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B H Jansen

Publications and source records attributed to B H Jansen.

32 records · Page 2Linked to original sources
PubMed1982

Identification and labeling of EEG graphic elements using autoregressive spectral estimates.

Syntactic EEG analysis requires descriptive labeling of short (1 s) epochs in an EEG. While discriminant analysis methods are useful for this purpose, significant improvement in label correctness can be achieved using the heuristic method described in this paper. The method is based on the estimation of frequency spectra by autoregressive (AR) modeling. The location of the peak frequencies and the power of these peaks are used to assign labels to 1 s epochs. Artefacts and epochs with exceptionally high or low amplitude and/or frequency values are identified as well. The assignment of labels is accomplished by comparing parameters, extracted from the power spectra estimated for 1 s epochs, with thresholds. These thresholds are automatically adapted to each individual EEG lead. In this paper, the method is outlined and its performance is compared with a discriminant analysis approach and visual labeling.

Computers↗
PubMed1981

Evaluation of a syntactic pattern recognition approach to quantitative electroencephalographic analysis.

This paper reports an experiment designed to evaluate the usefulness of a syntactic pattern recognition approach to quantitative EEG analysis. This approach provides a systematic way of organizing and interpreting spatially and temporally distributed activity in an EEG record. Many automated scoring systems evaluate only background activity, ignoring the significant transient events that may occur in an EEG. In contrast, syntactic methods allow evaluation of background activity in combination with transient events. In this paper, 454 EEGs recorded from a population of renal patients are evaluated using (1) the syntactic approach, and (2) a method based on discriminant analysis. The main finding is that the syntactic approach outperforms the discriminant analysis method when each technique is compared to visual scoring.

Electroencephalography↗
PubMed1981

Piece-wise EEG analysis: an objective evaluation.

In this paper, a method is described to evaluate EEGs by means of a piece-wise analysis. The procedure involves the recursive computation of a 5th-order autoregressive model by means of a Kalman filter. As an illustration the result of applying this method to sleep recordings is described in this paper. Also, an objective comparison of this method with a more conventional approach (based on analyzing 30-s intervals), and with adaptive segmentation (Praetorius et al., 1977) was carried out using the same data. The results obtained indicate that our method is useful in extracting elementary patterns from an EEG and that the piece-wise analysis approach is to be favored over more conventional techniques.

Computers↗
PubMed1978

Quantification of EEG variability.

This paper presents a method of segmenting the EEG based on the well-known power spectrum analysis. This procedure is applied to the EEG recordings of two normal subjects in order to determine the temporal EEG variability. These results are compared with a more classical approach.

Adult↗
PubMed1978

Demonstration of segmentation techniques for EEG records.

In this paper three different techniques for segmenting EEG's are presented. The principles of these techniques (the Kalman filter approach, the power spectrum analysis and the texture matrix approach) are explained and the results obtained summarised. The segmentation is used in an interactive EEG interpretation system.

Adult↗
PubMedDate not supplied

K-complex detection using multi-layer perceptrons and recurrent networks.

The feasibility of using a multi-layer perceptron and Elman's recurrent network for the detection of specific waveforms (K-complexes) in electroencephalograms (EEGs), regardless of their location in the signal segment, is explored. Experiments with simulated and actual EEG data were performed. In case of the perceptron, the input consisted of the magnitude and/or phase values obtained from 10-s signal intervals, whereas the recurrent net operated on the digitized data samples directly. It was found that both nets performed well on the simulated data, but not on the actual EEG data. The reasons for the failure of both nets are discussed.

Electroencephalography↗
PubMedDate not supplied

Pre-stimulus spectral EEG patterns and the visual evoked response.

The relationship between the latencies and amplitudes of the N1 and P2 components of the visual evoked potential (VEP) and the psychophysiological state of the brain immediately preceding the time of the stimulus has been investigated in 7 male subjects. Power spectral measures in the delta, theta, alpha and beta bands of the 1 sec pre-stimulus EEG were used to assess the brain state, and low intensity flashes, delivered randomly between 2 and 6 whole seconds, were used as the stimuli. Trials were ranked separately according to the relative amounts of pre-stimulus power in each EEG band and were partitioned into groups by an equal pre-stimulus spectral power criterion. Averaged EPs were computed from these groups and multiple regression analysis was used to relate pre-stimulus spectral power values to EP features. Five of the 7 subjects displayed consistent increases in N1-P2 amplitude as a function of increasing pre-stimulus relative alpha power. The between-subjects effect of pre-stimulus EEG on N1 latency was small, but was moderate for P2 latency (both significant). Both N1 and P2 latency were found to decrease with increasing amounts of pre-stimulus relative delta and theta power.

Adult↗
PubMedDate not supplied

The effect of the phase of prestimulus alpha activity on the averaged visual evoked response.

The relationship between the latencies and amplitudes of the N1 and P2 components of the averaged visual evoked potential (EP) and the phase of the alpha activity immediately preceding the time of the stimulus, has been investigated in 7 male subjects. Low intensity flashes, delivered randomly between 2 and 6 whole seconds, were used as the stimuli. The phase angle of the EEG at the moment of stimulation was computed for all trials containing more than 100 microV2 of prestimulus alpha power. The single trials were grouped into 8 classes on the basis of the phase angle value, and averaged EPs for each individual were computed from these groups. In addition, averaged EPs were computed in 3 ways: (1) a grand average consisting of all artifact-free trials, (2) an 'alpha average' consisting of all trials containing more than 100 microV2 of prestimulus alpha power, and (3) a 'non-alpha average' consisting of all trials with less than 100 microV2 of prestimulus alpha power. Each of these 3 averages were cross-correlated with the phase-selective averages. It was found that the particular N1 component assessed in this experiment may possibly be entrained alpha activity, and that the measured P2 component is not an alpha process, yet it is influenced by the amount of prestimulus alpha activity.

Adult↗