Wavelet framework for improved target detection in oddball paradigms using P300 and gamma band analysis

We present an application level framework which makes use of Wavelet Packet Analysis (WPA) for improved target detection in oddball paradigm, which are being researched for a brain biometric system. The novelty lies in the usage of both P300 (delta and theta band) and gamma band features from a wavelet perspective using just forty trials. The features were extracted using WPA analysis for target detection, wherein Daubechies (Db4) and Coiflet (Coif3) wavelets are used respectively to extract the P300 and Gamma band energy features. A comparison on the classification accuracy is also presented when the P300 features are used with and without Gamma band features. This work also discusses a new dynamic backward referencing technique which seems to enhance the features (delta, theta and gamma band) from eight channels. A Radial Basis Function (RBF) classifier is used to classes the obtained features as target and non-target for both the paradigms. Initial results on these lines from four subjects show motivating results for further time frequency research.