Distinction Sensitive Learning Vector Quantization (DSLVQ) application as a classifier based feature selection method for a Brain Computer Interface
This paper describes a simple but very powerful method for feature selection. The Distinction Sensitive Learning Vector Quantizer (DSLVQ) is a learning classifier which focuses on relevant features according to its own instance based classifications. Two different experiments describe the application of DSLVQ as a feature selector for an EEG-based Brain Computer Interface (BCI) system. It is shown that optimal electrode positions as well as frequency bands are strongly dependent on each subject and that a subject specific feature selection is when important for BCI systems.