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The capability of predicting relapse in chronic alcoholism using quantitative EEG was investigated. For this purpose, 78 in-patients with alcoholism underwent EEG recordings (eyes closed) 7 days after the beginning of detoxification. Additionally, other clinical evaluations were carried out. After discharge from hospital, patients were regularly(More)
This paper presents the results of the practical use of artificial neural networks in the field of EEG analysis. It describes the general methodology of application as well as a case study of a discrimination of depressive and psychotic patients using 16-channel long-term EEG data prepared by classical pre-processing (spectral decomposition). This study(More)
The topographic distributions of absolute delta and theta powers were used to classify demented patients and normals by means of z statistics, discriminant analysis and artificial neural networks (NN). The data were taken from two psychopharmacological studies in mildly to moderately demented patients (111 and 96 patients for studies I and II, respectively)(More)
This paper discusses the general usability of artificial neural networks for the analysis of EEG data. The advantages and drawbacks of this new technology are compared, especially from the perspective of medical computer science. Furthermore this text clarifies the fundamental principles of neural networks, the relations to the biological model and how they(More)
Artificial neural networks (ANN) are widely used to solve problems of differentiating between groups. However, serious comparisons of this method with the traditional procedure for such tasks (discriminant analysis) are rare. Discussing the results of both methods with the example of highly topical data, we try to demonstrate advantages and drawbacks of(More)
This paper describes the application of artificial neural networks for the analysis of data of the evoked potential type. A discussion of different preprocessing schemes stresses the importance of a suitable method which supports the artificial neural networks in their classification task. This preprocessing differs completely from well-known data reduction(More)
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