TY - JOUR N2 - 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. L1 - http://www.journals.pan.pl/Content/90409/PDF/10.1515-mms-2016-0021%20paper08.pdf L2 - http://www.journals.pan.pl/Content/90409 PY - 2016 IS - No 2 EP - 260 DO - 10.1515/mms-2016-0021 KW - Adaptive Directional Time-Frequency Distribution KW - EEG signals KW - Time-Frequency features KW - pattern recognition A1 - Khan, Nabeel A. A1 - Ali, Sadiq PB - Polish Academy of Sciences Committee on Metrology and Scientific Instrumentation VL - vol. 23 DA - 2016.06.30 T1 - Classification of EEG Signals Using Adaptive Time-Frequency Distributions SP - 251 UR - http://www.journals.pan.pl/dlibra/publication/edition/90409 T2 - Metrology and Measurement Systems ER -