A Simple Approach to Find the Best Wavelet Basis in Classification Problems

In this paper, we address the problem of finding the best wavelet basis in wavelet packet analysis for applications based on classification. We implement and evaluate our proposed method in the design of a self-paced 2-state mental task-based brain-computer interface (BCI) as one possible type of classification-based applications. The autoregressive coefficients of the best wavelet basis are concatenated to form the feature vector. The 2-stage classification process is based on quadratic discriminant analysis and majority voting. Seventeen wavelets from 2 different families are tested. A 5×5 cross-validation process is per-formed twice to do model selection and system performance evaluation. The results show that the proposed method can be well applied to BCI systems.