the detection of auditory steady-state response.

Auditory steady-state response (ASSR) is useful for hearing threshold estimation. The ASSR is usually detected with objective response detectors (ORD). The performance of these detectors depends on the signal-to-noise ratio (SNR) as well as the signal length. Since it is undesirable to increase the signal length, then, this work provides a multivariate technique for improving the SNR and consequently the detection power. We propose the insertion of a short calibration step before the detection protocol, in order to perform a search among the available electroencephalogram (EEG) derivations and select the derivation with the highest SNR. The ORD used in this work was the magnitude-squared coherence (MSC). The standard detection protocol is to use the same EEG derivation in all exams. Using 22-scalp positions, the new technique achieved a detection rate higher than that obtained in 99.13% of the standard detection protocol. When restrictions were applied to the search, a superior performance was achieved. Thus, the technique proposed was able to track the best EEG derivations before exams and seems to be able to deal with the variability between individuals and between sessions.

Analysis of EEG-EMG Coherence in Low Frequency Bands.

In this, study, an attempt is made to analyze the corticomuscular coupling of the brain and muscular system in the low-frequency components during ramp descent (RD) and stair descent (SD) locomotion. For this purpose, magnitude squared coherence (MSC) is computed from the simultaneous EEG and EMG signals recorded during the ramp and stair descent tasks. The MSC is extracted from the low- frequency bands such as delta (0.1-3 Hz) and theta bands (4-7 Hz). The study utilizes a publicly available database consisting of simultaneous recorded EEG, lower limb EMG and full body motion information from ten healthy subjects. The results show that there exists corticomuscular coupling between motor cortex (C1, C2 and Cz contacts) and tibialis anterior muscle activities during RD and SD. In addition, the MSC differs for both the tasks and frequency bands. In delta band frequencies, the MSC is found to be higher in C2 regions. In the case of theta, the MSC is higher in C1 during RD and in Cz during SD. Therefore, the MSC associated with the low frequency components could be used to detect walking intentions.

EEG Signal Connectivity for Characterizing Interictal Activity in Patients With Mesial Temporal Lobe Epilepsy.

Over the last decade, several methods for analysis of epileptiform signals in electroencephalography (EEG) have been proposed. These methods mainly use EEG signal features in either the time or the frequency domain to separate regular, interictal, and ictal brain activity. The aim of this work was to evaluate the feasibility of using functional connectivity (FC) based feature extraction methods for the analysis of epileptiform discharges in EEG signals. These signals were obtained from EEG-fMRI sessions of 10 patients with mesial temporal lobe epilepsy (MTLE) with unilateral hippocampal atrophy. The connectivity functions investigated were motif synchronization, imaginary coherence, and magnitude squared coherence in the alpha, beta, and gamma bands of the EEG. EEG signals were sectioned into 1-s epochs and classified according to (using neurologist markers): activity far from interictal epileptiform discharges (IED), activity immediately before an IED and, finally, mid-IED activity. Connectivity matrices for each epoch for each FC function were built, and graph theory was used to obtain the following metrics: strength, cluster coefficient, betweenness centrality, eigenvector centrality (both local and global), and global efficiency. The statistical distributions of these metrics were compared among the three classes, using ANOVA, for each FC function. We found significant differences in all global (p < 0.001) and local (p < 0.00002) graph metrics of the far class compared with before and mid for motif synchronization on the beta band; local betweenness centrality also pointed to a degree of lateralization on the frontotemporal structures. This analysis demonstrates the potential of FC measures, computed using motif synchronization, for the characterization of epileptiform activity of MTLE patients. This methodology may be helpful in the analysis of EEG-fMRI data applied to epileptic foci localization. Nonetheless, the methods must be tested with a larger sample and with other epileptic phenotypes.