Classification of EEG Signals Using Adaptive Time-Frequency Distributions

Time-Frequency (t-f) distributions are frequently employed for analysis of new-born EEG signals because of their non-stationary characteristics. Most of the existing time-frequency distributions fail to concentrate energy for a multicomponent signal having multiple directions of energy distribution in the t-f domain. In order to analyse such signals, we propose an Adaptive Directional Time-Frequency Distribution (ADTFD). The ADTFD outperforms other adaptive kernel and fixed kernel TFDs in terms of its ability to achieve high resolution for EEG seizure signals. It is also shown that the ADTFD can be used to define new time-frequency features that can lead to better classification of EEG signals, e.g. the use of the ADTFD leads to 97.5% total accuracy, which is by 2% more than the results achieved by the other methods.