CLASSIFICATION OF ELECTRO-ENCEPHALOGRAMS WITH PATTERN RECOGNITION ALGORITHMS.
Abstract
The report proposes a mathematical model for making decisions about the condition of a subject from EEG date and algorithms for implementing the model. Pattern recognition methods are combined with the experience of a practicing electroencephalographer to balance the availability of mathematical models, computational feasibility, and experience. The aim of the model building is to produce a computationally feasible algorithm for a digital computer that generates a chart showing the condition of the subject as a function of time. The report gives preliminary results on feature extraction. In its present version, the pattern recognizer treats feature extraction and pattern classification distinctly and limits learning to transition probabilities of the Markov chain. The decision procedure that is outlined is applicable to any model that defines discrete states and permits Markovian movement between states. (Author)
Document Details
- Document Type
- Technical Report
- Publication Date
- Mar 01, 1969
- Accession Number
- AD0685734
Entities
People
- Albert Hung
- Richard C. Dubes
- W. R. Mccrum
Organizations
- Michigan State University