Classification of the adolescent EEG by the spectral and segmental characteristics for normals

The 16-channel EEG records of 45 adolescents with schizophrenia and 39 healthy adolescents were subjected to statistical combinatorial analysis of 160 elementary EEG characteristics (6 spectral and 4 segmental EEG characteristics for a channel). Employing pattern recognition algorithm "Kora-n", a list of 37 combined EEG patterns was compiled. This list characterized with a minimal error the EEG of healthy adolescents in such a way that none of these characters featured the EEG of adolescents with schizophrenia. Analysis of this list of EEG characteristics suggests that the contrast between EEG of healthy and ill adolescents is the sharpest in the F4, Cz, T3 and O1 derivations. Compared to EEG samples of schizophrenic subjects, EEGs of healthy subjects exhibit lower levels of delta and theta activity mainly in the frontal and temporal regions of the cortex and higher level of alpha activity predominantly in the occipital region. Applicability of the list of EEG patterns for diagnostics of schizophrenia-type disorders of adolescents is discussed.     

Feature extraction and recognition of epileptiform activity in EEG by combining PCA with ApEn

This paper proposes a new method for feature extraction and recognition of epileptiform activity in EEG signals. The method improves feature extraction speed of epileptiform activity without reducing recognition rate. Firstly, Principal component analysis (PCA) is applied to the original EEG for dimension reduction and to the decorrelation of epileptic EEG and normal EEG. Then discrete wavelet transform (DWT) combined with approximate entropy (ApEn) is performed on epileptic EEG and normal EEG, respectively. At last, Neyman–Pearson criteria are applied to classify epileptic EEG and normal ones. The main procedure is that the principle component of EEG after PCA is decomposed into several sub-band signals using DWT, and ApEn algorithm is applied to the sub-band signals at different wavelet scales. Distinct difference is found between the ApEn values of epileptic and normal EEG. The method allows recognition of epileptiform activities and discriminates them from the normal EEG. The algorithm performs well at epileptiform activity recognition in the clinic EEG data and offers a flexible tool that is intended to be generalized to the simultaneous recognition of many waveforms in EEG.     

基于脑电信号识别采集的假肢控制系统

本文介绍了脑电信号（EEG）的模式识别和步骤,分析了EEG采集领域的发展和医学原理。通过研究脑电信号和假肢运动的联系,总结脑电控制假肢的可行性结论。设计出从头皮电极到模／数转换器的基于脑电信号识别采集的假肢控制系统,能够满足脑电假肢的各种要求。     

Effects of EEG of the osmotic opening of the blood-brain barrier in rats

EEG activity was recorded in rats submitted to osmotic opening of the BBB by intracarotid mannitol infusion.This procedure produced an immediate short-lasting depression of the EEG and a tardive paroxysmal EEG activity. Both these phenomena were more relevant on the ipsilateral hemisphere. In some instances a tonico-clonic seizure was recorded.Pre-treatment with diazepam abolished the occurrence of the tardive EEG and behavioral modifications.In accord with previous findings, focal seizure activity is likely to be responsible for the metabolic abnormalities associated with osmotic opening of the BBB. This preparation therefore produces in the brain unphysiological states in respect to local metabolism and electrical function.     

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.     

基于平稳小波变换的EEG视觉的信号修正

眼球运动和眨眼会在眼球周围产生电信号,这种电信号的存在直接影响到对EEG信号的分析特征提取及EEG模式的分类等研究。本文提出了一种基于小波阈值滤噪方法来修正EEG信号中出现的视觉伪信号(OA)。这种用于EEG视觉伪信号处理的小波方法的实现过程如下：1)用平稳小波变换(SWT)对原始EEG信号进行处理;2)设置低频带信号的系数阈值;3)对滤噪后的信号进行重构。实验结果表明这种方法同时适用于眨眼和眼球运动产生的伪信号。最后,通过对采集的信号处理前后做了对比,说明其有效性。     

Maintenance and generalization of 40-Hz EEG biofeedback effects

Maintenance of conditioning of 40-Hz EEG activity was investigated in six adults 1 to 3 years after they had experienced biofeedback training to increase 40-Hz EEG. Subjects were first retrained to alternately increase and suppress 40-Hz EEG. All six subjects achieved a preset performance criterion in 16–20 minutes. Five of these subjects also subsequently demonstrated significant control of 40-Hz EEG without feedback. The sixth subject did not demonstrate control after 76 minutes and four sessions of attempted retraining with feedback. Transfer of 40-Hz EEG control to a problem-solving task was tested in all subjects in a final session. Cognitive test items were presented and subjects were instructed to alternately increase and suppress 40-Hz EEG while solving the problems. Rates of 40-Hz EEG in suppression periods during problem solving were significantly greater than during suppression periods without problems. No significant differences in problem-solving performance were found comparing 40-Hz increase and suppression periods. This study supports previous research suggesting an association between 40-Hz EEG and mental activity, and suggests methods for further study of transfer of EEG biofeedback effects.     

A stochastic limit cycle oscillator model of the EEG

We present an empirical model of the electroencephalogram (EEG) signal based on the construction of a stochastic limit cycle oscillator using Itô calculus. This formulation, where the noise influences actually interact with the dynamics, is substantially different from the usual definition of measurement noise. Analysis of model data is compared with actual EEG data using both traditional methods and modern techniques from nonlinear time series analysis. The model demonstrates visually displayed patterns and statistics that are similar to actual EEG data. In addition, the nonlinear mechanisms underlying the dynamics of the model do not manifest themselves in nonlinear time series analysis, paralleling the situation with real, non-pathological EEG data. This modeling exercise suggests that the EEG is optimally described by stochastic limit cycle behavior.     

Amplitude-frequency modulation of electroencephalogram associated with rhythmic movements

Evoked, motor and final potentials and some other EEG phenomena are suggested as additional components accompanying movements, external stimuli, imagination etc. Obviously, the EEG reactions are not restricted to them. On the basis of the method of synchronic averaging a way for detecting the amplitude-frequency modulation (AFM) related to the repeated movements is proposed. This method permitted to reliably single out the EEG effects which sometimes were detected by visual analysis. As the experiments showed the depth of AFM EEG accompanying the movements was about 3-6% (in this case spontaneous AFM plays the role of noise and equals 50% or more). Relations between changes in AFM for EEG recorded from various points, as well as for EEG rhythms were investigated.     

Developmental features of the brain bioelectrical activity in children with remote consequences of a perinatal lesion of the CNS. II. EEG typology in normal and mental disorders

This research represents EEG - investigation by children with remote consequences of perinatal CNS pathology. Its described the different EEG types in normal and mental disorders in children. Its showed a early EEG - markers of abnormal ontogenesis in longitudinal study. The data obtained gives to prevent a negative dynamic of mental and speech development (learning disability, motor alalia, autism).     

Analysis of EEG signals during epileptic and alcoholic states using AR modeling techniques

Electroencephalogram (EEG) analysis remains problematic due to limited understanding of the signal origin, which leads to the difficulty of designing evaluation methods. In spite of these shortcomings, the EEG is a valuable tool in the evaluation of some neurological disorders as well as in the evaluation of overall cerebral activity. In most studies, which use quantitative EEG analysis, the properties of measured EEG are computed by applying power spectral density (PSD) estimation for selected representative EEG samples. The sample for which the PSD is calculated is assumed to be stationary. This work deals with a comparative study of the PSD obtained from normal, epileptic and alcoholic EEG signals. The power density spectra were calculated using fast Fourier transform (FFT) by Welch's method, auto regressive (AR) method by Yule–Walker and Burg's method. The results are tabulated for these different classes of EEG signals.     

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

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

Period-peak analysis of the EEG with microprocessor applications

This paper describes the development and evaluation of a period-peak algorithm for background analysis of the clinical electroencephalogram (EEG). The procedure is a time-domain method which is harmonious with manual interpretation of the EEG tracing. Conceptually the algorithm functions in 2 modes. Major counts are detected by successsive baseline crossings in the period analysis mode. Presence of superimposed activity between major-counts induces a transition to the peak-detection mode. In this manner, period-peak analysis is capable of detecting the simultaneity of slow base-waves and relatively fast superimposed activity in the EEG. Preliminary studies have been conducted in which the analysis results of this procedure were compared to those of other EEG algorithms. In general, the period-peak algorithm offered less bias towards either end of the EEG spectrum. Subsequent to testing of a FORTRAN version, the period-peak algorithm has been implemented in assembly language on a dedicated microprocessor system for on-line analysis of EEG data.     

Theoretical analysis on relationship between the neural activity and the EEG

Firstly, a collective oscillation mode of the neural activity is derived from the neural network system by using the multicompartment equation and the projection operator technique. This technique takes into account higher order interactions among neurons. The solution of the equation gives a chain structure with an infinite number of circuit loops in which each of them is only composed of four neurons. The obtained eigenvalues are quite similar to the spectrum of frequencies of the EEG. Secondly, the time-dependent behavior of the observed EEG is simulated by starting from the elementary process of action potential trains of neurons, which includes the effect of the collective oscillation mode mentioned above. This gives a comprehensive derivation of the EEG from the neural activity of action potentials. The simulation assumes that information of the action potential trains can be transmitted to the EEG through the intermediate states of the postsynaptic potential trains and the slow waves. The paper reports that a slightly modulated activity of a relatively small amount of neurons can cause a strong influence on the shape of the global EEG and that the calculated results reproduce the characteristic features of the EEG in a rat such as the theta rhythm, the spindle wave and the arousal wave.     

Hidden pattern discovery on event related potential EEG signals

EEG signals are important to capture brain disorders. They are useful for analyzing the cognitive activity of the brain and diagnosing types of seizure and potential mental health problems. The Event Related Potential can be measured through the EEG signal. However, it is always difficult to interpret due to its low amplitude and sensitivity to changes of the mental activity. In this paper, we propose a novel approach to incrementally detect the pattern of this kind of EEG signal. This approach successfully summarizes the whole stream of the EEG signal by finding the correlations across the electrodes and discriminates the signals corresponding to various tasks into different patterns. It is also able to detect the transition period between different EEG signals and identify the electrodes which contribute the most to these signals. The experimental results show that the proposed method allows the significant meaning of the EEG signal to be obtained from the extracted pattern.     

Physiological origins and functional correlates of EEG rhythmic activities: Implications for self-regulation

Recent neurophysiological findings in relation to thalamocortical mechanisms for sensory processing, together with established anatomical and expanding functional evidence, have provided a rational theoretical framework for the interpretation of normal and abnormal EEG rhythmic activities. This perspective is integrated here with earlier animal studies which were the foundation for many current applications of EEG self-regulation as a clinical tool. Basic evidence concerning the origins, frequency modulation, and functional significance of normal EEG rhythmic activities is reviewed here in an effort to provide guiding principles for the interpretation of clinical abnormalities and their remediation with EEG feedback training.     

Cortical Source Analysis of High-Density EEG Recordings in Children

EEG is traditionally described as a neuroimaging technique with high temporal and low spatial resolution. Recent advances in biophysical modelling and signal processing make it possible to exploit information from other imaging modalities like structural MRI that provide high spatial resolution to overcome this constraint¹. This is especially useful for investigations that require high resolution in the temporal as well as spatial domain. In addition, due to the easy application and low cost of EEG recordings, EEG is often the method of choice when working with populations, such as young children, that do not tolerate functional MRI scans well. However, in order to investigate which neural substrates are involved, anatomical information from structural MRI is still needed. Most EEG analysis packages work with standard head models that are based on adult anatomy. The accuracy of these models when used for children is limited², because the composition and spatial configuration of head tissues changes dramatically over development³. In the present paper, we provide an overview of our recent work in utilizing head models based on individual structural MRI scans or age specific head models to reconstruct the cortical generators of high density EEG. This article describes how EEG recordings are acquired, processed, and analyzed with pediatric populations at the London Baby Lab, including laboratory setup, task design, EEG preprocessing, MRI processing, and EEG channel level and source analysis.      

EEG applications for sport and performance

One approach to understanding processes that underlie skilled performing has been to study electrical brain activity using electroencephalography (EEG). A notorious problem with EEG is that genuine cerebral data is often contaminated by artifacts of non-cerebral origin. Unfortunately, such artifacts tend to be exacerbated when the subject is in motion, meaning that obtaining reliable data during exercise is inherently problematic. These problems may explain the limited number of studies using EEG as a methodological tool in the sports sciences. This paper discusses how empirical studies have generally tackled the problem of movement artifact by adopting alternative paradigms which avoid recording during actual physical exertion. Moreover, the specific challenges that motion presents to obtaining reliable EEG data are discussed along with practical and computational techniques to confront these challenges. Finally, as EEG recording in sports is often underpinned by a desire to optimise performance, a brief review of EEG-biofeedback and peak performance studies is also presented. A knowledge of practical aspects of EEG recording along with the advent of new technology and increasingly sophisticated processing models offer a promising approach to minimising, if perhaps not entirely circumventing, the problem of obtaining reliable EEG data during motion.     

Simulation of nonstationary EEG

In this paper we present a systematic method for generating simulations of nonstationary EEG. Such simulations are needed, for example, in the evaluation of tracking algorithms. First a state evolution process is simulated. The states are initially represented as segments of stationary autoregressive processes which are described with the corresponding predictor coefficients and prediction error variances. These parameters are then concatenated to give a piecewise time-invariant parameter evolution. The evolution is projected onto an appropriately selected set of smoothly time-varying functions. This projection is used to generate the final EEG simulation. As an example we use this method to simulate the EEG of a drowsy rat. This EEG can be described as toggling between two states that differ in the degree of synchronization of the activity-inducing neuron clusters. Received: 22 June 1994 / Accepted in revised form: 18 February 1997