Abstract. In this paper we empirically evaluate feature selection methods for classification of Brain-Computer Interface (BCI) data. We selected five state-of the-art methods, suitable for the noisy, correlated and highly dimensional BCI data, namely: information gain ranking, correlation-based feature selection, ReliefF, consistency-based feature selection and 1R ranking. We tested them with ten classification algorithms, representing different learning paradigms, on a benchmark BCI competition dataset. The results show that all feature selectors significantly reduced the number of features and also improved accuracy when used with suitable classification algorithms. The top three feature selectors in terms of classification accuracy were correlation-based feature selection, information gain and 1R ranking, with correlation based feature selection choosing the smallest number of features.
In REF Koprinska empirically evaluated feature selection methods for classification of Brain-Computer Interface (BCI) data.
Feature Selection for Brain-Computer Interfaces
PAKDD Workshops
Computer Science