Timely detection of dynamical change in scalp EEG signals

A Spherical Phase Space Partitioning Based Symbolic Time Series Analysis (SPSP—STSA) for Emotion Recognition Using EEG Signals

Emotion recognition systems have been of interest to researchers for a long time. Improvement of brain-computer interface systems currently makes EEG-based emotion recognition more attractive. These systems try to develop strategies that are capable of recognizing emotions automatically. There are many approaches due to different features extractions methods for analyzing the EEG signals. Still, Since the brain is supposed to be a nonlinear dynamic system, it seems a nonlinear dynamic analysis tool may yield more convenient results. A novel approach in Symbolic Time Series Analysis (STSA) for signal phase space partitioning and symbol sequence generating is introduced in this study. Symbolic sequences have been produced by means of spherical partitioning of phase space; then, they have been compared and classified based on the maximum value of a similarity index. Obtaining the automatic independent emotion recognition EEG-based system has always been discussed because of the subject-dependent content of emotion. Here we introduce a subject-independent protocol to solve the generalization problem. To prove our method’s effectiveness, we used the DEAP dataset, and we reached an accuracy of 98.44% for classifying happiness from sadness (two- emotion groups). It was 93.75% for three (happiness, sadness, and joy), 89.06% for four (happiness, sadness, joy, and terrible), and 85% for five emotional groups (happiness, sadness, joy, terrible and mellow). According to these results, it is evident that our subject-independent method is more accurate rather than many other methods in different studies. In addition, a subject-independent method has been proposed in this study, which is not considered in most of the studies in this field.

A General Metric for the Similarity of Both Stochastic and Deterministic System Dynamics

Many problems in the study of dynamical systems—including identification of effective order, detection of nonlinearity or chaos, and change detection—can be reframed in terms of assessing the similarity between dynamical systems or between a given dynamical system and a reference. We introduce a general metric of dynamical similarity that is well posed for both stochastic and deterministic systems and is informative of the aforementioned dynamical features even when only partial information about the system is available. We describe methods for estimating this metric in a range of scenarios that differ in respect to contol over the systems under study, the deterministic or stochastic nature of the underlying dynamics, and whether or not a fully informative set of variables is available. Through numerical simulation, we demonstrate the sensitivity of the proposed metric to a range of dynamical properties, its utility in mapping the dynamical properties of parameter space for a given model, and its power for detecting structural changes through time series data.

Mutual Information of Multiple Rhythms for EEG Signals

Electroencephalograms (EEG) are one of the most commonly used measures to study brain functioning at a macroscopic level. The structure of the EEG time series is composed of many neural rhythms interacting at different spatiotemporal scales. This interaction is often named as cross frequency coupling, and consists of transient couplings between various parameters of different rhythms. This coupling has been hypothesized to be a basic mechanism involved in cognitive functions. There are several methods to measure cross frequency coupling between two rhythms but no single method has been selected as the gold standard. Current methods only serve to explore two rhythms at a time, are computationally demanding, and impose assumptions about the nature of the signal. Here we present a new approach based on Information Theory in which we can characterize the interaction of more than two rhythms in a given EEG time series. It estimates the mutual information of multiple rhythms (MIMR) extracted from the original signal. We tested this measure using simulated and real empirical data. We simulated signals composed of three frequencies and background noise. When the coupling between each frequency component was manipulated, we found a significant variation in the MIMR. In addition, we found that MIMR was sensitive to real EEG time series collected with open vs. closed eyes, and intra-cortical recordings from epileptic and non-epileptic signals registered at different regions of the brain. MIMR is presented as a tool to explore multiple rhythms, easy to compute and without a priori assumptions.

Identification of Alzheimer's EEG With a WVG Network-Based Fuzzy Learning Approach

A novel analytical framework combined fuzzy learning and complex network approaches is proposed for the identification of Alzheimer's disease (AD) with multichannel scalp-recorded electroencephalograph (EEG) signals. Weighted visibility graph (WVG) algorithm is first applied to transform each channel EEG into network and its topological parameters were further extracted. Statistical analysis indicates that AD and normal subjects show significant difference in the structure of WVG network and thus can be used to identify Alzheimer's disease. Taking network parameters as input features, a Takagi-Sugeno-Kang (TSK) fuzzy model is established to identify AD's EEG signal. Three feature sets-single parameter from multi-networks, multi-parameters from single network, and multi-parameters from multi-networks-are considered as input vectors. The number and order of input features in each set is optimized with various feature selection methods. Classification results demonstrate the ability of network-based TSK fuzzy classifiers and the feasibility of three input feature sets. The highest accuracy that can be achieved is 95.28% for single parameter from four networks, 93.41% for three parameters from single network. In particular, multi-parameters from the multi-networks set obtained the best result. The highest accuracy, 97.12%, is achieved with five features selected from four networks. The combination of network and fuzzy learning can highly improve the efficiency of AD's EEG identification.

Ngram-derived pattern recognition for the detection and prediction of epileptic seizures

This work presents a new method that combines symbol dynamics methodologies with an Ngram algorithm for the detection and prediction of epileptic seizures. The presented approach specifically applies Ngram-based pattern recognition, after data pre-processing, with similarity metrics, including the Hamming distance and Needlman-Wunsch algorithm, for identifying unique patterns within epochs of time. Pattern counts within each epoch are used as measures to determine seizure detection and prediction markers. Using 623 hours of intracranial electrocorticogram recordings from 21 patients containing a total of 87 seizures, the sensitivity and false prediction/detection rates of this method are quantified. Results are quantified using individual seizures within each case for training of thresholds and prediction time windows. The statistical significance of the predictive power is further investigated. We show that the method presented herein, has significant predictive power in up to 100% of temporal lobe cases, with sensitivities of up to 70-100% and low false predictions (dependant on training procedure). The cases of highest false predictions are found in the frontal origin with 0.31-0.61 false predictions per hour and with significance in 18 out of 21 cases. On average, a prediction sensitivity of 93.81% and false prediction rate of approximately 0.06 false predictions per hour are achieved in the best case scenario. This compares to previous work utilising the same data set that has shown sensitivities of up to 40-50% for a false prediction rate of less than 0.15/hour.

Responsive neurostimulation for the treatment of medically intractable epilepsy

With an annual incidence of 50/100,000 people, nearly 1% of the population suffers from epilepsy. Treatment with antiepileptic medication fails to achieve seizure remission in 20-30% of patients. One treatment option for refractory epilepsy patients who would not otherwise be surgical candidates is electrical stimulation of the brain, which is a rapidly evolving and reversible adjunctive therapy. Therapeutic stimulation can involve direct stimulation of the brain nuclei or indirect stimulation of peripheral nerves. There are three stimulation modalities that have class I evidence supporting their uses: vagus nerve stimulation (VNS), stimulation of the anterior nuclei of the thalamus (ANT), and, the most recently developed, responsive neurostimulation (RNS). While the other treatment modalities outlined deliver stimulation regardless of neuronal activity, the RNS administers stimulation only if triggered by seizure activity. The lower doses of stimulation provided by such responsive devices can not only reduce power consumption, but also prevent adverse reactions caused by continuous stimulation, which include the possibility of habituation to long-term stimulation. RNS, as an investigational treatment for medically refractory epilepsy, is currently under review by the FDA. Eventually systems may be developed to enable activation by neurochemical triggers or to wirelessly transmit any information gathered. We review the mechanisms, the current status, the target options, and the prospects of RNS for the treatment of medically intractable epilepsy.

Dynamical nonstationarity of resting EEGs in patients with attention-deficit/hyperactivity disorder (AD/HD)

This study applied dynamical nonstationarity analysis (DNA) to the resting EEGs of patients with attention-deficit/hyperactivity disorder (AD/HD). We aimed to assess and characterize AD/HD using features based on the local and global duration of dynamical microstate. We hypothesized that AD/HD patients would have difficulties in maintaining stable cognitive states (e.g., attention deficit and impulsivity) and that they would thus exhibit EEGs with temporal dynamics distinct from normal controls, i.e., rapidly and frequently changing dynamics. To test this hypothesis, we recorded EEGs from 12 adolescent subjects with AD/HD and 11 age-matched healthy subjects in the resting state with eyes closed and eyes open. We found that AD/HD patients exhibited significantly faster changes in dynamics than controls in the right temporal region during the eyes closed condition, but slower changes in dynamics in the frontal region during the eyes open condition. AD/HD patients exhibited a disruption in the rate of change of dynamics in the frontotemporal region at rest, probably due to executive and attention processes. We suggest that the DNA using complementary local and global features based on the duration of dynamical microstates could be a useful tool for the clinical diagnosis of subjects with AD/HD.

Detecting epileptic seizure from scalp EEG using Lyapunov spectrum

One of the inherent weaknesses of the EEG signal processing is noises and artifacts. To overcome it, some methods for prediction of epilepsy recently reported in the literature are based on the evaluation of chaotic behavior of intracranial electroencephalographic (EEG) recordings. These methods reduced noises, but they were hazardous to patients. In this study, we propose using Lyapunov spectrum to filter noise and detect epilepsy on scalp EEG signals only. We determined that the Lyapunov spectrum can be considered as the most expected method to evaluate chaotic behavior of scalp EEG recordings and to be robust within noises. Obtained results are compared to the independent component analysis (ICA) and largest Lyapunov exponent. The results of detecting epilepsy are compared to diagnosis from medical doctors in case of typical general epilepsy.

Seizure prediction and its applications

Epilepsy is characterized by intermittent, paroxysmal, hypersynchronous electrical activity that may remain localized and/or spread and severely disrupt the brain's normal multitask and multiprocessing function. Epileptic seizures are the hallmarks of such activity. The ability to issue warnings in real time of impending seizures may lead to novel diagnostic tools and treatments for epilepsy. Applications may range from a warning to the patient to avert seizure-associated injuries, to automatic timely administration of an appropriate stimulus. Seizure prediction could become an integral part of the treatment of epilepsy through neuromodulation, especially in the new generation of closed-loop seizure control systems.

Observability of nonlinear dynamics: normalized results and a time-series approach

This paper investigates the observability of nonlinear dynamical systems. Two difficulties associated with previous studies are dealt with. First, a normalized degree observability is defined. This permits the comparison of different systems, which was not generally possible before. Second, a time-series approach is proposed based on omnidirectional nonlinear correlation functions to rank a set of time series of a system in terms of their potential use to reconstruct the original dynamics without requiring the knowledge of the system equations. The two approaches proposed in this paper and a former method were applied to five benchmark systems and an overall agreement of over 92% was found.

Epileptic seizure detection from ECoG using recurrence time statistics

A recurrence time statistics T1 is defined and used as a feature extraction method for seizure detection. The preliminary data shows that during seizure T1 generates a peak and this peak clearly distinguishes the seizure state from background activity. When applied to multi-channel ECoG recordings, the spatial-temporal signature of T1 can be clearly observed to discriminate seizures. The T1 feature was used for automated seizure detection on two sets of long term monitoring ECoG data. The detection probability reached 97% with a 0.29 per hour average false alarm rate.

Complexity analysis of stride interval time series by threshold dependent symbolic entropy

The stride interval of human gait fluctuates in complex fashion. It reflects the rhythm of the locomotor system. The temporal fluctuations in the stride interval provide us a non-invasive technique to evaluate the effects of neurological impairments on gait and its changes with age and disease. In this paper, we have used threshold dependent symbolic entropy, which is based on symbolic nonlinear time series analysis to study complexity of gait of control and neurodegenerative disease subjects. Symbolic entropy characterizes quantitatively the complexity even in time series having relatively few data points. We have calculated normalized corrected Shannon entropy (NCSE) of symbolic sequences extracted from stride interval time series. This measure of complexity showed significant difference between control and neurodegenerative disease subjects for a certain range of thresholds. We have also investigated complexity of physiological signal and randomized noisy data. In the study, we have found that the complexity of physiological signal was higher than that of random signals at short threshold values.

Epileptic seizure predictability from scalp EEG incorporating constrained blind source separation

Most of the methods for prediction of epilepsy recently reported in the literature are based on the evaluation of chaotic behavior of intracranial electroencephalographic (EEG) recordings. These recordings require intensive surgical operations to implant the electrodes within the brain which are hazardous to the patient. Here, we have developed a novel approach to quantify the dynamical changes of the brain using the scalp EEG. The scalp signals are preprocessed by means of an effective block-based blind source separation (BSS) technique to separate the underlying sources within the brain. The algorithm significantly removes the effect of eye blinking artifacts. An overlap window procedure has been incorporated in order to mitigate the inherent permutation problem of BSS and maintain the continuity of the estimated sources. Chaotic behavior of the underlying sources has then been evaluated by measuring the largest Lyapunov exponent. For our experiments, we provided twenty sets of simultaneous intracranial and scalp EEG recordings from twenty patients. The above recordings have been compared. Similar results were obtained when the intracranial electrodes recorded the electrical activity of the epileptic focus. Our preliminary results show a great improvement when the epileptic focus is not captured by the intracranial electrodes.

Seizure anticipation: from algorithms to clinical practice

PURPOSE OF REVIEW

Our understanding of the mechanisms that lead to the occurrence of epileptic seizures is rather incomplete. If it were possible to identify preictal precursors from the EEG of epilepsy patients, therapeutic possibilities could improve dramatically. Studies on seizure prediction have advanced from preliminary descriptions of preictal phenomena via proof of principle studies and controlled studies to studies on continuous multi-day recordings.

RECENT FINDINGS

Following mostly promising early reports, recent years have witnessed a debate over the reproducibility of results and suitability of approaches. The current literature is inconclusive as to whether seizures are predictable by prospective algorithms. Prospective out-of-sample studies including a statistical validation are missing. Nevertheless, there are indications of a superior performance for approaches characterizing relations between different brain regions.

SUMMARY

Prediction algorithms must be proven to perform better than a random predictor before prospective clinical trials involving seizure intervention techniques in patients can be justified.

A Robust Method for Detection of Linear and Nonlinear Interactions: Application to Renal Blood Flow Dynamics

We have developed a method that can identify switching dynamics in time series, termed the improved annealed competition of experts (IACE) algorithm. In this paper, we extend the approach and use it for detection of linear and nonlinear interactions, by employing histograms showing the frequency of switching modes obtained from the IACE, then examining time-frequency spectra. This extended approach is termed Histogram of improved annealed competition of experts-time frequency (HIACE-TF). The hypothesis is that frequent switching dynamics in HIACE-TF results are due to interactions between different dynamic components. To validate this assertion, we used both simulation examples as well as application to renal blood flow data. We compared simulation results to a time-phase bispectrum (TPB) approach, which can also be used to detect time-varying quadratic phase coupling between various components. We found that the HIACE-TF approach is more accurate than the TPB in detecting interactions, and remains accurate for signal-to-noise ratios as low as 15 dB. With all 10 data sets, comprised of volumetric renal blood flow data, we also validated the feasibility of the HIACE-TF approach in detecting nonlinear interactions between the two mechanisms responsible for renal autoregulation. Further validation of the HIACE-TF approach was achieved by comparing it to a realistic mathematical model that has the capability to generate either the presence or the absence of nonlinear interactions between two renal autoregulatory mechanisms.

A Method for Segmentation of Switching Dynamic Modes in Time Series

A method to identify switching dynamics in time series, based on Annealed Competition of Experts algorithm (ACE), has been developed by Kohlmorgen et al. Incorrect selection of embedding dimension and time delay of the signal significantly affect the performance of the ACE method, however. In this paper, we utilize systematic approaches based on mutual information and false nearest neighbor to determine appropriate embedding dimension and time delay. Moreover, we obtained further improvements to the original ACE method by incorporating a deterministic annealing approach as well as phase space closeness measure. Using these improved implementations, we have enhanced the performance of the ACE algorithm in determining the location of the switching of dynamic modes in the time series. The application of the improved ACE method to heart rate data obtained from rats during control and administration of double autonomic blockade conditions indicate that the improved ACE algorithm is able to segment dynamic mode changes with pinpoint accuracy and that its performance is superior to the original ACE algorithm.

Correlation dimension and integral do not predict epileptic seizures

Reports in the literature have indicated potential value of the correlation integral and dimension for prediction of epileptic seizures up to several minutes before electrographic onset. We apply these measures to over 2000 total hours of continuous electrocortiogram, taken from 20 patients with epilepsy, examine their sensitivity to quantifiable properties such as the signal amplitude and autocorrelation, and investigate the influence of embedding and filtering strategies on their performance. The results are compared against those obtained from surrogate time series. Our conclusion is that neither the correlation dimension nor the correlation integral has predictive power for seizures.

Detecting dynamical changes in time series using the permutation entropy

Timely detection of unusual and/or unexpected events in natural and man-made systems has deep scientific and practical relevance. We show that the recently proposed conceptually simple and easily calculated measure of permutation entropy can be effectively used to detect qualitative and quantitative dynamical changes. We illustrate our results on two model systems as well as on clinically characterized brain wave data from epileptic patients.

Controlled test for predictive power of Lyapunov exponents: their inability to predict epileptic seizures

Lyapunov exponents are a set of fundamental dynamical invariants characterizing a system's sensitive dependence on initial conditions. For more than a decade, it has been claimed that the exponents computed from electroencephalogram (EEG) or electrocorticogram (ECoG) signals can be used for prediction of epileptic seizures minutes or even tens of minutes in advance. The purpose of this paper is to examine the predictive power of Lyapunov exponents. Three approaches are employed. (1) We present qualitative arguments suggesting that the Lyapunov exponents generally are not useful for seizure prediction. (2) We construct a two-dimensional, nonstationary chaotic map with a parameter slowly varying in a range containing a crisis, and test whether this critical event can be predicted by monitoring the evolution of finite-time Lyapunov exponents. This can thus be regarded as a "control test" for the claimed predictive power of the exponents for seizure. We find that two major obstacles arise in this application: statistical fluctuations of the Lyapunov exponents due to finite time computation and noise from the time series. We show that increasing the amount of data in a moving window will not improve the exponents' detective power for characteristic system changes, and that the presence of small noise can ruin completely the predictive power of the exponents. (3) We report negative results obtained from ECoG signals recorded from patients with epilepsy. All these indicate firmly that, the use of Lyapunov exponents for seizure prediction is practically impossible as the brain dynamical system generating the ECoG signals is more complicated than low-dimensional chaotic systems, and is noisy.

Machine failure forewarning via phase-space dissimilarity measures

We present a model-independent, data-driven approach to quantify dynamical changes in nonlinear, possibly chaotic, processes with application to machine failure forewarning. From time-windowed data sets, we use time-delay phase-space reconstruction to obtain a discrete form of the invariant distribution function on the attractor. Condition change in the system's dynamic is quantified by dissimilarity measures of the difference between the test case and baseline distribution functions. We analyze time-serial mechanical (vibration) power data from several large motor-driven systems with accelerated failures and seeded faults. The phase-space dissimilarity measures show a higher consistency and discriminating power than traditional statistical and nonlinear measures, which warrants their use for timely forewarning of equipment failure.

Toward a neurodynamical understanding of ictogenesis

Although considerable information on cellular and network mechanisms of epilepsy exists, it is still not understood why, how, and when the transition from interictal to ictal state takes place. The authors review their work on nonlinear EEG analysis and provide consistent evidences that dynamical changes in the neural activity allows the characterization of a preictal state several minutes before seizure onset. This new neurodynamical approach of ictogenesis opens new perspectives for studying the basic mechanisms in epilepsy as well as for possible therapeutic interventions.

Inability of Lyapunov exponents to predict epileptic seizures

It has been claimed that Lyapunov exponents computed from electroencephalogram or electrocorticogram (ECoG) time series are useful for early prediction of epileptic seizures. We show, by utilizing a paradigmatic chaotic system, that there are two major obstacles that can fundamentally hinder the predictive power of Lyapunov exponents computed from time series: finite-time statistical fluctuations and noise. A case study with an ECoG signal recorded from a patient with epilepsy is presented.

Channel-consistent forewarning of epileptic events from scalp EEG

Phase-space dissimilarity measures (PSDM) have been recently proposed to provide forewarning of impending epileptic events from scalp electroencephalographic (EEG) for eventual ambulatory settings. Despite high noise in scalp EEG, PSDM yield consistently superior performance over traditional nonlinear indicators, such as Kolmogorov entropy, Lyapunov exponents, and correlation dimension. However, blind application of PSDM may result in channel inconsistency, whereby multiple datasets from the same patient yield conflicting forewarning indications in the same channel. This paper presents a first attempt to solve this problem.

Epileptic seizure prediction and control

Epileptic seizures are manifestations of epilepsy, a serious brain dynamical disorder second only to strokes. Of the world's approximately 50 million people with epilepsy, fully 1/3 have seizures that are not controlled by anti-convulsant medication. The field of seizure prediction, in which engineering technologies are used to decode brain signals and search for precursors of impending epileptic seizures, holds great promise to elucidate the dynamical mechanisms underlying the disorder, as well as to enable implantable devices to intervene in time to treat epilepsy. There is currently an explosion of interest in this field in academic centers and medical industry with clinical trials underway to test potential prediction and intervention methodology and devices for Food and Drug Administration (FDA) approval. This invited paper presents an overview of the application of signal processing methodologies based upon the theory of nonlinear dynamics to the problem of seizure prediction. Broader application of these developments to a variety of systems requiring monitoring, forecasting and control is a natural outgrowth of this field.

Human EEG responses to controlled alterations of the Earth's magnetic field

OBJECTIVES

Examine the effects of controlled changes in the Earth's magnetic field on electroencephalogram (EEG) and subjective report.

METHODS

Fifty volunteers were exposed double-blind to changes in field magnitude, angle of inclination, and angle of deviation. Volunteers were also exposed to magnetic field conditions found near the North and South Pole. EEG recorded over temporal and occipital sites was compared across 4s baseline, field exposure, and no-change control trials.

RESULTS

No EEG spectral differences as a function of gender or recording site were found. Geomagnetic field alterations had no effect on total energy (0.5-42 Hz), energy within traditional EEG analysis bands, or on the 95% spectral edge. Most volunteers reported no sensations; others reported non-specific symptoms unrelated to type of field change.

DISCUSSION

Three hypothesized field detection mechanisms were not supported: (1) mechanical reception through torque exerted on the ferromagnetic material magnetite; (2) movement-induced induction of an electric field in the body; and (3) enhanced sensitivity due to alterations in the rates of chemical reactions involving electron spin states.

CONCLUSIONS

Humans have little ability to detect brief alterations in the geomagnetic field, even if these alteration are of a large magnitude.

Correlation-dimension and autocorrelation fluctuations in epileptic seizure dynamics

We focus on an anomalous scaling region in correlation integral [C(epsilon)] analysis of electrocorticogram in epilepsy patients. We find that epileptic seizures typically are accompanied by wide fluctuations in the slope of this scaling region. An explanation, based on analyzing the interplay between the autocorrelation and C(epsilon), is provided for these fluctuations. This anomalous slope appears to be a sensitive measure for tracking (but not predicting) seizures.

Characterizing neurodynamic changes before seizures

The study of dynamic changes in neural activity preceding epileptic seizure allows the characterization of a preictal state several minutes before seizure onset. This opens up new perspectives for studying the mechanisms of epileptogenesis as well as for possible therapeutic interventions, which represent a major breakthrough. In this review the authors present and discuss the results from their group in this domain using nonlinear analysis of brain signals, as well as the limitations of this topic and current questions.

Epileptic event forewarning from scalp EEG

The authors present a model-independent approach to quantify changes in the dynamics underlying nonlinear time-serial data. From time-windowed datasets, the authors construct discrete distribution functions on the phase space. Condition change between base case and test case distribution functions is assessed by dissimilarity measures via L1 distance and chi2 statistic. The discriminating power of these measures is first tested on noiseless data from the Lorenz and Bondarenko models, and is then applied to detecting dynamic change in multichannel clinical scalp EEG data. The authors compare the dissimilarity measures with the traditional nonlinear measures used in the analysis of chaotic systems. They also assess the potential usefulness of the new measures for robust, accurate, and timely forewarning of epileptic events.