EEG Phase Research Articles

Study on intuition by EEG phase synchronization relevant to proficiency during factorization

Study on intuition by EEG phase synchronization relevant to proficiency during factorization

GPU-based high-performance computing of multichannel EEG phase wavelet synchronization.

The work is devoted to GPU-based high performance realization of algorithm for wavelet phase synchronization. Wavelet phase coherence was applied for analyzing brain activity in states with different degrees of mental and sensory attention. In the analysis of electroencephalographic correlates of mental states, as a rule the focus is on the analysis of the spectral power of a quasi-stationary EEG or task-related power of time-frequency EEG spectra. The analysis of the wavelet phase coherence provides additional information on the organization of brain activity, but requires more computing time. Fast implementation can simplify the use of this method in practice.

A robust statistical framework for instantaneous electroencephalogram phase and frequency estimation and analysis

Objective: The instantaneous phase (IP) and instantaneous frequency (IF) of the electroencephalogram (EEG) are considered as notable complements for the EEG spectrum. The calculation of these parameters commonly includes narrow-band filtering, followed by the calculation of the signal’s analytical form. The calculation of the IP and IF is highly susceptible to the filter parameters and background noise level, especially in low analytical signal amplitudes. The objective of this study is to propose a robust statistical framework for EEG IP/IF estimation and analysis. Approach: Herein, a Monte Carlo estimation scheme is proposed for the robust estimation of the EEG IP and IF. It is proposed that any EEG phase-related inference should be reported as an average with confidence intervals obtained by repeating the IP and IF estimation under infinitesimal variations (selected by an expert), in algorithmic parameters such as the filter’s bandwidth, center frequency and background noise level. In the second part of the paper, a stochastic model consisting of the superposition of narrow-band foreground and background EEG is used to derive analytically probability density functions of the instantaneous envelope (IE) and IP of EEG signals, which justify the proposed Monte Carlo scheme. Main results: The instantaneous analytical envelope of the EEG, which has been empirically used in previous studies, is shown to have a fundamental impact on the accuracy of the EEG phase contents. It is rigorously shown that the IP/IF estimation quality highly depends on the IE and any phase/frequency interpretations in low IE are statistically unreliable and require a hypothesis test. Significance: The impact of the proposed method on previous studies, including time-domain phase synchrony, phase resetting, phase locking value and phase amplitude coupling are studied with examples. The findings of this research can set forth new standards for EEG phase/frequency estimation and analysis techniques.

Application of a reconstruction technique in detection of dominant SSVEP frequency

Abstract Steady-state visual evoked potentials (SSVEPs) have a number of specific properties such as an oscillating feature when compared to event-related potentials (ERPs). Based on this oscillating property, a short electroencephalogram (EEG) segment containing an SSVEP can be used to reconstruct a series of EEG segments where the SSVEP has the same initial phase but the phase of the background EEG varies randomly. When these reconstructed EEG segments are averaged, the background EEG is weakened, whereas the SSVEP is strengthened. Therefore, the signal-to-noise ratio (SNR) of the SSVEP in the averaged signal is significantly improved. This reconstruction technique is first proposed in this work, and the method of extracting SSVEPs based on this technique is referred to as the reconstruction extraction (RE) method. The RE, power spectrum (PS), and canonical correlation analysis (CCA) methods were applied to EEG segments that were 1s in length in order to detect SSVEPs. The results show that the detection accuracy of the RE method is similar to that of the CCA method, although it is higher than that of the PS method in situations where the SSVEP has low strength. However, in contrast to the CCA, the RE can be applied using only one signal electrode. This suggests that the RE method can be adopted in a real-time SSVEP-based brain–computer interface (BCI) system.

P 24 Electrophysiological connectivity during oscillatory cycles of alpha activity and the BOLD-signal correlate in major depression

Introduction Major depressive disorder (MDD) belongs to the leading causes of disability worldwide. From an electrophysiological point of view, EEG-alpha activity and alpha phase synchronization seem to be possible biomarkers with diagnostic and prognostic value. In parallel, functional magnetic resonance imaging (fMRI) studies suggest altered blood oxygen-level dependent (BOLD) signals in e.g. frontal and prefrontal brain areas ( Lemogne et al., 2012 ). However, the association between electrophysiological alterations and differences of fMRI activity in MDD remains vague. The goal of this study was to analyze the linkage between physiologically synchronized neuronal activity and hemodynamic changes as assessed by simultaneous EEG-fMRI recordings and its alterations in MDD. Method Therefore, simultaneous EEG-fMRI 12 min resting state measurements of 22 unmedicated MDD-patients and 15 healthy controls were conducted. EEG phase synchronization as markers for cortical connectivity between two scalp regions were calculated at the electrode level. Time series of the phase synchronization were averaged according to the repetition time (2400 ms) and used as regressors for the BOLD signal analysis. Results While interhemispheric activity e.g. between electrodes F3 and F4 showed increased BOLD signals in associated areas at both sides in healthy subjects, intrahemispheric connectivity, e.g. between C3 and O1 was associated with heterogeneous patterns with mainly frontal increased and occipitally decreased BOLD signals. Concerning the EEG phase synchronization associated BOLD signals, a significant difference between patients suffering from MDD and healthy subjects was found within the subgenual gyrus for a variety of intra- and interhemispheric EEG-connections. In particular, in patients, the subgenual gyrus seems to be more involved in the regulation of EEG-alpha synchronization in comparison to healthy controls. Conclusion EEG-alpha phase synchronization is associated with marked BOLD signal changes. The regulation of EEG-alpha phase synchronization seems to be altered in MDD with the subgenual gyrus as a key hub.

At what latency does the phase of brain oscillations influence perception?

Recent evidence has shown a rhythmic modulation of perception: prestimulus ongoing electroencephalography (EEG) phase in the θ (4-8 Hz) and α (8-13 Hz) bands has been directly linked with fluctuations in target detection. In fact, the ongoing EEG phase directly reflects cortical excitability: it acts as a gating mechanism for information flow at the neuronal level. Consequently, the key phase modulating perception should be the one present in the brain when the stimulus is actually being processed. Most previous studies, however, reported phase modulation peaking 100 ms or more before target onset. To explain this discrepancy, we first use simulations showing that contamination of spontaneous oscillatory signals by target-evoked ERP and signal filtering (e.g., wavelet) can result in an apparent shift of the peak phase modulation towards earlier latencies, potentially reaching the prestimulus period. We then present a paradigm based on linear systems analysis which can uncover the true latency at which ongoing EEG phase influences perception. After measuring the impulse response function, we use it to reconstruct (rather than record) the brain activity of human observers during white noise sequences. We can then present targets in those sequences, and reliably estimate EEG phase around these targets without any influence of the target-evoked response. We find that in these reconstructed signals, the important phase for perception is that of fronto-occipital ∼6 Hz background oscillations at about 75 ms after target onset. These results confirm the causal influence of phase on perception at the time the stimulus is effectively processed in the brain.

Ongoing slow oscillatory phase modulates speech intelligibility in cooperation with motor cortical activity.

Neural oscillation is attracting attention as an underlying mechanism for speech recognition. Speech intelligibility is enhanced by the synchronization of speech rhythms and slow neural oscillation, which is typically observed as human scalp electroencephalography (EEG). In addition to the effect of neural oscillation, it has been proposed that speech recognition is enhanced by the identification of a speaker’s motor signals, which are used for speech production. To verify the relationship between the effect of neural oscillation and motor cortical activity, we measured scalp EEG, and simultaneous EEG and functional magnetic resonance imaging (fMRI) during a speech recognition task in which participants were required to recognize spoken words embedded in noise sound. We proposed an index to quantitatively evaluate the EEG phase effect on behavioral performance. The results showed that the delta and theta EEG phase before speech inputs modulated the participant’s response time when conducting speech recognition tasks. The simultaneous EEG-fMRI experiment showed that slow EEG activity was correlated with motor cortical activity. These results suggested that the effect of the slow oscillatory phase was associated with the activity of the motor cortex during speech recognition.

The Triple-Flash Illusion Reveals a Driving Role of Alpha-Band Reverberations in Visual Perception.

The modulatory role of spontaneous brain oscillations on perception of threshold-level stimuli is well established. Here, we provide evidence that alpha-band (∼10 Hz) oscillations not only modulate perception of threshold-level sensory inputs but also can drive perception and generate percepts without a physical stimulus being present. We used the "triple-flash" illusion: Occasional perception of three flashes when only two spatially coincident veridical ones, separated by ∼100 ms, are presented. The illusion was proposed to result from superposition of two hypothetical oscillatory impulse response functions generated in response to each flash: When the delay between flashes matches the period of the oscillation, the superposition enhances a later part of the oscillation that is normally damped; when this enhancement crosses perceptual threshold, a third flash is erroneously perceived (Bowen, 1989). In Experiment 1, we varied stimulus onset asynchrony and validated Bowen's theory: The optimal stimulus onset asynchrony for illusion to occur was correlated, across human subjects (both genders), with the subject-specific impulse response function period determined from a separate EEG experiment. Experiment 2 revealed that prestimulus parietal, but no occipital, alpha EEG phase and power, as well as poststimulus alpha phase-locking, together determine the occurrence of the illusion on a trial-by-trial basis. Thus, oscillatory reverberations create something out of nothing: A third flash where there are only two.SIGNIFICANCE STATEMENT We highlight a novel property of alpha-band (∼10 Hz) oscillations based on three experiments (two EEG and one psychophysics) by demonstrating that alpha-band oscillations do not merely modulate perception, but can also drive perception. We show that human participants report seeing a third flash when only two are presented (the "triple-flash" illusion) most often when the interflash delay matches the period of participant's oscillatory impulse response function reverberating in alpha. Within-subject, the phase and power of ongoing parietal, but not occipital, alpha-band oscillations at the time of the first flash determine illusory percept on a trial-by-trial basis. We revealed a physiologically plausible mechanism that validates and extends the original theoretical account of the triple-flash illusion proposed by Bowen in 1989.

At What Latency Does the Phase of Brain Oscillations Influence Perception?

Recent evidence has shown a rhythmic modulation of perception: prestimulus ongoing electroencephalography (EEG) phase in the θ (4–8 Hz) and α (8–13 Hz) bands has been directly linked with fluctuations in target detection. In fact, the ongoing EEG phase directly reflects cortical excitability: it acts as a gating mechanism for information flow at the neuronal level. Consequently, the key phase modulating perception should be the one present in the brain when the stimulus is actually being processed. Most previous studies, however, reported phase modulation peaking 100 ms or more before target onset. To explain this discrepancy, we first use simulations showing that contamination of spontaneous oscillatory signals by target-evoked ERP and signal filtering (e.g., wavelet) can result in an apparent shift of the peak phase modulation towards earlier latencies, potentially reaching the prestimulus period. We then present a paradigm based on linear systems analysis which can uncover the true latency at which ongoing EEG phase influences perception. After measuring the impulse response function, we use it to reconstruct (rather than record) the brain activity of human observers during white noise sequences. We can then present targets in those sequences, and reliably estimate EEG phase around these targets without any influence of the target-evoked response. We find that in these reconstructed signals, the important phase for perception is that of fronto-occipital ∼6 Hz background oscillations at about 75 ms after target onset. These results confirm the causal influence of phase on perception at the time the stimulus is effectively processed in the brain.

Large-Scale Phase Synchrony Reflects Clinical Status After Stroke: An EEG Study

Background and Purpose. Stroke-induced focal brain lesions often exert remote effects via residual neural network activity. Electroencephalographic (EEG) techniques can assess neural network modifications after brain damage. Recently, EEG phase synchrony analyses have shown associations between the level of large-scale phase synchrony of brain activity and clinical symptoms; however, few reports have assessed such associations in stroke patients. Objective. The aim of this study was to investigate the clinical relevance of hemispheric phase synchrony in stroke patients by calculating its correlation with clinical status. Methods. This cross-sectional study included 19 patients with post-acute ischemic stroke admitted for inpatient rehabilitation. Interhemispheric phase synchrony indices (IH-PSIs) were computed in 2 frequency bands (alpha [α], and beta [β]), and associations between indices and scores of the Functional Independence Measure (FIM), the National Institutes of Health Stroke Scale (NIHSS), and the Fugl−Meyer Motor Assessment (FMA) were analyzed. For further assessments of IH-PSIs, ipsilesional intrahemispheric PSIs (IntraH-PSIs) as well as IH- and IntraH-phase lag indices (PLIs) were also evaluated. Results. IH-PSIs correlated significantly with FIM scores and NIHSS scores. In contrast, IH-PSIs did not correlate with FMA scores. IntraH-PSIs correlate with FIM scores after removal of the outlier. The results of analysis with PLIs were consistent with IH-PSIs. Conclusions. The PSIs correlated with performance on the activities of daily living scale but not with scores on a pure motor impairment scale. These results suggest that large-scale phase synchrony represented by IH-PSIs provides a novel surrogate marker for clinical status after stroke.

Advantages of EEG phase patterns for the detection of gait intention in healthy and stroke subjects

Objective. One use of EEG-based brain–computer interfaces (BCIs) in rehabilitation is the detection of movement intention. In this paper we investigate for the first time the instantaneous phase of movement related cortical potential (MRCP) and its application to the detection of gait intention. Approach. We demonstrate the utility of MRCP phase in two independent datasets, in which 10 healthy subjects and 9 chronic stroke patients executed a self-initiated gait task in three sessions. Phase features were compared to more conventional amplitude and power features. Main results. The neurophysiology analysis showed that phase features have higher signal-to-noise ratio than the other features. Also, BCI detectors of gait intention based on phase, amplitude, and their combination were evaluated under three conditions: session-specific calibration, intersession transfer, and intersubject transfer. Results show that the phase based detector is the most accurate for session-specific calibration (movement intention was correctly detected in 66.5% of trials in healthy subjects, and in 63.3% in stroke patients). However, in intersession and intersubject transfer, the detector that combines amplitude and phase features is the most accurate one and the only that retains its accuracy (62.5% in healthy subjects and 59% in stroke patients) w.r.t. session-specific calibration. Significance. MRCP phase features improve the detection of gait intention and could be used in practice to remove time-consuming BCI recalibration.

Heightened Background Cortical Synchrony in Patients With Epilepsy: EEG Phase Synchrony Analysis During Awake and Sleep Stages Using Novel Ensemble Measure.

Excessive cortical synchrony within neural ensembles has been implicated as an important mechanism driving epileptiform activity. The current study measures and compares background electroencephalographic (EEG) phase synchronization in patients having various types of epilepsies and healthy controls during awake and sleep stages. A total of 120 patients with epilepsy (PWE) subdivided into 3 groups (juvenile myoclonic epilepsy [JME], temporal lobe epilepsy [TLE], and extra-temporal lobe epilepsy [Ex-TLE]; n = 40 in each group) and 40 healthy controls were subjected to overnight polysomnography. EEG phase synchronization (SI) between the 8 EEG channels was assessed for delta, theta, alpha, sigma, and high beta frequency bands using ensemble measure on 10-second representative time windows and compared between patients and controls and also between awake and sleep stages. Mean ± SD of SI was compared using 2-way analysis of variance followed by pairwise comparison ( P ≤ .05). In both delta and theta bands, the SI was significantly higher in patients with JME, TLE, and Ex-TLE compared with controls, whereas in alpha, sigma, and high beta bands, SI was comparable between the groups. On comparison of SI between sleep stages, delta band: progressive increase in SI from wake ⇒ N1 ⇒ N2 ⇒ N3, whereas REM (rapid eye movement) was comparable to wake; theta band: decreased SI during N2 and increase during N3; alpha band: SI was highest in wake and lower in N1, N2, N3, and REM; and sigma and high beta bands: progressive increase in SI from wake ⇒ N1 ⇒ N2 ⇒ N3; however, sigma band showed lower SI during REM. This study found an increased background cortical synchronization in PWE compared with healthy controls in delta and theta bands during wake and sleep. This background hypersynchrony may be an important property of epileptogenic brain circuitry in PWE, which enables them to effortlessly generate a paroxysmal EEG depolarization shift.

Theta-alpha EEG phase distributions in the frontal area for dissociation of visual and auditory working memory

Working memory (WM) is known to be associated with synchronization of the theta and alpha bands observed in electroencephalograms (EEGs). Although frontal-posterior global theta synchronization appears in modality-specific WM, local theta synchronization in frontal regions has been found in modality-independent WM. How frontal theta oscillations separately synchronize with task-relevant sensory brain areas remains an open question. Here, we focused on theta-alpha phase relationships in frontal areas using EEG, and then verified their functional roles with mathematical models. EEG data showed that the relationship between theta (6 Hz) and alpha (12 Hz) phases in the frontal areas was about 1:2 during both auditory and visual WM, and that the phase distributions between auditory and visual WM were different. Next, we used the differences in phase distributions to construct FitzHugh-Nagumo type mathematical models. The results replicated the modality-specific branching by orthogonally of the trigonometric functions for theta and alpha oscillations. Furthermore, mathematical and experimental results were consistent with regards to the phase relationships and amplitudes observed in frontal and sensory areas. These results indicate the important role that different phase distributions of theta and alpha oscillations have in modality-specific dissociation in the brain.

Robust electroencephalogram phase estimation with applications in brain-computer interface systems

Objective: In this study, a robust method is developed for frequency-specific electroencephalogram (EEG) phase extraction using the analytic representation of the EEG. Based on recent theoretical findings in this area, it is shown that some of the phase variations—previously associated to the brain response—are systematic side-effects of the methods used for EEG phase calculation, especially during low analytical amplitude segments of the EEG.Approach: With this insight, the proposed method generates randomized ensembles of the EEG phase using minor perturbations in the zero-pole loci of narrow-band filters, followed by phase estimation using the signal’s analytical form and ensemble averaging over the randomized ensembles to obtain a robust EEG phase and frequency. This Monte Carlo estimation method is shown to be very robust to noise and minor changes of the filter parameters and reduces the effect of fake EEG phase jumps, which do not have a cerebral origin.Main results: As proof of concept, the proposed method is used for extracting EEG phase features for a brain computer interface (BCI) application. The results show significant improvement in classification rates using rather simple phase-related features and a standard K-nearest neighbors and random forest classifiers, over a standard BCI dataset.Significance: The average performance was improved between 4–7% (in absence of additive noise) and 8–12% (in presence of additive noise). The significance of these improvements was statistically confirmed by a paired sample t-test, with 0.01 and 0.03 p-values, respectively. The proposed method for EEG phase calculation is very generic and may be applied to other EEG phase-based studies.

An Investigation on the Utility and Reliability of Electroencephalogram Phase Signal Upon Interpreting Cognitive Responses in the Brain: A Critical Discussion

Within the neuroscience and computational neuroscience communities, applications such as evaluating different cognitive responses of the brain, brain-computer interface (BCI) systems and brain connectivity studies have increasingly been using EEG phase information during the past few decades. The utility of EEG phase can be directly linked to the neural propagation and synchronized firing of neuronal populations during different cognitive states of the brain. Nevertheless, it has previously been shown that phase of narrow-band (frequency specific) foreground EEG (desired) is prone to contain fake spikes and variations (unrelated to brain activity) in the presence of background spontaneous EEG and low SNRs of foreground EEG (the low-amplitude analytic signals or LAAS problem). Accordingly, extracting the instantaneous EEG phase sequence for further utilization upon interpreting the cognitive states of the brain using phase related quantities, such as instantaneous frequency, phase shift, phase resetting and phase locking value, is a very sensitive and rigorous process. In this study, a simple procedure is proposed to illustrate the effects of LAAS problem on the utility of EEG phase related quantities in aforementioned applications, also to investigate the reliability of interpretations of the brain’s cognitive states based on such quantities. Results show that, without a proper and effective solution strategy, such potential flaws lead to incorrect physiological and pathological interpretations.

Genetic influences on functional connectivity associated with feedback processing and prediction error: Phase coupling of theta-band oscillations in twins

Detection and evaluation of the mismatch between the intended and actually obtained result of an action (reward prediction error) is an integral component of adaptive self-regulation of behavior. Extensive human and animal research has shown that evaluation of action outcome is supported by a distributed network of brain regions in which the anterior cingulate cortex (ACC) plays a central role, and the integration of distant brain regions into a unified feedback-processing network is enabled by long-range phase synchronization of cortical oscillations in the theta band. Neural correlates of feedback processing are associated with individual differences in normal and abnormal behavior, however, little is known about the role of genetic factors in the cerebral mechanisms of feedback processing. Here we examined genetic influences on functional cortical connectivity related to prediction error in young adult twins (age 18, n=399) using event-related EEG phase coherence analysis in a monetary gambling task. To identify prediction error-specific connectivity pattern, we compared responses to loss and gain feedback. Monetary loss produced a significant increase of theta-band synchronization between the frontal midline region and widespread areas of the scalp, particularly parietal areas, whereas gain resulted in increased synchrony primarily within the posterior regions. Genetic analyses showed significant heritability of frontoparietal theta phase synchronization (24 to 46%), suggesting that individual differences in large-scale network dynamics are under substantial genetic control. We conclude that theta-band synchronization of brain oscillations related to negative feedback reflects genetically transmitted differences in the neural mechanisms of feedback processing. To our knowledge, this is the first evidence for genetic influences on task-related functional brain connectivity assessed using direct real-time measures of neuronal synchronization.

The effects of EEG phase at stimulus onset

The effects of EEG phase at stimulus onset

Identifying stereotypic evolving micro-scale seizures (SEMS) in the hypoxic-ischemic EEG of the pre-term fetal sheep with a wavelet type-II fuzzy classifier.

Perinatal hypoxic-ischemic encephalopathy (HIE) around the time of birth due to lack of oxygen can lead to debilitating neurological conditions such as epilepsy and cerebral palsy. Experimental data have shown that brain injury evolves over time, but during the first 6-8 hours after HIE the brain has recovered oxidative metabolism in a latent phase, and brain injury is reversible. Treatments such as therapeutic cerebral hypothermia (brain cooling) are effective when started during the latent phase, and continued for several days. Effectiveness of hypothermia is lost if started after the latent phase. Post occlusion monitoring of particular micro-scale transients in the hypoxic-ischemic (HI) Electroencephalogram (EEG), from an asphyxiated fetal sheep model in utero, could provide precursory evidence to identify potential biomarkers of injury when brain damage is still treatable. In our studies, we have reported how it is possible to automatically detect HI EEG transients in the form of spikes and sharp waves during the latent phase of the HI EEG of the preterm fetal sheep. This paper describes how to identify stereotypic evolving micro-scale seizures (SEMS) which have a relatively abrupt onset and termination in a frequency range of 1.8-3Hz (Delta waves) superimposed on a suppressed EEG amplitude background post occlusion. This research demonstrates how a Wavelet Type-II Fuzzy Logic System (WT-Type-II-FLS) can be used to automatically identify subtle abnormal SEMS that occur during the latent phase with a preliminary average validation overall performance of 78.71%±6.63 over the 390 minutes of the latent phase, post insult, using in utero pre-term hypoxic fetal sheep models.

Identification of emotion associated brain functional network with phase locking value.

Recognition of discriminative brain functional network pattern and regions corresponding to emotions are important in understanding the neuron functional network underlying the human emotion process. Emotion models mapping onto brain is possible with the help of emotion-specific network patterns and its corresponding brain regions. This paper presents a method to identify emotion related functional connectivity pattern and their distinctive associated regions using EEG phase synchrony (phase locking value (PLV)) connectivity analysis. The emotion-specific channel pairs, reactive band, and synchrony related locations are identified based on the network dissimilarities between emotion and rest tasks. With the most reactive pairs identified, the emotion-specific functional network is formed. The proposed method is validated on `database for emotion analysis using physiological signals (DEAP)' that confirms the distinct nature of identified functional connectivity pattern and the regions corresponding to the emotion.

Theta phase coherence in affective picture processing reveals dysfunctional sensory integration in psychopathic offenders

Psychopathic offenders are described as emotionally cold, displaying deficits in affective responding. However, research demonstrates that many of the psychopathy-related deficits are moderated by attention, such that under conditions of high attentional and perceptual load psychopathic offenders display deficits in affective responses, but do not in conditions of low load. To date, most studies use measures of defensive reflex (i.e., startle) and conditioning manipulations to examine the impact of load on psychopathy-related processing, but have not examined more direct measures of attention processing. In a sample of adult male offenders, the present study examined time-frequency EEG phase coherence in response to a picture-viewing paradigm that manipulated picture familiarity to assess neural changes in processing based on perceptual demands. Results indicated psychopathy-related differences in the theta response, an index of readiness to perceive and integrate sensory information. These data provide further evidence that psychopathic offenders have disrupted integration of sensory information.