Time-Dependent Statistical and Correlation Properties of Neural Signals during Handwriting

To elucidate the cortical control of handwriting, we examined time-dependent statistical and correlational properties of simultaneously recorded 64-channel electroencephalograms (EEGs) and electromyograms (EMGs) of intrinsic hand muscles. We introduced a statistical method, which offered advantages compared to conventional coherence methods. In contrast to coherence methods, which operate in the frequency domain, our method enabled us to study the functional association between different neural regions in the time domain. In our experiments, subjects performed about 400 stereotypical trials during which they wrote a single character. These trials provided time-dependent EMG and EEG data capturing different handwriting epochs. The set of trials was treated as a statistical ensemble, and time-dependent correlation functions between neural signals were computed by averaging over that ensemble. We found that trial-to-trial variability of both the EMGs and EEGs was well described by a log-normal distribution with time-dependent parameters, which was clearly distinguished from the normal (Gaussian) distribution. We found strong and long-lasting EMG/EMG correlations, whereas EEG/EEG correlations, which were also quite strong, were short-lived with a characteristic correlation durations on the order of 100 ms or less. Our computations of correlation functions were restricted to the spectral range (13–30 Hz) of EEG signals where we found the strongest effects related to handwriting. Although, all subjects involved in our experiments were right-hand writers, we observed a clear symmetry between left and right motor areas: inter-channel correlations were strong if both channels were located over the left or right hemispheres, and 2–3 times weaker if the EEG channels were located over different hemispheres. Although we observed synchronized changes in the mean energies of EEG and EMG signals, we found that EEG/EMG correlations were much weaker than EEG/EEG and EMG/EMG correlations. The absence of strong correlations between EMG and EEG signals indicates that (i) a large fraction of the EEG signal includes electrical activity unrelated to low-level motor variability; (ii) neural processing of cortically-derived signals by spinal circuitry may reduce the correlation between EEG and EMG signals.     

Ambulatory cassette EEG in epilepsy diagnosis

The electroencephalographic evaluation of patients with possible or proven epilepsy is no longer limited to routine laboratory EEGs or intensive inpatient monitoring. Expanded temporal sampling of the EEG, which increases the probability of documenting, characterizing, and quantitating the electrographic manifestations of these illnesses, is now available on a portable, outpatient, and less cumbersome inpatient basis by means of ambulatory cassette recordings. The technological advances which have made this technique feasible include small multi-channel tape recorders, miniature preamplifiers, and rapid video/audio playback units. New designs in montages and analysis techniques have made the procedure practical. Clinical series and controlled trials have confirmed the usefulness of cassette EEG monitoring in the evaluation of epilepsy and a wide range of other paroxysmal neurologic disorders. Ambulatory EEG diagnostic yields have been shown to be superior to routine laboratory studies and nearly as good as inpatient telemetry evaluations. The role of cassette recordings in clinical electroencephalography continues to be defined as new applications are established.     

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.     

Relationship of spatial synchronization of θ-band bioelectric potentials of human EEG with the success of visual spatial task performance

Students (46 young men) were asked to memorize and reproduce the order and location (on a computer monitor) of signals. Two groups of subjects were formed according to how accurately they reproduced the true location and order of the signals. Subjects of the first group, in contrast to the second group, reproduced the signal location and order at a high accuracy and with few mistakes even in the first trials. EEGs were recorded prior to the test, during memorizing, and after completion of the task. In the initial state and after completion of the task, the two groups did not differ in the EEG θ-rhythm. During memorizing the signal sequence, an increase in the coefficients of coherence was recorded in the EEG θ band of the right brain hemisphere of the first-group of students; this was not characteristic of the second group. Three systems of connection with the foci of activity were determined in the right occipital, central, and frontal cortical areas, where the coherence of the EEG θ band was significantly higher during memorizing in the students that had exhibited a high accuracy of signal reproduction. Since the right hemisphere deals mainly with the perception of visual spatial information and it is more active in processing nonverbal and stereotyped signals, we have suggested that the students of two groups employed different strategies in solving the task during memorizing.     

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.     

Context-dependent EEG spectral characteristics during performance of tasks

Spatial and frequency EEG characteristics of two groups of healthy adult subjects were examined in two series of experiments, which differed in conditions of the second cognitive task in a trial. The first task was the same in the two series: subjects had to evaluate size relationship between two closely spaced circles. The second task successively presented in trials of the first series consisted in the recognition of words/pseudowords, and in the second series, subjects had to localize a target letter in a matrix. It was assumed that the cognitive performance in the first series predominantly involved the ventral visual system, whereas during task performance in the second series, predominant involvement of the ventral and dorsal visual systems alternated. Multichannel EEG fragments recorded prior to the presentation of the task pairs were analyzed. Analysis of variance of the EEG spectral power revealed the generalized significant effect of the factor of the second task in the pair for delta band and lower beta subband, the power being higher in the first series. Factor brain hemisphere had a significant effect for the alpha band in the occipital area, the spectral power being lower in the left hemisphere for both experimental series. The task x hemisphere interaction was significant in the temporal cortical areas for the EEG power in alpha2 band, i.e., the predominant involvement of the ventral visual system was associated with stronger asymmetry of alpha2 rhythm and lower spectral power in this band in the left temporal area. Thus, the character of the forthcoming cognitive activity was shown to be reflected in spatio-frequency characteristics of the preceding EEG.     

Aging-related changes of EEG synchronization during a visual working memory task

Differences of EEG synchronization between normal old and young people during a working memory (WM) task were investigated. The synchronization likelihood (SL) is a novel method to assessed synchronization in multivariate time series for non-stationary systems. To evaluate this method to study the mechanisms of WM, we calculated the SL values in brain electrical activity for both resting state and task state. EEG signals were recorded from 14 young adults and 12 old adults during two different states, respectively. SL was used to measure EEG synchronization between 19 electrodes in delta, theta, alpha1, alpha2 and beta frequency bands. Bad task performance and significantly decreased EEG synchronization were found in old group compared to young group in alpha1, alpha2 and beta frequency bands during the WM task. Moreover, significantly decreased EEG synchronization in beta band in the elder was also detected during the resting state. The findings suggested that reduced EEG synchronization may be one of causes for WM capacity decline along with healthy aging.     

A model for dual channel segmentation of the EEG signal

Dual channel segmentation of the EEG signal has been developed. The purpose was to divide the signals into segments, according to information common for the two channels. The criterion for segmentation was based on the changes in the cross-spectrum of the two signals. It has been shown theoretically, as well as by simulation studies and by analysis of real EEG data that this method is sensitive to changes common for both channels, whereas segmentation does not occur as a result of changes in each channel separately.     

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.     

急性轻中度缺氧EEG信号的复杂度研究

建立了一个急性高空缺氧实验模型,记录了四种不同高度条件下从缺氧前(正常呼吸)到缺氧后30分钟时的EEG,分析了其复杂度。发现缺氧引起复杂度明显变化,随时间和高度增加,一定程度缺氧可使EEG复杂度低于正常。表明EEG复杂度对脑缺氧较为敏感,可用于缺氧程度进行评估,有望成为临床诊断的一个指标。     

Brain activation levels and the significance of the stage B(I) indices in neuroses]

Two forms of neuroses--neurasthenia and hysteria--show statistically definitive differences in the EEG patterns. In the initial EEGs of neurasthenic patients, as a rule, more or less marked alpha-rhythm is preserved, whereas the EEG in hysteria in most cases consists of low amplitude fast frequencies ("flat" EEG) and only in 30-35% cases short episodes of alpha-waves can be recorded. In the course of medical treatment the index alpha often increases and the EEG gradually obtains normal features. One of the most favourable signs of convalescence is the renewal of the ability to develop the phase of drowsiness with the stage B in the EEG, during which the outburst of alpha-waves is recorded as a reaction to stimulation.     

Blind source separation,wavelet denoising and discriminant analysis for EEG artefacts and noise cancelling

This paper proposes an automatic method for artefact removal and noise elimination from scalp electroencephalogram recordings (EEG). The method is based on blind source separation (BSS) and supervised classification and proposes a combination of classical and news features and classes to improve artefact elimination (ocular, high frequency muscle and ECG artefacts). The role of a supplementary step of wavelet denoising (WD) is explored and the interactions between BSS, denoising and classification are analyzed. The results are validated on simulated signals by quantitative evaluation criteria and on real EEG by medical expertise. The proposed methodology successfully rejected a good percentage of artefacts and noise, while preserving almost all the cerebral activity. The “denoised artefact-free” EEG presents a very good improvement compared with recorded raw EEG: 96% of the EEGs are easier to interpret.     

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.     

A self-paced brain interface system that uses movement related potentials and changes in the power of brain rhythms

Movement execution results in the simultaneous generation of movement-related potentials (MRP) as well as changes in the power of Mu and Beta rhythms. This paper proposes a new self-paced multi-channel BI that combines features extracted from MRPs and from changes in the power of Mu and Beta rhythms. We developed a new algorithm to classify the high-dimensional feature space. It uses a two-stage multiple-classifier system (MCS). First, an MCS classifies each neurological phenomenon separately using the information extracted from specific EEG channels (EEG channels are selected by a genetic algorithm). In the second stage, another MCS combines the outputs of MCSs developed in the first stage. Analysis of the data of four able-bodied subjects shows the superior performance of the proposed algorithm compared with a scheme where the features were all combined in a single feature vector and then classified.     

Reduction of the number of recorded EEG channels for routine monitoring in the intensive care unit]

Monitoring patients in the intensive care unit with the aid of the conventional electroencephalogram employing a large number of recording channels is rather difficult, and can be laborious. This imposes limits on the routine application of this method. To investigate the possibility of developing a new monitoring device for easier application in the ICU, we aimed to establish whether the relevant information provided by a multi-channel EEG could be found in a subgroup of channels, thus reducing the number of channels required. Preferably those channels should be identified for use which are least contaminated by artefacts under routine conditions in the ICU. A total of 150 EEG recordings from the intensive care unit were inspected visually for the presence of artefacts. The derivations C3-P3 and C4-P4 proved to be least contaminated, at 35% and 39%, respectively. In these derivations visual assessment of the EEG was found to be impossible due to artefacts in only 4 and 5%, of all cases, respectively. A data set comprising 52 EEG segments with the fewest possible artefacts, was analysed using time series methods. On the basis of multivariate autoregressive processes, a measure was derived which describes the loss of information associated with a reduction in the number of EEG channels. The computation of the information loss for several channel combinations revealed that the derivations F3-C3, C3-P3 and A1-Cz represent a good compromise between information content, number of channels and frequency of artefacts. Practical experience shows that, at least for the control of sedation, a further reduction to a single channel should be possible.(ABSTRACT TRUNCATED AT 250 WORDS)     

Improving N1 classification by grouping EEG trials with phases of pre-stimulus EEG oscillations

A reactive brain-computer interface using electroencephalography (EEG) relies on the classification of evoked ERP responses. As the trial-to-trial variation is evitable in EEG signals, it is a challenge to capture the consistent classification features distribution. Clustering EEG trials with similar features and utilizing a specific classifier adjusted to each cluster can improve EEG classification. In this paper, instead of measuring the similarity of ERP features, the brain states during image stimuli presentation that evoked N1 responses were used to group EEG trials. The correlation between momentary phases of pre-stimulus EEG oscillations and N1 amplitudes was analyzed. The results demonstrated that the phases of time–frequency points about 5.3 Hz and 0.3 s before the stimulus onset have significant effect on the ERP classification accuracy. Our findings revealed that N1 components in ERP fluctuated with momentary phases of EEG. We also further studied the influence of pre-stimulus momentary phases on classification of N1 features. Results showed that linear classifiers demonstrated outstanding classification performance when training and testing trials have close momentary phases. Therefore, this gave us a new direction to improve EEG classification by grouping EEG trials with similar pre-stimulus phases and using each to train unit classifiers respectively.     

Classification of Motor Imagery EEG Based on Time-Domain and Frequency-Domain Dual-Stream Convolutional Neural Network

Background and objectiveAn important task of the brain-computer interface (BCI) of motor imagery is to extract effective time-domain features, frequency-domain features or time-frequency domain features from the raw electroencephalogram (EEG) signals for classification of motor imagery. However, choosing an appropriate method to combine time domain and frequency domain features to improve the performance of motor imagery recognition is still a research hotspot.MethodsIn order to fully extract and utilize the time-domain and frequency-domain features of EEG in classification tasks, this paper proposed a novel dual-stream convolutional neural network (DCNN), which can use time domain signal and frequency domain signal as the inputs, and the extracted time-domain features and frequency-domain features are fused by linear weighting for classification training. Furthermore, the weight can be learned by the DCNN automatically.ResultsThe experiments based on BCI competition II dataset III and BCI competition IV dataset 2a showed that the model proposed by this study has better performance than other conventional methods. The model used time-frequency signal as the inputs had better performance than the model only used time-domain signals or frequency-domain signals. The accuracy of classification was improved for each subject compared with the models only used one signals as the inputs.ConclusionsFurther analysis shown that the fusion weight of different subject is specifically, adjusting the weight coefficient automatically is helpful to improve the classification accuracy.     

Hyperactive individuals as young adults: current and longitudinal electroencephalographic evaluation and its relation to outcome.

In a 10-year follow-up study electroencephalograms (EEGs) of 31 hyperactive and 27 matched control subjects of mean ages 19.17 and 18.59 years respectively showed no significant differences in any of the features assessed. Sequential EEGs, available for only the hyperactive subjects, suggested that a much greater proportion were normal at the 10-year follow-up assessment than at the 5-year follow-up assessment and that the normalization tended to take place mainly in the second 5-year period. This supports the hypothesis that EEG abnormalities of hyperactive persons are those of an immature pattern that tends to normalize with age. Correlation between EEG findings at the 10-year follow-up assessment and global outcome measures was not significant. Initial and 5-year EEGs also failed to predict global outcome at the 10-year follow-up assessment.     

Canonical bicoherence analysis of dynamic EEG data

Bicoherence has been used in quantifying quadratic phase coupling (QPC) in electroencephalography (EEG) signals. However, for high-dimensional EEG signals, the calculations of traditional auto– and cross–bicoherences of signals from multiple electrodes are computationally very expensive. This has been compounded by the recognition of the non-stationary character of EEG signals. This paper introduces a new approach, the time-varying canonical bicoherence (CBC) by short-time weighted Fourier transforms, for analyzing QPC nonlinearities of dynamic EEG signals. This new method shows both computational efficiency and simple interpretation of estimated canonical bicoherences. The canonical bicoherence analysis of EEG records, during a human visual stimulus-driven cognitive process, put into evidence of quadratic phase couplings of Beta waves and Delta waves in the frontal regions.