A chaos-based visual encryption mechanism for clinical EEG signals

In this study, we have developed a chaos-based visual encryption mechanism that can be applied for clinical electroencephalography (EEG) signals. In comparison with other types of random sequences, chaos sequences were mainly used to increase unpredictability. We used a 1D chaotic scrambler and a permutation scheme to achieve EEG visual encryption. One approach of realizing the visual encryption mechanism is to scramble the signal values of the input EEG signal by multiplying a 1D chaotic signal to randomize the EEG signal values. We then applied a chaotic address scanning order encryption to the randomized reference values. Simulation results show that when the correct deciphering parameters are entered, the signal is completely recovered, and the percent root-mean-square difference (PRD) values for control and alcoholic clinical EEG signals are 4.33 × 10⁻¹⁵ and 4.11 × 10⁻¹⁵%, respectively. As long as there is an input parameter error, with an initial point error of 0.00000001% as an example, thereby making these clinical EEG signals unrecoverable.     

动画教学模式下大学生课堂短时注意力脑电信号研究

目的通过视频刺激试验分析人脑专注度特征,探讨动画教学模式对于大学生课堂短时注意力的影响。方法从30位观看动画演示法、传统讲授法教学视频的被试者中筛选出20份脑电信号数据,经过预处理后采用事件相关频谱扰动(event-related spectral dynamics,ERSP)进行时频分析,同时结合主观调查问卷,分析动画教学模式对大学生课堂短时注意力的影响。结果时频分析结果显示,在动画教学模式下,时间窗30~150 s内被试者脑电信号能量更高,并且在beta波段(20~35 Hz)时,两种教学模式之下的脑电信号的差异具有统计学意义,F3、AF4、FC6通道处尤为明显。结论动画教学模式能够提升大学生课堂短时注意力,同时beta波段的部分特殊频段存在可识别注意力集中程度的特征值,这能够为高校教学模式的改革以及可穿戴脑机接口设备在智能化课堂教学的应用提供理论基础。     

Testing for non-linearity in EEG signal of healthy subjects

Spontaneous EEG of 21 healthy human subjects obtained by standard procedure of recording is analysed using non-linear prediction methods to check whether the signals were generated by a non-linear dynamics process or by a linear stochastic process. The test for non-linearity is performed by surrogate data method with non-linear prediction error as the test statistic. The null hypothesis that EEG signal (in rest, with eyes closed) is generated by linear stochastic process can be rejected in 17 cases (5%) out of the 336 (21 subjects, 16 channels) studied epochs. However, most of these rejections concern 3 subjects. The 88% of rejections of the null hypothesis concern frontal channels. The null hypothesis is not rejected for epochs recorded with eyes open and during photostimulation.     

EEG and fMRI Coregistration to Investigate the Cortical Oscillatory Activities During Finger Movement

Electroencephalography combined with functional magnetic resonance imaging (EEG-fMRI) may be used to identify blood oxygenation level dependent (BOLD) signal changes associated with physiological and pathological EEG event. In this study we used EEG-fMRI to determine the possible correlation between topographical movement-related EEG changes in brain oscillatory activity recorded from EEG electrodes over the scalp and fMRI-BOLD cortical responses in motor areas during finger movement. Thirty-two channels of EEG were recorded in 9 subjects during eyes-open condition inside a 1.5 T magnetic resonance (MR) scanner using a MR-compatible EEG recording system. Off-line MRI artifact subtraction software was applied to obtain continuous EEG data during␣fMRI acquisition. For EEG data analysis we used the event-related-synchronization/desynchronization (ERS/ERD) approach to investigate where movement-related decreases in alpha and beta power are located. For image statistical analysis we used a general linear model (GLM) approach. There was a significant correlation between the positive-negative ratio of BOLD signal peaks and ERD values in the electrodes over the region of activation. We conclude that combined EEG-fMRI may be used to investigate movement-related oscillations of the human brain inside an MRI scanner and the movement-related changes in the EMG or EEG signals are useful to identify the brain activation sources responsible for BOLD-signal changes.     

Correlation Dimension Maps of EEG from Epileptic Absences

The understanding of brain activity, and in particular events such as epileptic seizures, lies on the characterisation of the dynamics of the neural networks. The theory of non-linear dynamics provides signal analysis techniques which may give new information on the behaviour of such networks. Methods: We calculated correlation dimension maps for 19-channel EEG data from 3 patients with a total of 7 absence seizures. The signals were analysed before, during and after the seizures. Phase randomised surrogate data was used to test chaos. Results: In the seizures of two patients we could distinguish two dynamical regions on the cerebral cortex, one that seemed to exhibit chaos whereas the other seemed to exhibit noise. The pattern shown is essentially the same for seizures triggered by hyperventilation, but differ for seizures triggered by light flashes. The chaotic dynamics that one seems to observe is determined by a small number of variables and has low complexity. On the other hand, in the seizures of another patient no chaotic region was found. Before and during the seizures no chaos was found either, in all cases. Conclusions: The application of non-linear signal analysis revealed the existence of differences in the spatial dynamics associated to absence seizures. This may contribute to the understanding of those seizures and be of assistance in clinical diagnosis.     

Can the analytic techniques of nonlinear dynamics distinguish periodic, random and chaotic signals?

Recent advances in the mathematical discipline of nonlinear dynamics have led to its use in the analysis of many biologic processes. But the ability of the tools of nonlinear dynamic analysis to identify chaotic behavior has not been determined. We analyzed a series of signals--periodic, chaotic and random--with five tools of nonlinear dynamics. Periodic signals were sine, square, triangular, sawtooth, modulated sine waves and quasiperiodic, generated at multiple amplitudes and frequencies. Chaotic signals were generated by solving sets of nonlinear equations including the logistic map, Duffing's equation, Lorenz equations and the Silnikov attractor. Random signals were both discontinuous and continuous. Gaussian noise was added to some signals at magnitudes of 1, 2, 5, 10 and 20% of the signal's amplitude. Each signal was then subjected to tools of nonlinear dynamics (phase plane plot, return map, Poincaré section, correlation dimension and spectral analysis) to determine the relative ability of each to characterize the underlying system as periodic, chaotic or random. In the absence of noise, phase plane plots and return maps were the most sensitive detectors of chaotic and periodic processes. Spectral analysis could determine if a process was periodic or quasiperiodic, but could not distinguish between chaotic and random signals. Correlation dimension was useful to determine the overall complexity of a signal, but could not be used in isolation to identify a chaotic process. Noise at any level effaced the structure of the phase plane plot. Return maps were relatively immune to noise at levels of up to 5%. Spectral analysis and correlation dimension were insensitive to noise. Accordingly, we recommend that unknown signals be subjected to all of the techniques to increase the accuracy of identification of the underlying process. Based on these data, we conclude that no single test is sufficiently sensitive or specific to categorize an unknown signal as chaotic.     

Radial basis function neural networks for the characterization of heart rate variability dynamics

This study introduces new neural network based methods for the assessment of the dynamics of the heart rate variability (HRV) signal. The heart rate regulation is assessed as a dynamical system operating in chaotic regimes. Radial-basis function (RBF) networks are applied as a tool for learning and predicting the HRV dynamics. HRV signals are analyzed from normal subjects before and after pharmacological autonomic nervous system (ANS) blockade and from diabetic patients with dysfunctional ANS. The heart rate of normal subjects presents notable predictability. The prediction error is minimized, in fewer degrees of freedom, in the case of diabetic patients. However, for the case of pharmacological ANS blockade, although correlation dimension approaches indicate significant reduction in complexity, the RBF networks fail to reconstruct adequately the underlying dynamics. The transient attributes of the HRV dynamics under the pharmacological disturbance is elucidated as the explanation for the prediction inability.     

Seizure characterisation using frequency-dependent multivariate dynamics

The characterisation of epileptic seizures assists in the design of targeted pharmaceutical seizure prevention techniques and pre-surgical evaluations. In this paper, we expand on the recent use of multivariate techniques to study the cross-correlation dynamics between electroencephalographic (EEG) channels. The maximum overlap discrete wavelet transform (MODWT) is applied in order to separate the EEG channels into their underlying frequencies. The dynamics of the cross-correlation matrix between channels, at each frequency, are then analysed in terms of the eigenspectrum. By examination of the eigenspectrum, we show that it is possible to identify frequency-dependent changes in the correlation structure between channels which may be indicative of seizure activity.The technique is applied to EEG epileptiform data and the results indicate that the correlation dynamics vary over time and frequency, with larger correlations between channels at high frequencies. Additionally, a redistribution of wavelet energy is found, with increased fractional energy demonstrating the relative importance of high frequencies during seizures. Dynamical changes also occur in both correlation and energy at lower frequencies during seizures, suggesting that monitoring frequency-dependent correlation structure can characterise changes in EEG signals during these. Future work will involve the study of other large eigenvalues and inter-frequency correlations to determine additional seizure characteristics.     

Wavelet Analysis as a Tool for Investigating Movement-Related Cortical Oscillations in EEG-fMRI Coregistration

Electroencephalography combined with functional magnetic resonance imaging (EEG-fMRI) identifies blood oxygenation level dependent (BOLD) signal changes associated with physiological and pathological EEG events. In this study we used EEG-fMRI to determine the possible correlation between topographical movement-related EEG changes in brain oscillatory activity recorded from EEG electrodes over the scalp and fMRI cortical responses in motor areas during finger movement. Thirty-two channels of EEG were recorded in 12 subjects during eyes-closed condition inside a three T magnetic resonance (MR) scanner using an MR-compatible EEG recording system. Off-line MRI artifact subtraction software was applied to obtain continuous EEG data during fMRI acquisition. For EEG data analysis we used a time–frequency approach to measure time by varying the energy in a signal at a given frequency band by the convolution of the EEG signal with a wavelet family in the alpha and beta bands. The correlation between the BOLD signal associated with the EEG regressor provides that sensory motor region is a source of the EEG. We conclude that combined EEG-fMRI can be used to investigate movement-related oscillations of the human brain inside an MRI scanner and wavelet analysis adds further details on the EEG changes. The movement-related changes in the EEG signals are useful to identify the brain activation sources responsible for BOLD-signal changes.     

Amplitude suppression and chaos control in epileptic EEG signals

In this paper we have proposed a novel amplitude suppression algorithm for EEG signals collected during epileptic seizure. Then we have proposed a measure of chaoticity for a chaotic signal, which is somewhat similar to measuring sensitive dependence on initial conditions by measuring Lyapunov exponent in a chaotic dynamical system. We have shown that with respect to this measure the amplitude suppression algorithm reduces chaoticity in a chaotic signal (EEG signal is chaotic). We have compared our measure with the estimated largest Lyapunov exponent measure by the largelyap function, which is similar to Wolf's algorithm. They fit closely for all but one of the cases. How the algorithm can help to improve patient specific dosage titration during vagus nerve stimulation therapy has been outlined.     

Nonlinear analysis of the EEG of schizophrenics with optimal embedding dimension

We estimated the correlation dimensions of EEGs in patients with schizophrenia to investigate the dynamical properties underlying the EEG. We employed a new method, proposed by Kennel et al. (Kennel MB, Brown R, Abarbanel HDI. Determining embedding dimension for phase-space reconstruction using a geometrical construction. Phys Rev A 1992;45:3403-11), to calculate the correlation dimension D2. That method determined the proper minimum embedding dimension by looking at the behaviour of nearest neighbours under a change in the embedding dimension d from d to d + 1. We demonstrated that for limited noisy data, our algorithm was strikingly faster and more accurate than previous ones. We estimated the D2 of EEGs from 16 channels in patients with schizophrenia according to DSM-IV whereas previous studies, which estimated chaoticity of EEG in schizophrenia, recorded EEG only in a limited number of channels. Schizophrenic patients had a lower correlation dimension in the left inferior frontal and anterior temporal regions compared with controls. Our finding of decreased left frontal and temporal chaotic activity in schizophrenics is in line with the findings of a hypofrontality and hypotemporality reported in previous clinical studies such as EEG, blood flow, brain MRI and positron emission tomography studies in schizophrenia. This result suggests that chaos analysis may be a useful tool in analysing EEG data to explore the brain mechanism of schizophrenia.     

Chaos–chaos transition of left hemisphere EEGs during standard tasks of Waterloo-Stanford Group Scale of hypnotic susceptibility

Abstract

A recent study, recurrence quantification analysis of EEG signals during standard tasks of Waterloo-Stanford Group Scale of hypnotic susceptibility investigated recurrence quantifiers (RQs) of hypnotic electroencephalograph (EEG) signals recorded after hypnotic induction while subjects were doing standard tasks of Waterloo-Stanford Group Scale (WSGS) of hypnotic susceptibility to distinguish subjects of different hypnotizability levels. Following the same analysis, the current study determines the capability of different RQs to distinguish subjects of low, medium and high hypnotizability level and studies the influence of hypnotizability level on underlying dynamic of tasks. Besides, EEG channels were sorted according to the number of their RQs, which differed significantly among subjects of different hypnotizability levels. Another valuable result was determination of major brain regions in observing significant differences in various task types (ideomotors, hallucination, challenge and memory).     

Gripping-force identification using EEG and phase-demodulation approach

In this paper we investigate the fuzzy identification of brain-code during simple gripping-force control tasks. Since the synchronized oscillatory activity and the phase dynamics between the brain areas are two important mechanisms in the brain’s function and information transfer, we decided to examine whether it is possible to extract the encoded information from the EEG signals using the phase-demodulation approach. The EEG was measured during the performance of different visuomotor tasks and the information we were trying to decode was the gripping force as applied by the subjects. The study revealed that it is possible, by using simple beta-rhythm filtering, phase demodulation, principal component analysis and a fuzzy model, to estimate the gripping-force response by using EEG signals as the inputs for the proposed model. The presented study has shown that even though EEG signals represent a superposition of all the active neurons, it is still possible to decode some information about the current activity of the brain centers. Furthermore, the cross-validation showed that the information about the gripping force is encoded in a very similar way for all the examined subjects. Thus, the phase shifts of the EEG signals seem to have a key role during activity and information transfer in the brain, while the phase-demodulation method proved to be a crucial step in the signal processing.     

Electrophysiological correlates of human intrasaccadic processing

Visual discrimination performance is thought to be suppressed during saccades in order to contribute to space constancy. However, under certain experimental conditions, visual inhibition may not take place, suggesting a more complex underlying mechanism. We tested the discrimination ability of 20 healthy subjects during visually guided horizontal saccades and recorded simultaneously the evoked brain activity from 30 channels over occipital, parietal, and temporal areas. During the execution of saccadic eye movements, visual stimuli were presented for 30 ms. In order to prevent retinal afterimages, stimuli were followed by a visual mask. In a control condition, the same stimuli were presented with stationary eyes. Electro-oculogram (EOG) and electroencephalogram (EEG) signals were recorded continuously together with information about the stimuli and the subject's response. Evoked potentials were computed offline, and component latency, field strength (global field power), and topography were compared between conditions. During saccades, subjects showed only slightly reduced discrimination performance which remained very high above the chance level; thus, there was no evidence for strong saccadic suppression with the supra-threshold stimuli employed. However, the cortical activation patterns exhibited large alteration when a physically identical stimulus was presented during the eye movement: around 130 ms latency, field strength was significantly smaller than when stationary targets were processed, and scalp topography was also different. These effects on evoked field distributions may be attributed to neural interactions of an efference copy signal (linked to the oculomotor command) with the afferent excitation following the visual stimulus.     

Correlations between brain electrical activities of two spatially separated human subjects

Six channels electroencephalogram (EEG) were recorded simultaneously from pairs of separated human subjects in two acoustically and electromagnetically shielded rooms. While brain electric responses to visual pattern-reversal stimuli were elicited in one subject, the other subject relaxed without stimulation. EEGs of both subjects were averaged at times of stimulus onset, effective voltage of the averaged signals was computed within a running window, and expressed as ratio (Q) to the effective voltage of averaged EEG signal from non-stimulation periods. These ratios in non-stimulated subjects at the latency of the maximum response in stimulated subjects were analysed. Significant departures of Q ratios from reference distributions, based on baseline EEG in non-stimulation periods, were found in most non-stimulated subjects. The results indicate that correlations between brain activities of two separated subjects may occur, although no biophysical mechanism is known.     

基于C-FFuzzyEn的神经电生理信号同步性分析

基于传统互模糊熵,结合分数阶微积分提出分数阶互模糊熵（C-FFuzzyEn）,并基于该算法分析混沌耦合系统的同步性,进行健康对照者和癫痫患者不同脑区脑电信号的耦合性对比。结果表明,与传统互模糊熵相比,C-FFuzzyEn提高了不同耦合度模型的区分能力;与健康对照者相比,癫痫患者在癫痫发作时不同通道脑电信号之间C-FFuzzyEn较小,与癫痫发作时各神经元同步放电现象相吻合;相比互模糊熵,C-FFuzzyEn区分健康对照者与癫痫患者脑区之间脑电信号同步性的效果更好。C-FFuzzyEn可应用于脑电信号等神经电生理信号的同步性分析。     

HAI实验中EEG信号的非线性动力学研究

采用一维时间序列相空间重构技术和混沌的定量判据，对缺氧窒息而引起的中枢神经损伤（Hypoxic-Asphyxic Injury，HAl）实验中仔猪的脑电（Electroencephalogram，EEG）信号进行了分析与计算。通过对生理和损伤状态下仔猪EEG信号的相图、功率谱、关联维数和Lyapunov指数的对比研究，得出如下结论：（1）EEG的相图、功率谱、关联维数和Lyapunov指数反映了大脑的总体动态特征，它们可作为一种定量研究EEG的新方法进行脑损伤的早期诊断；（2）在正常的生理状态下EEG是混沌的，而在损伤状态下则趋于有序。     

Classification of low-density EEG for epileptic seizures by energy and fractal features based on EMD

We are here to present a new method for the classification of epileptic seizures from electroencephalogram (EEG) signals. It consists of applying empirical mode decomposition (EMD) to extract the most relevant intrinsic mode functions (IMFs) and subsequent computation of the Teager and instantaneous energy, Higuchi and Petrosian fractal dimension, and detrended fluctuation analysis (DFA) for each IMF. We validated the method using a public dataset of 24 subjects with EEG signals from 22 channels and showed that it is possible to classify the epileptic seizures, even with segments of six seconds and a smaller number of channels (e.g., an accuracy of 0.93 using five channels). We were able to create a general machine-learning-based model to detect epileptic seizures of new subjects using epileptic-seizure data from various subjects, after reducing the number of instances, based on the k-means algorithm.     

Identification of the phase code in an EEG during gripping-force tasks: a possible alternative approach to the development of the brain-computer interfaces

BACKGROUND: The subject of brain-computer interfaces (BCIs) represents a vast and still mainly undiscovered land, but perhaps the most interesting part of BCIs is trying to understand the information exchange and coding in the brain itself. According to some recent reports, the phase characteristics of the signals play an important role in the information transfer and coding. The mechanism of phase shifts, regarding the information processing, is also known as the phase coding of information. OBJECTIVE: The authors would like to show that electroencephalographic (EEG) signals, measured during the performance of different gripping-force control tasks, carry enough information for the successful prediction of the gripping force, as applied by the subjects, when using a methodology based on the phase demodulation of EEG data. Since the presented methodology is non-invasive it could be used as an alternative approach for the development of BCIs. MATERIALS AND METHODS: In order to predict the gripping force from the EEG signals we used a methodology that uses subsequent signal processing methods: simplistic filtering methods, for extracting the appropriate brain rhythm; principal component analysis, for achieving the linear independence and detecting the source of the signal; and the phase-demodulation method, for extracting the phase-coded information about the gripping force. A fuzzy inference system is then used to predict the gripping force from the processed EEG data. RESULTS: The proposed methodology has clearly demonstrated that EEG signals carry enough information for a successful prediction of the subject's performance. Moreover, a cross-validation showed that information about the gripping force is encoded in a very similar way between the subjects tested. As for the development of BCIs, considering the computational time to pre-process the data and train the fuzzy model, a real-time online analysis would be possible if the real-time non-causal limitations of the methodology could be overcome. CONCLUSION: The study has shown that phase coding in the human brain is a possible mechanism for information coding or transfer during visuo-motor tasks, while the phase-coded content about the gripping forces can be successfully extracted using the phase-demodulation approach. Since the methodology has proven to be appropriate for the case of this study it could also be used as an alternative approach for the development of BCIs for similar tasks.     

A multivariate investigation of visual word,face, and ensemble processing: Perspectives from EEG-based decoding and feature selection

Recent investigations have focused on the spatiotemporal dynamics of visual recognition by appealing to pattern analysis of EEG signals. While this work has established the ability to decode identity-level information (such as the identity of a face or of a word) from neural signals, much less is known about the precise nature of the signals that support such feats, their robustness across visual categories, or their consistency across human participants. Here, we address these questions through the use of EEG-based decoding and multivariate feature selection as applied to three visual categories: words, faces and face ensembles (i.e., crowds of faces). Specifically, we use recursive feature elimination to estimate the diagnosticity of time and frequency-based EEG features for identity-level decoding across three datasets targeting each of the three categories. We then relate feature diagnosticity across categories and across participants while, also, aiming to increase decoding performance and reliability. Our investigation shows that word and face processing are similar in their reliance on spatiotemporal information provided by occipitotemporal channels. In contrast, ensemble processing appears to also rely on central channels and exhibits a similar profile with word processing in the frequency domain. Further, we find that feature diagnosticity is stable across participants and is even capable of supporting cross-participant feature selection, as demonstrated by systematic boosts in decoding accuracy and feature reduction. Thus, our investigation sheds new light on the nature and the structure of the information underlying identity-level visual processing as well as on its generality across categories and participants.