Biomedical subjects

P A Karjalainen

Publications and source records attributed to P A Karjalainen.

11 recordsLinked to original sources

PubMed2001

Analysis of galvanic skin responses with principal components and clustering techniques.

An advanced method for analyzing the patterning of successive galvanic skin responses (GSR) is presented. The proposed method is based on principal component analysis in which the vector containing the measured signal is presented as a weighted sum of orthogonal basis vectors. The method is tested using measurements from 20 healthy controls and 13 psychotic patients. For each subject, 11 surprising auditory stimuli were delivered to right ear at irregular intervals and evoked GSRs were recorded from the hand. For most of the healthy controls, there was a clear pattern in successive GSRs, whereas within psychotic patients the lack of time-locking of GSRs seemed to be characteristical. These between group differences can be revealed by the proposed method. With application to clustering a significant discrimination, with overall correct ratings of 82%, of healthy controls and psychotic patients is achieved. A significant fact is that all patients were ranked correctly giving the proposed method a sensitivity of 100%.

Case-Control Studies↗

PubMed2001

A new computational approach for cortical imaging.

Estimation of current or potential distribution on the cortex is used to obtain information about neural sources from the scalp recorded electroencephalogram. If the active sources in the brain are superficial, the estimated field distribution on the cortex also yields information about the active source configuration. In these cases, these methods can be used as source localization methods. In this study, we concentrate on finite-element-based cortex potential estimation. Usually these methods require surface interpolation of the recorded voltages at the electrodes onto the entire scalp surface. We propose a new computational approach which does not require the use of surface interpolation but does it implicitly and uses only the recorded data at the electrodes. We refer to this method as the systematic approach (SA). We compare the SA with the surface interpolation approach (IA) and show that the SA is able to produce somewhat better accuracy than the IA. However, the main asset is that the sensitivity of the cortical potential maps to the regularization parameter is significantly lower than with the IA.

Brain Mapping↗

PubMed2000

Effects of electrode properties on EEG measurements and a related inverse problem.

A trend in EEG measurements is to increase the number of measurement electrodes in order to improve the spatial resolution of the recorded voltage distribution at the scalp. It is assumed that this would implicate better accuracy in the EEG inverse estimates. However, this does not necessarily hold. The reason for this is that the electrodes create a well conducting shunting "layer" on the scalp which affects the voltage distribution. This may decrease the information obtained and may therefore worsen the inverse estimates. Electrodes in EEG inverse problems are commonly modeled as point electrodes. This model cannot take into account the possible shunting effect of the electrodes. In this study the measurement electrodes are modeled using the so-called complete electrode model which takes into account the actual size of the electrode, the contact impedance between the skin and the electrode and also the shunting effect of the electrodes. In this paper the effects of the electrode size and the contact impedance on the voltage distribution are studied by simulations. It is shown that, depending on the size and the contact impedance of the electrodes, increasing the number of electrodes does not necessarily improve the accuracy of the inverse estimates. We also conclude that the use of the point electrode model is quite adequate in normal EEG studies. The use of a complete electrode model is necessary if electrodes cover more than 50% of the surface area.

Electric Impedance↗

PubMed1999

Estimation of the dynamics of event-related desynchronisation changes in electroencephalograms.

A method for the estimation of medium rate transitions of non-stationary electroencephalograms (EEG) is proposed. The method is applicable to such EEG dynamics that are between (a) fast transitions for which segmentation procedures are used and (b) slow transitions for which adaptive filters work properly. The estimation of the transition dynamics is based on a novel time-varying autoregressive model. This model belongs to the class of deterministic regression time-varying autoregressive models and its parametrisation allows only simultaneous transitions in all coefficient evolutions. Data from 22 patients was analysed. The performance of the method is first evaluated with realistic simulations of known transition dynamics and it is shown to be able to track medium-rate transitions. The method is then applied to the estimation of the dynamics of event related desynchronisation. It is shown that the proposed method is able to estimate the transitions which are less apparent, such as from a multi-infarct patient.

Algorithms↗

PubMed1999

Effects of local skull inhomogeneities on EEG source estimation.

The accuracy of the head model affects the solutions of the EEG inverse problems. If a simple three-sphere model and standard conductivity values for brain, skull and scalp regions are used, significant errors may occur in the dipole localisation. One of the most sensitive head model parameters is the conductivity of the skull. A realistic three-dimensional finite-element model provides a method to study the effect of inhomogeneities of the skull on the solutions of EEG inverse problems. In this paper the effect of a local skull conductivity inhomogeneity on source estimation accuracy is analyzed by computer simulations for different numbers of electrodes. It is shown that if the inhomogeneity of the skull conductivity is not taken into account, localisation errors of approximately 1 cm can be encountered in the equivalent current dipole estimation. This modelling error introduces a bias to the solution which cannot be compensated by increasing the number of electrodes.

Biophysical Phenomena↗

PubMed1999

Subspace regularization method for the single-trial estimation of evoked potentials.

A method for the single-trial estimation of the evoked potentials is proposed. The method is based on the so-called subspace regularization approach in which the second-order statistics of the set of the measurements is used to form a prior information model for the evoked potentials. The method is closely related to the Bayesian estimation. The performance of the proposed method is evaluated using realistic simulations. As a specific application the method is applied to the estimation of the target responses in the P300 test.

Bayes Theorem↗

PubMed1998

A Kalman filter approach to track fast impedance changes in electrical impedance tomography.

In electrical impedance tomography (EIT), an estimate for the cross-sectional impedance distribution is obtained from the body by using current and voltage measurements made from the boundary. All well-known reconstruction algorithms use a full set of independent current patterns for each reconstruction. In some applications, the impedance changes may be so fast that information on the time evolution of the impedance distribution is either lost or severely blurred. In this paper, we propose an algorithm for EIT reconstruction that is able to track fast changes in the impedance distribution. The method is based on the formulation of EIT as a state-estimation problem and the recursive estimation of the state with the aid of the Kalman filter. The performance of the proposed method is evaluated with a simulation of human thorax in a situation in which the impedances of the ventricles change rapidly. We show that with optimal current patterns and proper parameterization, the proposed approach yields significant enhancement of the temporal resolution over the conventional reconstruction strategy.

Algorithms↗

PubMed1998

Tikhonov regularization and prior information in electrical impedance tomography.

The solution of impedance distribution in electrical impedance tomography is a nonlinear inverse problem that requires the use of a regularization method. The generalized Tikhonov regularization methods have been popular in the solution of many inverse problems. The regularization matrices that are usually used with the Tikhonov method are more or less ad hoc and the implicit prior assumptions are, thus, in many cases inappropriate. In this paper, we propose an approach to the construction of the regularization matrix that conforms to the prior assumptions on the impedance distribution. The approach is based on the construction of an approximating subspace for the expected impedance distributions. It is shown by simulations that the reconstructions obtained with the proposed method are better than with two other schemes of the same type when the prior is compatible with the true object. On the other hand, when the prior is incompatible with the true object, the method will still give reasonable estimates.

Algorithms↗

PubMed1997

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.

Algorithms↗

PubMed1997

Assessment of errors in static electrical impedance tomography with adjacent and trigonometric current patterns.

In electrical impedance tomography (EIT), difference imaging is often preferred over static imaging. This is because of the many unknowns in the forward modelling which make it difficult to obtain reliable absolute resistivity estimates. However, static imaging and absolute resistivity values are needed in some potential applications of EIT. In this paper we demonstrate by simulation the effects of different error components that are included in the reconstruction of static EIT images. All simulations are carried out in two dimensions with the so-called complete electrode model. Errors that are considered are the modelling error in the boundary shape of an object, errors in the electrode sizes and localizations and errors in the contact impedances under the electrodes. Results using both adjacent and trigonometric current patterns are given.

Computer Simulation↗

PubMed1997

Estimation of event-related synchronization changes by a new TVAR method.

The modeling of nonstationary electroencephalogram (EEG) with time-varying autoregressive (TVAR) models is discussed. The classical least squares TVAR approach is modified so that prior assumptions about the signal can be taken into account in an optimal way. The method is then applied to the estimation of event-related synchronization changes in the EEG. The results show that the new approach enables effective estimation of the parameter evolution of the time-varying EEG with better time resolution compared to previous methods. The new method also allows single-trial analysis of the event-related synchronization.

Algorithms↗