nterictal High Frequency Oscillations (HFOs [30-600 Hz]) have been recently used as reliable biomarkers for epileptogenic zones. Intra- and inter-subject variations represent the main source of HFOs diversity in terms of spectral and temporal characteristics. According to spectral characteristics, HFOs are usually classified into four sub-bands: gamma, high-gamma, ripples and fast ripples. The objective of this paper is to investigate the possibility of discriminating HFOs classes. Gabor Transform is used to extract relevant features from intracerebral electroencephalography signals recorded in patients candidate to epilepsy surgery. A multiclass linear discriminant analysis is applied to discriminate the HFOs categories. Results obtained from bootstrap on training/test datasets show high performances in terms of sensitivity and specificity. Results are also compared to those obtained by Daubechies Wavelets.