Feature selection with distinction sensitive learning vector quantisation and genetic algorithms

• Published in: IEEE International Conference on Neural Networks, 1994 Vol. 6; pp. 3448 - 3451 vol.6
• Main Authors: Flotzinger, D., Pregenzer, M., Pfurtscheller, G.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 1994
• Subjects: Biological cells; Biomedical engineering; Biomedical informatics; Brain computer interfaces; Control systems; Electrodes; Electroencephalography; Frequency; Genetic algorithms; Vector quantization
• ISBN: 078031901X; 9780780319011
• DOI: 10.1109/ICNN.1994.374888

Two feature selection methods, a distinction-sensitive learning vector quantizer (DSLVQ) and a genetic algorithm (GA) approach, are applied to multichannel electroencephalogram (EEG) patterns. It is shown how DSLVQ adjusts the influence of different input features according to their relevance for classification. Using a weighted distance function DSLVQ thereby performs feature selection along with classification. The results are compared with those of a GA which minimizes the number of features taken for classification while maximizing classification performance. The multichannel EEG patterns used in this paper stem from a study for the construction of a brain-computer interface, which is a system designed for handicapped persons to help them use their EEG for control of their environment. For such a system, reliable EEG classification, i.e. differentiation of several distinctive EEG patterns, is vital. In practice the number of electrodes for EEG recordings can be high (up to 56 and more) and different frequency bands and time intervals for each electrode can be used for classification simultaneously. This shows the importance of methods automatically selecting the most distinctive out of a number of available features.< >