Quantification of transient quadratic phase couplings within EEG burst patterns in sedated patients during electroencephalic burst-suppression period

The time dynamics of the quadratic phase coupling within burst patterns during electroencephalic burst-suppression has been quantified. It can be shown that a transient quadratic phase coupling (QPC) exists between the frequency ranges 0 to 2.5 and 3 to 7.5 Hz and between the frequency ranges 0 to 2.5 and 8 to 12 Hz. The QPC can be explained by an amplitude modulation, where the slow rhythm modulates the rhythmic activities with a higher frequency. By means of time-variant bicoherence analysis, a strong phase-locking between the modulating and the modulated component can be identified. The phase-locking is demonstrable within the first 250 ms after the burst onset and comes up to the maximum between 750 and 1250 ms. The effect is maintained over the whole first part of the burst (2 s) with a decreasing tendency after 1250 ms. All these effects cannot be found in the EEG before entering the burst suppression period (BSP). The transient coupling phenomena in the EEG bursts during BSP can be regarded as indicators for short-term interrelations between the underlying electrophysiologic processes.     

Technique for the quantification of transient quadratic phase couplings between heart rate components.

A technique for the time-variant analysis of quadratic phase coupling (QPC) in heart rate data is introduced and tested in 6 human neonates during quiet sleep. The set up of the approach is based up on the assumption that QPCs in the heart rate variability (HRV) are related to amplitude modulation effects. The application of the biamplitude deals with the detection of the coupling pattern and the bicoherence is used for the statistical quantification of coupling. By means of the results of bispectral analysis the time-variant processing has been adapted. The frequency-selective complex demodulation of the HRV leads to the envelope of the respiratory sinus arrhythmia (RSA), this has been used as one input for a time-variant coherence analysis. The other input is the low-pass filtered 10-second-rhythm of the HRV. A time-continuous quantification of the QPC, caused by amplitude modulation (10-second-rhythm modulates the RSA), is possible using this approach. According to our observed results in neonatal HRV both a phase co-ordination between the 10-second-rhythm and RSA as well as a non-linear coupling (amplitude modulation) between these HRV components can be seen.     

Bispectral analysis does not differentiate between anaesthesia EEG and a linear random process]

Bispectral analysis of the electroencephalogram (EEG) has been used to monitor depth of anaesthesia. In the majority of publications this has involved the use of the so called BIS-Index TM (Aspect Medical Systems, Inc.). The exact relationship of this index to such bispectral parameters as the bispectrum and bicoherence has not yet been reported. If the EEG is considered as a linear random process, bicoherence is trivial, i.e. it is independent of the EEG frequency. The aim of this study was to determine the proportion of EEG epochs with non-trivial bicoherence during isoflurane/N20 anaesthesia. We reanalyzed 25.5 hours of digitalised EEG signal from 9 patients undergoing gynaecological surgery. The test developed by Hinich for Gaussian distribution and linearity was then applied. The test was validated using various synthetic surrogate data: Gaussian random data, the z-component of the Lorenz attractor, the phase randomized EEG and the phase randomized z-component of the Lorenz attractor. The percentage of epochs (8.192 s, 1024 data points) with non-trivial bicoherence was: Lorenz data 95.4%, phase randomized Lorenz data 9.4%, synthetic Gaussian data 14.8%, original EEG 9.1%, phase randomized EEG 5.1%. The original EEG data were not found to contain a higher percentage of epochs with non-trivial bicoherence than the phase randomized data and the synthetic Gaussian data. We conclude that bispectral analysis does not substantially add to the information obtained with other methods of quantitative EEG analysis.     

Peak and averaged bicoherence for different EEG patterns during general anaesthesia

Background  

Changes in nonlinear neuronal mechanisms of EEG generation in the course of general anaesthesia have been extensively investigated in research literature. A number of EEG signal properties capable of tracking these changes have been reported and employed in anaesthetic depth monitors. The degree of phase coupling between different spectral components is a marker of nonlinear EEG generators and is claimed to be an important aspect of BIS. While bicoherence is the most direct measure of phase coupling, according to published research it is not directly used in the calculation of BIS, and only limited studies of its association with anaesthetic depth and level of consciousness have been published. This paper investigates bicoherence parameters across equal band and unequal band bifrequency regions, during different states of anaesthetic depth relating to routine clinical anaesthesia, as determined by visual inspection of EEG.     

A novel method for early diagnosis of Alzheimer’s disease based on higher-order spectral estimation of spontaneous speech signals

One main challenge for medical investigators is the early diagnosis of Alzheimer’s disease (AD) because it provides greater opportunities for patients to be eligible for more clinical trials. In this study, higher order spectra of human speech signals during AD were explored to analyze and compare the quadratic phase coupling of spontaneous speech signals for healthy and AD subjects using bispectrum and bicoherence. The results showed that the quadratic phase couplings of spontaneous speech signal of persons with Alzheimer’s were reduced compared to healthy subject. However, the speech phase coupled harmonics shifted to the higher frequencies in Alzheimer’s than healthy subjects. In addition, it was shown not only are there significant differences between Alzheimer’s and control subjects in parameters estimated, but also the speech patterns appeared to have fluctuated in both types of spontaneous speech.     

Quantitative electroencephalographic changes due to middle cerebral artery occlusion by endothelin 1 in conscious rats.

The powerful vasoconstrictor peptide endothelin-1 (ET1) has been shown to reduce local cerebral blood flow in brain areas supplied by the middle cerebral artery (MCA) to a pathologically low level upon intracerebral injection adjacent to the MCA. This reduction manifests itself as an ischemic infarct, that is fully developed within 3 days after ET1 injection. The aim of the present study is to examine the effect of ET1 on electroencephalographic (EEG) activity. ET1 was microinjected unilaterally at a dose of 60 pmol in 3 microl of saline to the MCA in conscious rats. EEG signals were recorded from the frontoparietal cortical area, supplied by MCA, from the first up to the fourteenth day after ET1 injection. EEG activity was analyzed by the fast Fourier transformation. A significant shift to a lower EEG frequency, i.e., augmentation of slow waves and a reduction of alpha-like and faster EEG waves was found post-ET1. This effect was maximal after 3-7 days when the most severe destruction of neurons in this cortical area occurs, as has been previously demonstrated. The results suggest that the quantitative EEG analysis may provide useful additional information about the functional disturbances associated with focal cerebral ischemia.     

Analysis and automatic identification of sleep stages using higher order spectra

Electroencephalogram (EEG) signals are widely used to study the activity of the brain, such as to determine sleep stages. These EEG signals are nonlinear and non-stationary in nature. It is difficult to perform sleep staging by visual interpretation and linear techniques. Thus, we use a nonlinear technique, higher order spectra (HOS), to extract hidden information in the sleep EEG signal. In this study, unique bispectrum and bicoherence plots for various sleep stages were proposed. These can be used as visual aid for various diagnostics application. A number of HOS based features were extracted from these plots during the various sleep stages (Wakefulness, Rapid Eye Movement (REM), Stage 1-4 Non-REM) and they were found to be statistically significant with p-value lower than 0.001 using ANOVA test. These features were fed to a Gaussian mixture model (GMM) classifier for automatic identification. Our results indicate that the proposed system is able to identify sleep stages with an accuracy of 88.7%.     

Combinatorial relations in the EEG spectrum]

The geometric mean frequency of any EEG wave divides its frequency band B into sub-bands b and s. For the wave beta the values s, b and B are the subsequent elements of the geometric progression with denominator equals to the invariant of the gold section. A hypothesis was proposed that all the EEG waves were described by the system SG of the recurrence equations. This system was derived by the generalization of the Fibonacci generating function. Theoretical invariants Ilambda of the system and experimental ratios b/s were found to coincide with the quadratic mean error equals to 1%. The system SG predicts the existence of the EEG waves, rho and sigma (55-118, 118-225 cycles per sec.), which have not yet been discovered experimentally.     

LEDPatNet19: Automated Emotion Recognition Model based on Nonlinear LED Pattern Feature Extraction Function using EEG Signals

Electroencephalography (EEG) signals collected from human brains have generally been used to diagnose diseases. Moreover, EEG signals can be used in several areas such as emotion recognition, driving fatigue detection. This work presents a new emotion recognition model by using EEG signals. The primary aim of this model is to present a highly accurate emotion recognition framework by using both a hand-crafted feature generation and a deep classifier. The presented framework uses a multilevel fused feature generation network. This network has three primary phases, which are tunable Q-factor wavelet transform (TQWT), statistical feature generation, and nonlinear textural feature generation phases. TQWT is applied to the EEG data for decomposing signals into different sub-bands and create a multilevel feature generation network. In the nonlinear feature generation, an S-box of the LED block cipher is utilized to create a pattern, which is named as Led-Pattern. Moreover, statistical feature extraction is processed using the widely used statistical moments. The proposed LED pattern and statistical feature extraction functions are applied to 18 TQWT sub-bands and an original EEG signal. Therefore, the proposed hand-crafted learning model is named LEDPatNet19. To select the most informative features, ReliefF and iterative Chi2 (RFIChi2) feature selector is deployed. The proposed model has been developed on the two EEG emotion datasets, which are GAMEEMO and DREAMER datasets. Our proposed hand-crafted learning network achieved 94.58%, 92.86%, and 94.44% classification accuracies for arousal, dominance, and valance cases of the DREAMER dataset. Furthermore, the best classification accuracy of the proposed model for the GAMEEMO dataset is equal to 99.29%. These results clearly illustrate the success of the proposed LEDPatNet19.     

Deterministic chaos and the first positive Lyapunov exponent: a nonlinear analysis of the human electroencephalogram during sleep

Under selected conditions, nonlinear dynamical systems, which can be described by deterministic models, are able to generate so-called deterministic chaos. In this case the dynamics show a sensitive dependence on initial conditions, which means that different states of a system, being arbitrarily close initially, will become macroscopically separated for sufficiently long times. In this sense, the unpredictability of the EEG might be a basic phenomenon of its chaotic character. Recent investigations of the dimensionality of EEG attractors in phase space have led to the assumption that the EEG can be regarded as a deterministic process which should not be mistaken for simple noise. The calculation of dimensionality estimates the degrees of freedom of a signal. Nevertheless, it is difficult to decide from this kind of analysis whether a process is quasiperiodic or chaotic. Therefore, we performed a new analysis by calculating the first positive Lyapunov exponent L ₁ from sleep EEG data. Lyapunov exponents measure the mean exponential expansion or contraction of a flow in phase space. L ₁ is zero for periodic as well as quasiperiodic processes, but positive in the case of chaotic processes expressing the sensitive dependence on initial conditions. We calculated L ₁ for sleep EEG segments of 15 healthy men corresponding to the sleep stages I, II, III, IV, and REM (according to Rechtschaffen and Kales). Our investigations support the assumption that EEG signals are neither quasiperiodic waves nor a simple noise. Moreover, we found statistically significant differences between the values of L ₁ for different sleep stages. All together, this kind of analysis yields a useful extension of the characterization of EEG signals in terms of nonlinear dynamical system theory.     

Two-stage biometric authentication method using thought activity brain waves

Brain waves are proposed as a biometric for verification of the identities of individuals in a small group. The approach is based on a novel two-stage biometric authentication method that minimizes both false accept error (FAE) and false reject error (FRE). These brain waves (or electroencephalogram (EEG) signals) are recorded while the user performs either one or several thought activities. As different individuals have different thought processes, this idea would be appropriate for individual authentication. In this study, autoregressive coefficients, channel spectral powers, inter-hemispheric channel spectral power differences, inter-hemispheric channel linear complexity and non-linear complexity (approximate entropy) values were used as EEG features by the two-stage authentication method with a modified four fold cross validation procedure. The results indicated that perfect accuracy was obtained, i.e. the FRE and FAE were both zero when the proposed method was tested on five subjects using certain thought activities. This initial study has shown that the combination of the two-stage authentication method with EEG features from thought activities has good potential as a biometric as it is highly resistant to fraud. However, this is only a pilot type of study and further extensive research with more subjects would be necessary to establish the suitability of the proposed method for biometric applications.     

Phase relationships of cerebral cortex potentials in the rabbit during the execution of motor responses

EEG waves phase relations in the sensorimotor and visual cortical areas were studied in 12 rabbits before and during a motor reaction in response to light stimulation. Phase relations in the background activity were characterized by a considerable dispersion (from 26 to 45 degrees). Light stimulation increased the quantity of synphasic EEG oscillations in adjacent cortical points and stabilized the phase shift between EEG waves in the sensorimotor and visual cortical areas. Motor reactions of rabbits to light occurred when theta-rhythm with the most constant phase shift was observed in the EEG of these areas.     

A genetic study of the human low-voltage electroencephalogram

Summary The studied phenotype, the low-voltage electroencephalogram (LVEEG), is characterized by the absence of an alpha rhythm from the resting EEG. In previous studies, evidence was found for a simple autosomal-dominant mode of inheritance of the LVEEG. Such a polymorphism in brain function can be used as a research model for the stepwise elucidation of the molecular mechanism involved in those aspects of neuronal activity that are reflected in the EEG. Linkage with the variable number of tandem repeats (VNTR) marker CMM6 (D20S19) and localization of an LVEEG (EEGV1) gene on 20q have previously been reported, and genetic heterogeneity has been demonstrated. This latter result has been corroborated by studing new marker (MS214). The phenotype of the LVEEG is described here in greater detail. Its main characteristic is the absence of rhythmic alpha activity, especially in occipital leads, whereas other wave forms such as beta or theta waves may be present. Analysis of 17 new families (some of them large), together with 60 previously described nuclear families, supports the genetic hypothesis of an autosomal-dominant mode of inheritance. Problems connected with the analysis of linkage heterogeneity, exclusion mapping, and the study of multipoint linkage are discussed. A possible explanation of the localization of LVEEG in the close vicinity of another gene influencing synchronization of the normal EEG, the gene for benign neonatal epilepsie, is given.     

Quantification of phase coupling and information transfer between electroencephalographic (EEG) signals: Analysis strategies,models and simulations

Different types of phase coupling between and within EEG signals are theoretically explained and coupling-related analysis strategies are reported. Effects of synchronization have been distinguished from effects signal transfer (propagation), where both are designated by a phase coupling. Six examples of phase coupling analysis are presented. The EEG data are derived from our previous investigations and analysis results are complemented by modelling and simulation studies.     

A Physiology-Based Seizure Detection System for Multichannel EEG

Background

Epilepsy is a common chronic neurological disorder characterized by recurrent unprovoked seizures. Electroencephalogram (EEG) signals play a critical role in the diagnosis of epilepsy. Multichannel EEGs contain more information than do single-channel EEGs. Automatic detection algorithms for spikes or seizures have traditionally been implemented on single-channel EEG, and algorithms for multichannel EEG are unavailable.

Methodology

This study proposes a physiology-based detection system for epileptic seizures that uses multichannel EEG signals. The proposed technique was tested on two EEG data sets acquired from 18 patients. Both unipolar and bipolar EEG signals were analyzed. We employed sample entropy (SampEn), statistical values, and concepts used in clinical neurophysiology (e.g., phase reversals and potential fields of a bipolar EEG) to extract the features. We further tested the performance of a genetic algorithm cascaded with a support vector machine and post-classification spike matching.

Principal Findings

We obtained 86.69% spike detection and 99.77% seizure detection for Data Set I. The detection system was further validated using the model trained by Data Set I on Data Set II. The system again showed high performance, with 91.18% detection of spikes and 99.22% seizure detection.

Conclusion

We report a de novo EEG classification system for seizure and spike detection on multichannel EEG that includes physiology-based knowledge to enhance the performance of this type of system.     

LASSO based stimulus frequency recognition model for SSVEP BCIs

Steady-state visual evoked potential (SSVEP) has been increasingly used for the study of brain–computer interface (BCI). How to recognize SSVEP with shorter time and lower error rate is one of the key points to develop a more efficient SSVEP-based BCI. To achieve this goal, we make use of the sparsity constraint of the least absolute shrinkage and selection operator (LASSO) for the extraction of more discriminative features of SSVEP, and then we propose a LASSO model using the linear regression between electroencephalogram (EEG) recordings and the standard square-wave signals of different frequencies to recognize SSVEP without the training stage. In this study, we verified the proposed LASSO model offline with the EEG data of nine healthy subjects in contrast to canonical correlation analysis (CCA). In the experiment, when a shorter time window was used, we found that the LASSO model yielded better performance in extracting robust and detectable features of SSVEP, and the information transfer rate obtained by the LASSO model was significantly higher than that of the CCA. Our proposed method can assist to reduce the recording time without sacrificing the classification accuracy and is promising for a high-speed SSVEP-based BCI.     

A field-theoretic approach to understanding scale-free neocortical dynamics

A mesoscopic field-theoretic approach is compared with neural network and brain imaging approaches to understanding brain dynamics. Analysis of high spatiotemporal resolution rabbit electroencephalogram (EEG) reveals neural fields in the form of spatial patterns in amplitude (AM) and phase (PM) modulation of gamma and beta carrier waves that serve to classify EEGs from trials with differing conditioned stimuli (CS+/−). Paleocortex exemplified by olfactory EEG has one AM–PM pattern at a time that forms by an input-dependent phase transition. Neocortex shows multiple overlapping AM–PM patterns before and during presentation of CSs. Modeling suggests that neocortex is stabilized in a scale-free state of self-organized criticality, enabling cooperative domains to form virtually instantaneously by phase transitions ranging in size from a few hypercolumns to an entire hemisphere. Self-organized local domains precede formation of global domains that supervene and contribute global modulations to local domains. This mechanism is proposed to explain Gestalt formation in perception.     

Adaptive Filtering Methods for Identifying Cross-Frequency Couplings in Human EEG

Oscillations have been increasingly recognized as a core property of neural responses that contribute to spontaneous, induced, and evoked activities within and between individual neurons and neural ensembles. They are considered as a prominent mechanism for information processing within and communication between brain areas. More recently, it has been proposed that interactions between periodic components at different frequencies, known as cross-frequency couplings, may support the integration of neuronal oscillations at different temporal and spatial scales. The present study details methods based on an adaptive frequency tracking approach that improve the quantification and statistical analysis of oscillatory components and cross-frequency couplings. This approach allows for time-varying instantaneous frequency, which is particularly important when measuring phase interactions between components. We compared this adaptive approach to traditional band-pass filters in their measurement of phase-amplitude and phase-phase cross-frequency couplings. Evaluations were performed with synthetic signals and EEG data recorded from healthy humans performing an illusory contour discrimination task. First, the synthetic signals in conjunction with Monte Carlo simulations highlighted two desirable features of the proposed algorithm vs. classical filter-bank approaches: resilience to broad-band noise and oscillatory interference. Second, the analyses with real EEG signals revealed statistically more robust effects (i.e. improved sensitivity) when using an adaptive frequency tracking framework, particularly when identifying phase-amplitude couplings. This was further confirmed after generating surrogate signals from the real EEG data. Adaptive frequency tracking appears to improve the measurements of cross-frequency couplings through precise extraction of neuronal oscillations.     

Phase velocity measurement of flexural waves in human tibia

A method is proposed to measure the phase velocities of the first mode of flexural waves in the human tibia. Keeping in mind the dispersive nature of flexural waves in beam-like bodies, a two point measurement method was developed which enables the calculation of the phase difference of the propagating wave between two observation points for a selected frequency range. The method for dispersion analysis was tested with synthetic and observed signals for a cylinder. This was done by comparison of observed radial acceleration on the surface of a PVC-cylinder with computed synthetic signals consisting only of first mode flexural waves. An in vivo study was performed with 43 subjects. The phase velocity measurements in human tibia show a good correlation with the bone mineral content estimated by means of the Cameron-Sorenson technique (Cameron and Sorenson, 1963). The bone mineral loss is reflected by decreasing phase velocities. This indicates that phase velocity measurements of flexural waves can be used for an estimation of bone mineral content in vivo.