Quantitative EEG analysis in clinical settings

Summary Quantitative EEG techniques are becoming more available. Eventually, all EEG will be digital. Various digital utility programs can help even now with storage and viewing the polygraph EEG itself. Techniques of frequency analysis, topographic mapping and discriminant functions are also available but have limited clinical use. Application as a monitoring tool and careful analysis of epileptic spikes have shown some promise but need further study to identify their proper clinical roles.Based in part on material presented at the Tenth Anniversary Meeting of the Japanese Society for Brain Electromagnetic Topography, Fukuoka, Japan, September, 1992.     

Dynamic brain mapping methodology and application

Brain mapping has opened important perspectives for the neurophysiological evaluation of patients, for the discrimination of drug effects on the brain and for the study of the relationship between the brain and behavior. Our Dynamic Brain Mapping System is the result of many years of EEG quantification. It was designed as a software-oriented system to favor the largest clinical application and simultaneously stimulate new research onjectives. Data collection and analysis procedures are critically important in brain mapping for a good understanding of the results. For clinical use, the maps should answer relevant EEG questions and be interpretable with the consolidated knowledge. Therefore, we have developed a new type of brain mapping technology which is called “Field blending interpolation” mapping offered together with the conventional technology with user-selectable interpolation algorithms. In addition to diagnosis, the use of computer-analyzed EEG and brain mapping can be instrumental in drug monitoring, drug selection and drug discriminations. Prospective studies are, however, required to validate the use of brain mapping in each of these new areas. Spatial analysis is the original goal of brain mapping. The development of a new data collection procedure and analysis will be instrumental in the determination of an adequate time and space resolution.     

Removing electroencephalographic artifacts by blind source separation

Eye movements, eye blinks, cardiac signals, muscle noise, and line noise present serious problems for electroencephalographic (EEG) interpretation and analysis when rejecting contaminated EEG segments results in an unacceptable data loss. Many methods have been proposed to remove artifacts from EEG recordings, especially those arising from eye movements and blinks. Often regression in the time or frequency domain is performed on parallel EEG and electrooculographic (EOG) recordings to derive parameters characterizing the appearance and spread of EOG artifacts in the EEG channels. Because EEG and ocular activity mix bidirectionally, regressing out eye artifacts inevitably involves subtracting relevant EEG signals from each record as well. Regression methods become even more problematic when a good regressing channel is not available for each artifact source, as in the case of muscle artifacts. Use of principal component analysis (PCA) has been proposed to remove eye artifacts from multichannel EEG. However, PCA cannot completely separate eye artifacts from brain signals, especially when they have comparable amplitudes. Here, we propose a new and generally applicable method for removing a wide variety of artifacts from EEG records based on blind source separation by independent component analysis (ICA). Our results on EEG data collected from normal and autistic subjects show that ICA can effectively detect, separate, and remove contamination from a wide variety of artifactual sources in EEG records with results comparing favorably with those obtained using regression and PCA methods. ICA can also be used to analyze blink-related brain activity.     

Current neurophysiological tests and revised JSCN technical standards for clinical EEG

The purpose of this lecture is to review the development of current neurophysiology and the revised standard of society for clinical EEG. 1. The improvement of neurophysiological tests. 1) EEG and evoked potential: EEG and evoked potential testing includes the routine EEG recording, EEG monitoring in surgical operation, all night sleep polygraph for the diagnosis of sleep apnea syndrome and many kinds of brain evoked potentials. Especially, the P300 component in the ERP(event-related evoked potential) is useful for the testing of essential brain functions. 2) EMG and evoked EMG: These tests are applied for the diagnosis of neurogenic, myogenic and neuromuscular junction disorder, and also the single fiber EMG using micro needle electrode is useful for the diagnosis of myasthenia gravis. Motor and sensory nerve conduction velocity are calculated from the latency of evoked EMGs. Furthermore, the distribution of these conduction velocities in many nerve fibers is measured by the collision technique. 3) Other tests: Near-infrared spectroscopy for the testing of brain functions has made rapid progress, and the transcranial magnetic stimulation method has come to be used for evaluation of functional diseases in the pyramidal tract, cerebellum and the spinal cord. 2. The revised JSCN technical standards for clinical EEG. The revised recording conditions of ECI(electro cerebral inactivity: flat EEG) in brain death are the focus of this lecture.     

On the process for evaluating Proposed new diagnostic EEG tests

Summary Assessment of new technology is an important part of the evolving art of medicine. For a variety of reasons, it is appropriate to restrict clinical applications of new technology to those tests which have been shown to be safe and effective. Efficacy of new EEG technology can be assessed through a variety of standard procedures, generally based on controlled, well organized clinical studies. Scientific reports using new EEG technology all too often fail to meet the standards traditionally expected for such clinical trials. Many such studies were never designed to be clinical trials. Misunderstandings occur when reports of scientific studies and informal clinical series become confused with formal clinical trials of efficacy. Based upon examples given during the 1991 ISBET meeting symposium on discriminant analysis, examples are discussed regarding how well individual kinds of presentations can be used to help clarify the generic clinical efficacy of the presented diagnostic tests.     

Comparison of sleep staging by polygraph and color density spectral array

Color Density Spectral Array (CDSA) is a new technique that uses the fast Fourier transform and color graphics to provide a display of frequency, power, and time. CDSA sleep records provide an overview of sleep architecture as well as quantitative+ EEG data. To validate this technique, overnight sleep records from five patients were independently staged from polygraph recordings and overnight CDSA records. Observed agreement between the two techniques was 85-92% for approximately 1,100 epochs per night.     

Piezoelectric film transducer for recording of oculography in electro-encephalogram-polygraphy

Oculography is important during clinical electroencephalography (EEG). Routinely, silver-silver plate/cup electrodes have been used. However, the electrical activity of the anterior parts of the brain can be mixed with the effect of eye/eyelid movements. This can result in artifacts disturbing or making it impossible to differentiate the frontal activity of the brain from eye movement artifacts. Therefore, crystal piezoelectric materials have been used for oculography, but they are relatively fragile in practice. In this study we present a new type of piezoelectric transducer for the recording of oculography, a piezoelectric polyvinylidenefluoride (PVDF) film transducer. Our preliminary material consists of routine EEG recordings of 15 subjects performed by means of this method. All recordings were of good quality and corresponded well with the routine electro-oculography recordings.     

SADE3: an effective system for automated detection of epileptiform events in long-term EEG based on context information

Interictal spike detection is a time-consuming, low-efficiency task, but is important to epilepsy diagnosis. Automated systems reported to date usually have their practical efficacy compromised by elevated rates of false-positive detections per minute, which are caused mainly by the influence of artifacts (such as noise activity and ocular movements) and by the adoption of single or simple approaches. This work describes the development of a hybrid system for automatic detection of spikes in long-term electroencephalogram (EEG), named System for Automatic Detection of Epileptiform Events in EEG (SADE(3)), which uses wavelet transform, neural networks and artificial intelligence procedures to recognize epileptic and to reject non-epileptic activity. The system's pre-processing stage filters the EEG epochs with the Coiflet wavelet function, which showed the closest correlation to epileptogenic (EPG) activity, in opposition to some other wavelet functions that did not correlate with these events. In contrast to current attempts using continuous wavelet transform, we chose to work with fast wavelet transform to reduce processing time and data volume. Detail components at appropriate decomposition levels were used to accentuate spikes, sharp waves, high-frequency noise activity and ocular artifacts. These four detailed components were used to train four specialized neural networks, designed to detect and classify the EPG and non-EPG events. An expert module analyzes the networks' outputs, together with multichannel and context information and concludes the detection. The system was evaluated with 126,000 EEG epochs, obtained from seven different patients during long-term monitoring, under diverse behavior and mental states. More than 6,721 spikes and sharp waves were previously identified by three experienced human electroencephalographers. In these tests, the SADE(3) system simultaneously achieved 70.9% sensitivity, 99.9% specificity and a rate of 0.13 false-positives per minute, indicating its usefulness and low vulnerability to artifact influence. After tests, the SADE(3) system showed itself to be able to process bipolar cortical EEG records, from long-term monitoring, up to 32 channels, without any data preparation or event positioning. At the same time, SADE(3) revealed a high capacity to reject non-epileptic paroxysms, robustness in relation to a variety of spike morphologies, flexibility in adjustment of performance rates and the capacity to actually save time during EEG reading. Furthermore, it can be adapted to other applications for pattern recognition, with simple adjustments.     

Reflections on Clinical Significance: What Do Our Best Treatments Accomplish and How Can We Best Find Out?

Recent developments in clinical psychology have involved important advances in the treatments available for childhood disorders. Sheldrick et al. evaluated selected treatments for childhood behavior problems, skillfully demonstrating the application of two statistical procedures for estimating clinical significance. Statistics to estimate clinical effects using traditional dependent measures is an certainly important step toward estimating the gains that such treatments can accomplish, and such estimation of clinical effects should arguably become a routine part of the treatment outcome literature. However, the consideration of a variety of additional indices as a complement to the statistical estimation of clinical significance may ultimately be necessary to determine the true utility of treatments. Many such indices were originally outlined by the APA Task Force on Psychological Intervention Guidelines and are reviewed here in the context of evaluating best treatments.     

QRS artifact elimination on full night sleep EEG

Spectral analysis is now a standard procedure for analyzing the electroencephalograms (EEG) obtained by polysomnographic recordings. These numerical methods assume an artifact-free EEG since artifacts create spurious spectral components. Our aim was the development of a QRS artifact removal technique that might be applied to full night EEG with a minimal human intervention. This technique should handle one EEG channel, with or without use of one ECG channel. Variance minimization, independent component analysis (ICA), morphological filters (MF) have been implemented. Careful attention has been given to define the MF structuring element. The tests on artifact-simulated and real data were checked on the residual ECG spectral components present in the cleaned EEG. The best results are obtained by the MF when the structuring element is an artifact template defined either directly on the EEG or on the ICA ECG component. Further developments are required to identify and subtract the T-wave artifacts.     

Validation of ICA as a tool to remove eye movement artifacts from EEG/ERP

Eye movement artifacts in electroencephalogram (EEG) recordings can greatly distort grand mean event‐related potential (ERP) waveforms. Different techniques have been suggested to remove these artifacts prior to ERP analysis. Independent component analysis (ICA) is suggested as an alternative method to “filter” eye movement artifacts out of the EEG, preserving the brain activity of interest and preserving all trials. However, the identification of artifact components is not always straightforward. Here, we compared eye movement artifact removal by ICA compiled on 10 s of EEG, on eye movement epochs, or on the complete EEG recording to the removal of eye movement artifacts by rejecting trials or by the Gratton and Coles method. ICA performed as well as the Gratton and Coles method. By selecting only eye movement epochs for ICA compilation, we were able to facilitate the identification of components representing eye movement artifacts.     

Detection of artifacts from high energy bursts in neonatal EEG

Detection of non-cerebral activities or artifacts, intermixed within the background EEG, is essential to discard them from subsequent pattern analysis. The problem is much harder in neonatal EEG, where the background EEG contains spikes, waves, and rapid fluctuations in amplitude and frequency. Existing artifact detection methods are mostly limited to detect only a subset of artifacts such as ocular, muscle or power line artifacts. Few methods integrate different modules, each for detection of one specific category of artifact. Furthermore, most of the reference approaches are implemented and tested on adult EEG recordings. Direct application of those methods on neonatal EEG causes performance deterioration, due to greater pattern variation and inherent complexity. A method for detection of a wide range of artifact categories in neonatal EEG is thus required. At the same time, the method should be specific enough to preserve the background EEG information. The current study describes a feature based classification approach to detect both repetitive (generated from ECG, EMG, pulse, respiration, etc.) and transient (generated from eye blinking, eye movement, patient movement, etc.) artifacts. It focuses on artifact detection within high energy burst patterns, instead of detecting artifacts within the complete background EEG with wide pattern variation. The objective is to find true burst patterns, which can later be used to identify the Burst-Suppression (BS) pattern, which is commonly observed during newborn seizure. Such selective artifact detection is proven to be more sensitive to artifacts and specific to bursts, compared to the existing artifact detection approaches applied on the complete background EEG. Several time domain, frequency domain, statistical features, and features generated by wavelet decomposition are analyzed to model the proposed bi-classification between burst and artifact segments. A feature selection method is also applied to select the feature subset producing highest classification accuracy. The suggested feature based classification method is executed using our recorded neonatal EEG dataset, consisting of burst and artifact segments. We obtain 78% sensitivity and 72% specificity as the accuracy measures. The accuracy obtained using the proposed method is found to be about 20% higher than that of the reference approaches. Joint use of the proposed method with our previous work on burst detection outperforms reference methods on simultaneous burst and artifact detection. As the proposed method supports detection of a wide range of artifact patterns, it can be improved to incorporate the detection of artifacts within other seizure patterns and background EEG information as well.     

脑电地形图操作规程及体会

本文介绍了脑电地形图(BEAM)的五项操作规程,同时详细介绍了每项操作规程的原理、方法、注意事项、经验体会及BEAM采样、分析诊断中常用的参考值。提出了操作BEAM的工作人员必须具备临床诊疗知识,具备计算机基本理论和基本技能,熟练掌握脑电图的基本理论和技能,否则会人为造成误差,影响BEAM的准确性。     

Artefakt oder realer Befund?

Artifacts in the EEG may show similar curve shapes as real phenomena. The differentiation of artifacts and real EEG changes is often explained by a logical field distribution or by knowledge of engineering fundamentals. Box distribution, recognition of artifacts and artifact fixes are explained     

Removal of ocular artifacts from electro-encephalogram by adaptive filtering

The electro-encephalogram (EEG) is useful for clinical diagnosts and in biomedical research. EEG signals, however, especially those recorded from frontal channels, often contain strong electro-oculogram (EOG) artifacts produced by eye movements. Existing regression-based methods for removing EOG artifacts require various procedures for preprocessing and calibration that are inconvenient and timeconsuming. The paper describes a method for removing ocular artifacts based on adaptive filtering. The method uses separately recorded vertical EOG and horizontal EOG signals as two reference inputs. Each reference input is first processed by a finite impulse response filter of length M (M=3 in this application) and then subtracted from the original EEG. The method is implemented by a recursive leastsquares algorithm that includes a forgetting factor (λ=0.9999 in this application) to track the non-stationary portion of the EOG signals. Results from experimental data demonstrate that the method is easy to implement and stable, converges fast and is suitable for on-line removal of EOG artifacts. The first three coefficients (up to M=3) were significantly larger than any remaining coefficients.     

Alcohol and the physiological detection of deception: Arousal and memory influences

Eighty male volunteers participated in an analogue study of the effects of alcohol intoxication at the time of a crime on the physiological detection of deception using control question and guilty knowledge techniques. Sixty-four of the subjects committed a mock crime and half of these were intoxicated during the crime. Sixteen subjects committed no crime and served as innocent controls. We found that intoxication at the time of the crime had no significant effect on polygraph test outcomes, although it did affect anticipatory arousal before the crime and subsequent memory for crime details. Manipulations designed to influence memory for crime details and arousal during the crime had differential effects for the two polygraph tests. On the guilty knowledge test, primed subjects who rehearsed specific details following the crime were more detectable than unprimed subjects. On the control question test, primed subjects were also more detectable, but only when arousal during the crime was high.     

Statistically comparing EEG/MEG waveforms through successive significant univariate tests: How bad can it be?

When making statistical comparisons, the temporal dimension of the EEG signal introduces problems. Guthrie and Buchwald (1991) proposed a formally correct statistical approach that deals with these problems: comparing waveforms by counting the number of successive significant univariate tests and then contrasting this number to a well‐chosen critical value. However, in the literature, this method is often used inappropriately. Using real EEG data and Monte Carlo simulations, we examined the problems associated with the incorrect use of this approach under circumstances often encountered in the literature. Our results show inflated false‐positive or false‐negative rates depending on parameters of the data, including filtering. Our findings suggest that most applications of this method result in an inappropriate familywise error rate control. Solutions and alternative methods are discussed.     

Muscle Artifact Removal from Human Sleep EEG by Using Independent Component Analysis

Muscle artifacts are typically associated with sleep arousals and awakenings in normal and pathological sleep, contaminating EEG recordings and distorting quantitative EEG results. Most EEG correction techniques focus on ocular artifacts but little research has been done on removing muscle activity from sleep EEG recordings. The present study was aimed at assessing the performance of four independent component analysis (ICA) algorithms (AMUSE, SOBI, Infomax, and JADE) to separate myogenic activity from EEG during sleep, in order to determine the optimal method. AMUSE, Infomax, and SOBI performed significantly better than JADE at eliminating muscle artifacts over temporal regions, but AMUSE was independent of the signal-to-noise ratio over non-temporal regions and markedly faster than the remaining algorithms. AMUSE was further successful at separating muscle artifacts from spontaneous EEG arousals when applied on a real case during different sleep stages. The low computational cost of AMUSE, and its excellent performance with EEG arousals from different sleep stages supports this ICA algorithm as a valid choice to minimize the influence of muscle artifacts on human sleep EEG recordings.     

Review of advanced techniques for the estimation of brain connectivity measured with EEG/MEG

Brain connectivity can be modeled and quantified with a large number of techniques. The main objective of this paper is to present the most modern and widely established mathematical methods for calculating connectivity that is commonly applied to functional high resolution multichannel neurophysiological signals, including electroencephalographic (EEG) and magnetoencephalographic (MEG) signals. A historical timeline of each technique is outlined along with some illustrative applications. The most crucial underlying assumptions of the presented methodologies are discussed in order to help the reader understand where each technique fits into the bigger picture of measuring brain connectivity. In this endeavor, linear, nonlinear, causality-assessing and information-based techniques are summarized in the framework of measuring functional and effective connectivity. Model based vs. data-driven techniques and bivariate vs. multivariate methods are also discussed. Finally, certain important caveats (i.e. stationarity assumption) pertaining to the applicability of the methods are also illustrated along with some examples of clinical applications.