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Discovering patterns in EEG-Signals: Comparative study of a few methods

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (1993) 667 LNAI 366-371
DOI: 10.1007/3-540-56602-3_152
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Abstract

The objective of this paper is to draw the attention of the ML-researchers to the domain of data analysis. The issue is illustrated by an attractive case study—automatic classification of non-averaged EEG-signals. We applied several approaches and obtained best results from a combination of an ID3-like program with Bayesian learning.

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APA

Kubat, M., Flotzinger, D., & Pfurtscheller, G. (1993). Discovering patterns in EEG-Signals: Comparative study of a few methods. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 667 LNAI, pp. 366–371). Springer Verlag. https://doi.org/10.1007/3-540-56602-3_152

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