Epilepsy surgery is the treatment of choice in selected patients with drug-resistant focal epilepsy. The efficacy of surgery depends on the complete resection of the epileptogenic brain tissue. High frequency oscillations (HFO) have been proposed as a biomarker of epileptogenicity. In this thesis, we investigated the clinical relevance of HFO in guiding epilepsy surgery by means of electrophysiology. We used an automated HFO detector. We developed a pipeline that is centered on the fully automated and methodically robust detection of clinically relevant HFO. Finally, we tested the clinical relevance of HFO detected through our pipeline against the postoperative seizure outcome in the individual patient. We contributed to the feasibility of HFO implementation in clinical practice by showing that 1) prospectively defined HFO are reliable clinical biomarkers of epileptogenicity in invasive recordings (Publication 1); 2) intraoperative recordings with higher spatial resolution improve HFO detection (Publication 2); 3) scalp HFO are biomarkers for the severity of epilepsy and can be used to monitor therapy (Publication 3) and 4) our definition of clinically relevant HFO is not confounded by cognitive tasks (Publication 4). Our research may improve surgical planning, leading to a more favorable seizure outcome for patients with drug resistant focal epilepsy. Our findings regarding the association of HFO and epilepsy severity may lead to the implementation of HFO in assessing seizure risk, and adjusting AED therapy accordingly. Automated HFO analysis has the potential to improve epilepsy diagnosis and treatment, as well as the quality of life for epilepsy patients.