Brain signal analysis based on recurrences

The EEG is one of the most commonly used tools in brain research. Though of high relevance in research, the data obtained is very noisy and nonstationary. In the present article we investigate the applicability of a nonlinear data analysis method, the recurrence quantification analysis (RQA), to such data. The method solely rests on the natural property of recurrence which is a phenomenon inherent to complex systems, such as the brain. We show that this method is indeed suitable for the analysis of EEG data and that it might improve contemporary EEG analysis.     

RQA在肌电分析中的应用

介绍了递归图的生成方法和定量递归分析（RQA）中递归点的百发数,确定性线段的百分数,线段分布香农熵等分析量的意义。应用RQA分析肱二头肌及肱桡肌在不同负重下的肌电信号,发现肽二头肌肌电信号的递归点百分数的比肱肌高,有较强的周期性嵌入。与同一信号所作的FFT谱分析相比较,RQA法有较同的区分灵敏性,是肌电分析的一种新方法,它在其他复杂的生理信号处理中也有十分广阔的应用前景。     

Anaesthesia Monitoring by Recurrence Quantification Analysis of EEG Data

Appropriate monitoring of the depth of anaesthesia is crucial to prevent deleterious effects of insufficient anaesthesia on surgical patients. Since cardiovascular parameters and motor response testing may fail to display awareness during surgery, attempts are made to utilise alterations in brain activity as reliable markers of the anaesthetic state. Here we present a novel, promising approach for anaesthesia monitoring, basing on recurrence quantification analysis (RQA) of EEG recordings. This nonlinear time series analysis technique separates consciousness from unconsciousness during both remifentanil/sevoflurane and remifentanil/propofol anaesthesia with an overall prediction probability of more than 85%, when applied to spontaneous one-channel EEG activity in surgical patients.     

Revealing deterministic structures in click-evoked otoacoustic emissions.

Click-evoked otoacoustic emissions (CEOAEs) were studied by means of recurrence quantification analysis (RQA) and were found to be endowed with a relevant amount of deterministic structuring. Such a structure showed highly significant correlation with the clinical evaluation of the signal over a data set including 56 signals. Moreover, 1) one of the RQA variables, Trend, was very sensitive to phase transitions in the dynamical regime of CEOAEs, and 2) appropriate use of principal component analysis proved able to isolate the individual character of the studied signals. These results are of general interest for the study of auditory signal transduction and generation mechanisms.     

Nonlinear time-course of lumbar muscle fatigue using recurrence quantifications

 Isometric skeletal muscle fatigue is usually assumed to be a linear process based upon the monotonic decrease in spectral frequency of the EMG. Since spectral analysis by fast Fourier transform (FFT) constitutes a linear transformation of the data, the present study was designed to reevaluate the time-course of muscle fatigue with a nonlinear tool, recurrence quantification analysis (RQA). Surface EMG recordings were obtained from the multifidus muscle of 17 human subjects during isometric posture-holding of the upper torso. The process of muscle fatigue was found to be linear for 59% of the subjects by FFT criteria, but nonlinear for 76% by RQA criteria. As a demonstrative control, both slow and fast transients occurring within a nonlinear mathematical process could be accurately depicted by RQA, but not by FFT. It is concluded that assessment of EMG patterns by nonlinear techniques can give insight into the time-course of fatiguing muscles attributed to the summation of several nonlinear and competing processes. Received: 12 November 1998 / Accepted in revised form: 29 November 1999     

Application of recurrence quantification analysis for the automated identification of epileptic EEG signals

Epilepsy is a common neurological disorder that is characterized by the recurrence of seizures. Electroencephalogram (EEG) signals are widely used to diagnose seizures. Because of the non-linear and dynamic nature of the EEG signals, it is difficult to effectively decipher the subtle changes in these signals by visual inspection and by using linear techniques. Therefore, non-linear methods are being researched to analyze the EEG signals. In this work, we use the recorded EEG signals in Recurrence Plots (RP), and extract Recurrence Quantification Analysis (RQA) parameters from the RP in order to classify the EEG signals into normal, ictal, and interictal classes. Recurrence Plot (RP) is a graph that shows all the times at which a state of the dynamical system recurs. Studies have reported significantly different RQA parameters for the three classes. However, more studies are needed to develop classifiers that use these promising features and present good classification accuracy in differentiating the three types of EEG segments. Therefore, in this work, we have used ten RQA parameters to quantify the important features in the EEG signals.These features were fed to seven different classifiers: Support vector machine (SVM), Gaussian Mixture Model (GMM), Fuzzy Sugeno Classifier, K-Nearest Neighbor (KNN), Naive Bayes Classifier (NBC), Decision Tree (DT), and Radial Basis Probabilistic Neural Network (RBPNN). Our results show that the SVM classifier was able to identify the EEG class with an average efficiency of 95.6%, sensitivity and specificity of 98.9% and 97.8%, respectively.     

Nonlinear features and complexity patterns of vegetation dynamics in the transition zone of North China

Normalized Difference Vegetation Index (NDVI) has been commonly used to estimate terrestrial vegetation distribution and productivity. In this study, we adopted recurrence quantification analysis (RQA) to investigate the spatial patterns of determinism of the vegetation dynamics ecological-geographical transition zones in North China, especially the differences between transition zone and the surrounding areas. The results indicated that there were obvious regional variances in spatial patterns of RQA indices—determinism, laminarity, entropy, and averaged diagonal line length. Remarkable differences of the determinism of NDVI time series also existed between transition zones and the surrounding areas. Moreover, the correlation analysis between the RQA indices and climatic factors suggested that the determinism of the NDVI time series was nonlinearly affected by hydrothermal conditions. Influenced by vegetation patterns, determinism reached the maximum when the annual precipitation is about 400 mm, which is the lower bound of cultivation and forest distribution, and along the 400 mm isohyet is the area where transition zones locate.     

Cognitive neuroscience of creativity: EEG based approaches

Cognitive neuroscience of creativity has been extensively studied using non-invasive electrical recordings from the scalp called electroencephalograms (EEGs) and event related potentials (ERPs). The paper discusses major aspects of performing research using EEG/ERP based experiments including the recording of the signals, removing noise, estimating ERP signals, and signal analysis for better understanding of the neural correlates of processes involved in creativity. Important factors to be kept in mind to record clean EEG signal in creativity research are discussed. The recorded EEG signal can be corrupted by various sources of noise and methodologies to handle the presence of unwanted artifacts and filtering noise are presented followed by methods to estimate ERPs from the EEG signals from multiple trials. The EEG and ERP signals are further analyzed using various techniques including spectral analysis, coherence analysis, and non-linear signal analysis. These analysis techniques provide a way to understand the spatial activations and temporal development of large scale electrical activity in the brain during creative tasks. The use of this methodology will further enhance our understanding the processes neural and cognitive processes involved in creativity.     

Comparing the HRV Time-Series Signals Acquired from Cannabis Consuming and Non-Consuming Indian Paddy-Field Workers by Recurrence Quantification Analysis

ObjectiveIn the last few decades, the consumption of cannabis-based products for recreational purposes has dramatically increased. Unfortunately, cannabis consumption has been associated with the incidences of cardiovascular diseases. Hence, there is a necessity for understanding the plausible mechanics of cardiophysiological changes due to cannabis consumption. Accordingly, the current study was designed to understand the suitability of the recurrence quantification analysis (RQA) method in detecting the changes in the heart rate variability (HRV) time-series signals due to the consumption of cannabis (bhang). Further, a machine learning model has been proposed for the automated detection of the cannabis takers.Materials and MethodsThe RQA of the HRV time-series signals from 200 healthy Indian male paddy-field workers were carried out. The obtained parameters were statistically analyzed using the Mann-Whitney U test. Further, the decision trees, weight-based feature ranking, and dimensionality reduction methods were employed for identifying the relevant features for the development of a suitable machine learning model.ResultsObservable changes in the patterns of the recurrence plots among the bhang consuming and non-consuming groups were noticed. However, there were no significant differences in the RQA parameters. Among the developed machine learning models, the SVM model obtained from the “Information gain ratio” feature selection method exhibited the highest accuracy (%) of 69.09 ± 9.33.ConclusionOur study suggests that the RQA method is not as effective as the time and frequency domain methods for detecting the alterations in the HRV time-series signals due to cannabis consumption. The SVM model was found to be the best model for the automated detection of cannabis takers. The selection of the features by the information gain ratio method played an important role in the development of the optimized SVM model.     

Extracting Visual Evoked Potentials from EEG Data Recorded During fMRI-guided Transcranial Magnetic Stimulation

Transcranial Magnetic Stimulation (TMS) is an effective method for establishing a causal link between a cortical area and cognitive/neurophysiological effects. Specifically, by creating a transient interference with the normal activity of a target region and measuring changes in an electrophysiological signal, we can establish a causal link between the stimulated brain area or network and the electrophysiological signal that we record. If target brain areas are functionally defined with prior fMRI scan, TMS could be used to link the fMRI activations with evoked potentials recorded. However, conducting such experiments presents significant technical challenges given the high amplitude artifacts introduced into the EEG signal by the magnetic pulse, and the difficulty to successfully target areas that were functionally defined by fMRI. Here we describe a methodology for combining these three common tools: TMS, EEG, and fMRI. We explain how to guide the stimulator''s coil to the desired target area using anatomical or functional MRI data, how to record EEG during concurrent TMS, how to design an ERP study suitable for EEG-TMS combination and how to extract reliable ERP from the recorded data. We will provide representative results from a previously published study, in which fMRI-guided TMS was used concurrently with EEG to show that the face-selective N1 and the body-selective N1 component of the ERP are associated with distinct neural networks in extrastriate cortex. This method allows us to combine the high spatial resolution of fMRI with the high temporal resolution of TMS and EEG and therefore obtain a comprehensive understanding of the neural basis of various cognitive processes.     

A new method to detect event-related potentials based on Pearson’s correlation

Event-related potentials (ERPs) are widely used in brain-computer interface applications and in neuroscience.  Normal EEG activity is rich in background noise, and therefore, in order to detect ERPs, it is usually necessary to take the average from multiple trials to reduce the effects of this noise.  The noise produced by EEG activity itself is not correlated with the ERP waveform and so, by calculating the average, the noise is decreased by a factor inversely proportional to the square root of N, where N is the number of averaged epochs. This is the easiest strategy currently used to detect ERPs, which is based on calculating the average of all ERP’s waveform, these waveforms being time- and phase-locked.  In this paper, a new method called GW6 is proposed, which calculates the ERP using a mathematical method based only on Pearson’s correlation. The result is a graph with the same time resolution as the classical ERP and which shows only positive peaks representing the increase—in consonance with the stimuli—in EEG signal correlation over all channels.  This new method is also useful for selectively identifying and highlighting some hidden components of the ERP response that are not phase-locked, and that are usually hidden in the standard and simple method based on the averaging of all the epochs.  These hidden components seem to be caused by variations (between each successive stimulus) of the ERP’s inherent phase latency period (jitter), although the same stimulus across all EEG channels produces a reasonably constant phase. For this reason, this new method could be very helpful to investigate these hidden components of the ERP response and to develop applications for scientific and medical purposes. Moreover, this new method is more resistant to EEG artifacts than the standard calculations of the average and could be very useful in research and neurology.  The method we are proposing can be directly used in the form of a process written in the well-known Matlab programming language and can be easily and quickly written in any other software language.     

脑电信息处理

脑电信息处理是脑功能研究重要组成部分。本文介绍了脑电信息处理的前沿领域,包括诱发电位、事件相关电位（ＥＲＰ）、正弦调制光（声）诱发脑电、４０ＨｚＥＲＰ和脑电非线笥动力学研究,并论及了认知活动与分形维数的关系。     

Elucidating protein secondary structures using alpha-carbon recurrence quantifications.

Secondary structures of proteins were studied by recurrence quantification analysis (RQA). High-resolution, 3-dimensional coordinates of alpha-carbon atoms comprising a set of 68 proteins were downloaded from the Protein Data Bank. By fine-tuning four recurrence parameters (radius, line, residue, separation), it was possible to establish excellent agreement between percent contribution of alpha-helix and beta-sheet structures determined independently by RQA and that of the DSSP algorithm (Define Secondary Structure of Proteins). These results indicate that there is an equivalency between these two techniques, which are based upon totally different pattern recognition strategies. RQA enhances qualitative contact maps by quantifying the arrangements of recurrent points of alpha carbons close in 3-dimensional space. For example, the radius was systematically increased, moving the analysis beyond local alpha-carbon neighborhoods in order to capture super-secondary and tertiary structures. However, differences between proteins could only be detected within distances up to about 6-11 A, but not higher. This result underscores the complexity of alpha-carbon spacing when super-secondary structures appear at larger distances. Finally, RQA-defined secondary structures were found to be robust against random displacement of alpha carbons upwards of 1 A. This finding has potential import for the dynamic functions of proteins in motion.     

Wavelet-based analysis of MMN responses in children.

Event-related potentials (ERPs) as part of the EEG are applied to assess auditory processing in children. Mismatch negativity (MMN) is a change-specific component of ERPs that indicates a pre-cognitive discrimination process. MMN responses were recorded in 10 healthy preschool children to four different types of signal changes. The signals investigated were processed using a discrete wavelet transform (DWT) to analyze the characteristics of the ERP components. All children showed distinct MMN that was significant in all tasks. The MMN amplitudes varied between subjects and depended on the different tasks. The wavelet transform allowed simplified analysis and quantification of the MMN component, as well as the double-peak structure of the P1 component. The variation in MMN amplitudes suggests the possibility of determining individual auditory profiles. Owing to the shorter time required, the MMN paradigm suggested combined with the DWT proposed offers a new objective investigation method for children.     

Prediction of protein structural classes by recurrence quantification analysis based on chaos game representation

In this paper, we intend to predict protein structural classes (α, β, α+β, or α/β) for low-homology data sets. Two data sets were used widely, 1189 (containing 1092 proteins) and 25PDB (containing 1673 proteins) with sequence homology being 40% and 25%, respectively. We propose to decompose the chaos game representation of proteins into two kinds of time series. Then, a novel and powerful nonlinear analysis technique, recurrence quantification analysis (RQA), is applied to analyze these time series. For a given protein sequence, a total of 16 characteristic parameters can be calculated with RQA, which are treated as feature representation of protein sequences. Based on such feature representation, the structural class for each protein is predicted with Fisher's linear discriminant algorithm. The jackknife test is used to test and compare our method with other existing methods. The overall accuracies with step-by-step procedure are 65.8% and 64.2% for 1189 and 25PDB data sets, respectively. With one-against-others procedure used widely, we compare our method with five other existing methods. Especially, the overall accuracies of our method are 6.3% and 4.1% higher for the two data sets, respectively. Furthermore, only 16 parameters are used in our method, which is less than that used by other methods. This suggests that the current method may play a complementary role to the existing methods and is promising to perform the prediction of protein structural classes.     

EMG recurrence quantifications in dynamic exercise

This study was designed to evaluate the suitability of nonlinear recurrence quantification analysis (RQA) in assessing electromyograph (EMG) signals during dynamic exercise. RQA has been proven to be effective in analyzing nonstationary signals. The subject group consisted of 19 male patients diagnosed with low back pain. EMG signals were recorded from left and right paraspinal muscles during isoinertial exercise both before and after 12 weeks of regimented physical therapy. Autorecurrence analysis was performed between the left and right EMG signals individually, and cross-recurrence analysis was performed on the left-right EMG pairs. Spectral analysis of the EMG signals was employed as an independent, objective measure of fatigue. Increase in the RQA variable % determinism during the 90-s dynamic tests was found to be a good marker for fatigue. Before physical therapy, this nonlinear marker revealed simultaneous increases in motor unit recruitment within each pool and between left and right pools. After physical therapy, the motor unit recruitment was less within and between pools, indicative of increased fatigue resistance. Finally, fatigue resistance (less increase in % determinism) correlated well with subjective scores of pain relief. Taken together, these latter results indicate that recurrence analysis may be useful in charting the efficacy of a specific exercise therapy program in reducing low back pain by elevating the fatigue threshold.     

Remote protein homology detection using recurrence quantification analysis and amino acid physicochemical properties

Remote homology detection refers to the detection of structure homology in evolutionarily related proteins with low sequence similarity. Supervised learning algorithms such as support vector machine (SVM) are currently the most accurate methods. In most of these SVM-based methods, efforts have been dedicated to developing new kernels to better use the pairwise alignment scores or sequence profiles. Moreover, amino acids’ physicochemical properties are not generally used in the feature representation of protein sequences. In this article, we present a remote homology detection method that incorporates two novel features: (1) a protein's primary sequence is represented using amino acid's physicochemical properties and (2) the similarity between two proteins is measured using recurrence quantification analysis (RQA). An optimization scheme was developed to select different amino acid indices (up to 10 for a protein family) that are best to characterize the given protein family. The selected amino acid indices may enable us to draw better biological explanation of the protein family classification problem than using other alignment-based methods. An SVM-based classifier will then work on the space described by the RQA metrics. The classification scheme is named as SVM-RQA. Experiments at the superfamily level of the SCOP1.53 dataset show that, without using alignment or sequence profile information, the features generated from amino acid indices are able to produce results that are comparable to those obtained by the published state-of-the-art SVM kernels. In the future, better prediction accuracies can be expected by combining the alignment-based features with our amino acids property-based features. Supplementary information including the raw dataset, the best-performing amino acid indices for each protein family and the computed RQA metrics for all protein sequences can be downloaded from http://ym151113.ym.edu.tw/svm-rqa.