Multichannel EEG Data Research Articles

Non-linear dynamic analysis of clozapine-induced electroencephalographic changes in schizophrenic patients - a preliminary study

1. In order to find the electroencephalographic (EEG) parameters that reflect the effect of clozapine in schizophrenic patients, the authors applied various non-linear analyses on multi-channel EEG data drawn from patients before and after a therapeutic trial of clozapine. 2. The correlation dimension was difficult to extract from our limited time series EEG data and the authors did not find a meaningful assocation with clozapine use. The primary Lyapunov exponent could be reliably calculated but also did not reflect the effect of clozapine. 3. However, the mutual cross-prediction (MCP) algorithm showed potentially meaningful results. The driving system was shifted to the frontal channels after a 4-week trial with clozapine. Moreover, MCP might have a value as a predictor of treatment response. 4. Although preliminary in nature, the MCP might have greater power for interpreting complex changes from channel to channel in EEG induced by clozapine.

Epileptic event forewarning from scalp EEG.

The authors present a model-independent approach to quantify changes in the dynamics underlying nonlinear time-serial data. From time-windowed datasets, the authors construct discrete distribution functions on the phase space. Condition change between base case and test case distribution functions is assessed by dissimilarity measures via L1 distance and chi2 statistic. The discriminating power of these measures is first tested on noiseless data from the Lorenz and Bondarenko models, and is then applied to detecting dynamic change in multichannel clinical scalp EEG data. The authors compare the dissimilarity measures with the traditional nonlinear measures used in the analysis of chaotic systems. They also assess the potential usefulness of the new measures for robust, accurate, and timely forewarning of epileptic events.

A natural basis for efficient brain-actuated control.

The prospect of noninvasive brain-actuated control of computerized screen displays or locomotive devices is of interest to many and of crucial importance to a few 'locked-in' subjects who experience near total motor paralysis while retaining sensory and mental faculties. Currently several groups are attempting to achieve brain-actuated control of screen displays using operant conditioning of particular features of the spontaneous scalp electroencephalogram (EEG) including central mu-rhythms (9-12 Hz). A new EEG decomposition technique, independent component analysis (ICA), appears to be a foundation for new research in the design of systems for detection and operant control of endogenous EEG rhythms to achieve flexible EEG-based communication. ICA separates multichannel EEG data into spatially static and temporally independent components including separate components accounting for posterior alpha rhythms and central mu activities. We demonstrate using data from a visual selective attention task that ICA-derived mu-components can show much stronger spectral reactivity to motor events than activity measures for single scalp channels. ICA decompositions of spontaneous EEG would thus appear to form a natural basis for operant conditioning to achieve efficient and multidimensional brain-actuated control in motor-limited and locked-in subjects.

Stationarity and redundancy of multichannel EEG data recorded during generalized tonic-clonic seizures.

To improve our understanding of the physiology of generalized tonic-clonic (GTC) seizures, we have investigated the stationarity and redundancy of 21-electrode EEG data recorded from ten patients during GTC seizures elicited by electroconvulsive therapy (ECT). Stationarity was examined by calculating probability density functions (pdfs) and power spectra over small equal-length non-overlapping time windows and then by studying, visually and quantitatively, the evolution of these quantities over the duration of the seizures. Our analysis shows that some seizures had no demonstrable stationarity, that most seizures had time intervals of at least a few seconds that were statistically stationary by several criteria, and that, in some seizures, there were leads which were delayed in manifesting the statistical changes associated with seizure onset evident in other leads. The redundancy analysis demonstrated for the first time posterior-to-anterior time delays in the mid-ictal region of GTC seizures. The implications of these findings are discussed for the analysis of GTC seizure EEG data, for the physiology of GTC seizures, and for ECT research.

Designing optimal spatial filters for single-trial EEG classification in a movement task

We devised spatial filters for multi-channel EEG that lead to signals which discriminate optimally between two conditions. We demonstrate the effectiveness of this method by classifying single-trial EEGs, recorded during preparation for movements of the left or right index finger or the right foot. The classification rates for 3 subjects were 94, 90 and 84%, respectively. The filters are estimated from a set of multi-channel EEG data by the method of Common Spatial Patterns, and reflect the selective activation of cortical areas. By construction, we obtain an automatic weighting of electrodes according to their importance for the classification task. Computationally, this method is parallel by nature, and demands only the evaluation of scalar products. Therefore, it is well suited for on-line data processing. The recognition rates obtained with this relatively simple method are as good as, or higher than those obtained previously with other methods. The high recognition rates and the method's procedural and computational simplicity make it a particularly promising method for an EEG-based brain–computer interface.

An automated system for epileptogenic focus localization in the electroencephalogram.

This paper describes an automated system for the detection and localization of foci of epileptiform activity in the EEG. The system detects sharp EEG transients (STs) in the process, but the emphasis is on epileptic focus localization. A combination of techniques involving signal processing, pattern recognition, and the expert rules of an experienced electroencephalographer, involving considerable spatiotemporal context information, is applied to multichannel EEG data. An overall emphasis on minimizing the number of false-positive sharp transient detections drives the system design. Tested on data from 13 subjects with epileptiform activity and 5 controls, all areas of focal epileptiform activity were detected by the system, although not all of the contributing foci were reported separately. Two false-positive foci were detected as well due to nonfocal spike activity and normal spike-like activity not present in the training set. The system detected 95.7% of the epileptiform events constituting the correctly detected foci, with a false detection rate of 11.1%.

Automated feature selection with a distinction sensitive learning vector quantizer

An extended version of Kohonen's Learning Vector Quantization (LVQ) algorithm, called Distinction Sensitive Learning Vector Quantization (DSLVQ), is introduced which overcomes a major problem of LVQ, the dependency on proper pre-processing methods for scaling and feature selection. The algorithm employs a weighted distance function and adapts the metric with learning. Highest weights are assigned to components in the input vectors which are most informative for classification; non-informative components are discarded. The algorithm is applied to the analyses of multi-channel EEG data and compared with experienced methods.

Re-examination of the evidence for low-dimensional, nonlinear structure in the human electroencephalogram

We have re-examined single channel EEG data obtained from normal human subjects. In the original analysis, calculation of the correlation dimension with the Grassberger-Procaccia algorithm produced results consistent with an interpretation of low-dimensional behavior. The re-examination suggests that the previous indication of low-dimensional structure was an artifact of autocorrelation in the oversampled signal. Calculations with a variant of the Grassberger-Procaccia algorithm modified to be less sensitive to autocorrelations, and comparison with linear gaussian surrogate data, indicate that these data may be more appropriately modeled by linearly filtered noise. Discriminant analysis further indicates that the correlation dimension is a poor discriminator for distinguishing between EEGs recorded at rest and during periods of cognitive activity. It remains possible that the application of other nonlinear measures or the examination of multichannel EEG data may resolve structures not found in these calculations.

Data compression by linear prediction for storage and transmission of EEG signals

The EEG time series has been subjected to various formalisms of analysis to extract meaningful information regarding the underlying neural events. In this paper the linear prediction (LP) method has been used for analysis and presentation of spectral array data for the better visualisation of background EEG activity. It has also been used for signal generation, efficient data storage and transmission of EEG. The LP method is compared with the standard Fourier method of compressed spectral array (CSA) of the multichannel EEG data. The autocorrelation autoregressive (AR) technique is used for obtaining the LP coefficients with a model order of 15. While the Fourier method reduces the data only by half, the LP method just requires the storage of signal variance and LP coefficients. The signal generated using white Gaussian noise as the input to the LP filter has a high correlation coefficient of 0.97 with that of original signal, thus making LP as a useful tool for storage and transmission of EEG. The biological significance of Fourier method and the LP method in respect to the microstructure of neuronal events in the generation of EEG is discussed.

Neural network based classification of single-trial EEG data

Standard Back Propagation (BP), Partially Recurrent (PR) and Cascade-Correlation (CC) neural networks were used to predict the side of finger movement on the basis of non-averaged single trial multi-channel EEG data recorded prior to movement. From these EEG data, power values were calculated and used as parameters for classification. The results obtained on three subjects show that the Cascade-Correlation neural network is an appropriate choice for neural network based classification of spatio-temporal single-trial EEG pattems. It is fast, stable and able to discover and recognize underlying dynamics of rhythmic activities within the alpha band which precede execution of hand movements.

Somatosensory evoked potentials and magnetic fields: separation of multiple source activities

Median nerve somatosensory evoked potentials (SEP) and magnetic fields (SEF) were recorded in two subjects with multichannel devices. Single-moving- and multiple-stationary-dipole models were compared with the brain electric source analysis program of Scherg. Subcortical sources, reflecting the afferent neural volley when entering the brainstem and leaving the thalamus, were found only in the SEP. The analysis of SEF and SEP revealed a minimum of four overlapping source activities in the region of the contralateral post- and precentral cortical projection areas. Two sources in the depth of the central sulcus could not be resolved unambiguously. The third, more superficial source, which probably reflects activation of area 1, was better defined in the source analysis of the SEP, because dipole orientation was close to radial. The fourth source was more posterior. Several problems observed in the analysis of the present MEG and EEG data suggest that the simultaneous measurement and analysis of multichannel EEG and MEG data will substantially increase spatio-temporal resolution.

Neuritor miniaturized EEG system — reliability evaluation : M.Y. Neufeld ∗, B. El-Ad ∗, A.D. Korcyn ∗, A. Israeli ∗∗, A. Chen-Menaker ∗∗. ∗ Sourasky Medical Center, Tel Aviv, Israel; ∗∗ Medis El, Israel

We devised spatial filters for multi-channel EEG that lead to signals which discriminate optimally between two conditions. We demonstrate the effectiveness of this method by classifying single-trial EEGs, recorded during preparation for movements of the left or right index finger or the right foot. The classification rates for 3 subjects were 94, 90 and 84%, respectively. The filters are estimated from a set of multi-channel EEG data by the method of Common Spatial Patterns, and reflect the selective activation of cortical areas. By construction, we obtain an automatic weighting of electrodes according to their importance for the classification task. Computationally, this method is parallel by nature, and demands only the evaluation of scalar products. Therefore, it is well suited for on-line data processing. The recognition rates obtained with this relatively simple method are as good as, or higher than those obtained previously with other methods. The high recognition rates and the method's procedural and computational simplicity make it a particularly promising method for an EEG-based brain–computer interface.

Prediction of the side of hand movements from single-trial multi-channel EEG data using neural networks

Thirty channels of EEG data were recorded prior to voluntary right or left hand movements. Event-related desynchronization (ERD) was quantified in the 8–10 Hz and 10–12 Hz bands in single-trial data and used as training input for a neural network comprised of a learning vector quantizer (LVQ). After a training period, the network was able to predict the side of hand movement from single-trial EEG data recorded prior to movement onset.

Application of singular value decomposition to topographic analysis of flash-evoked potentials.

Singular value decomposition is a robust numerical method for decomposing a matrix of multichannel EEG or EP data into a sharply reduced set of features with corresponding waveform, amplitude, and spatial vectors. In 19 normal subjects aged 19 to 40 years, the three largest features computed by the SVD algorithm accounted for 93-98 percent of the total variance of the averaged flash-evoked potential. There was good separation of major brain areas as well as clustering of related electrode sites. Orthogonal rotation of the three spatial vectors is essential to see clustering of brain areas across subjects. Three-dimensional display showed the regular presence of orthonormal occipital, frontopolar, and vertex spatial vectors. Since the spatial feature vectors cluster tightly and yet are orthonormal, statistical comparison of patients with normal control groups will be facilitated.

Common reference coherence data are confounded by power and phase effects

Coherence analysis of the EEG is used to study the coupling between cortical regions. High coherence between signals recorded at 2 electrodes is interpreted as evidence for neuroanatomic connections between the cortical areas underlying the electrodes. When common reference recordings are used, coherence measures the relationship between 2 time series, each of which is the difference between 2 signals measured at the scalp and is confounded by spectral power and phase at the recording and reference electrodes. Using multi-channel EEG data from 3 subjects, we illustrate the confounding of common reference data coherence computations and demonstrate the extreme effects of power and phase changes on coherence by simulating these changes in the EEG data. Common reference coherence data can be either inflated or deflated as a consequence of activity (i.e., spectral power) at the reference. Phase relationships among the reference and recording time series modulate the power effects on coherence. Both the power and phase effects can vary dramatically across frequencies, having profound and complicated effects on the shape of the coherence function. Based on these considerations, we conclude that common reference coherence data must be interpreted very cautiously and recommend that a new body of EEG coherence data must be gathered using reference-free recording methods before the utility of EEG coherence analysis for understanding brain function can be determined.

Automatic classification of multivariate EEGs using an amount of information measure and the eigenvalues of parametric time series model features

Two new classes of features are introduced for the automatic classification of multichannel stationary time series EEG data. The features are the Shannon-Gelfand-Yaglom measure of the amount of information between two sets of stationary Gaussian time series and the eigenvalues computed from a parametric model of the time series. The performance of these features for automatic sleep stage scoring from two EEG data channels, evaluated using the multinomial logistic function, is presented as an example. This parametric modeled EEG time series-two features for classification approach is a radical departure from the more conventional windowed periodogram spectral analysis-discriminant analysis packaged computer program approach.

Some modern aspects in numerical spectrum analysis of multichannel electroencephalographic data.

Digital auto- and cross-spectral analysis of multichannel EEG data, although promising an important insight into the nature of the EEG generating process, has been hindered in its broader application by the prohibitive amount of computer time consumed by the classical procedures. However, the recent introduction of a high speed Fourier transform method byCooley et al. (1965) has most favourably changed this situation. The present paper discusses the practical aspects of numerical spectrum analysis of EEG data using this new procedure and includes for practical purposes a proposal for an ALGOL program of the Fast Fourier Algorithm. In addition some important aspects of spectral windows are discussed and two illustrative examples of cross-spectral analyses are presented.